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AMER. ZOOL., 30:629-705 (1990) Rethinking Mammalian Brain Evolution1 TERRENCE W. DEACON Harvard University, Peabody Museum, Cambridge, Massachusetts 02138 SYNOPSIS. A critical review of past and current theories of mammalian brain evolution is presented in order to discuss conceptual problems that persist in the field. Problems with the concept of homology arise because of the interaction of cell lineages and axonal connectivity in the determination of structural features of the brain. Focusing on the continuity of information represented by ontogenetic mechanisms as opposed to morphological features avoids many of these problems and suggests homological relationships that otherwise have gone unnoticed. Many apparently progressive trends and parallelisms in mammalian brain evolution turn out to result from the influence of underlying developmental homologies. Confusions about evolutionary advancement, increasing architectonic differentiation, and the evolution of new brain structures result from a failure to appreciate how increasing brain size can bias developmental processes with respect to axonal competition, increased cellular metabolic demands and decreased information processing efficiency. Explanations of the evolution of novel structures and new connectional patterns are criticized for their failure to consider the constraints of neural developmental processes. The correlations between structural neogenesis, functional specialization and size changes in brain evolution are explained by a theory of competitive displacement of neural connections by others during development under the biasing influences of differential allometry, cell death or axon-target affinity changes. The "displacement hypothesis" is used to propose speculative accounts for the differential enlargement and multiplication of cortical areas, the origins of mammalian isocortex, the unusual features of dolphin cortex and the dramatic structural and functional reorganizations that characterize human brain evolution. INTRODUCTION Intrinsic difficulties Despite the fact that the evolution of the brain—particularly the human brain—is of intrinsic interest to anyone curious about the human mind and the origins of human nature, the scientific study of brain evolution is not a major subdiscipline within biology, psychology, anthropology or even the neurosciences. The apparently poor representation of this area of study in the sciences can in part be attributed to the paucity of direct paleontological evidence regarding brain evolution and the longtime inaccessibility of crucial comparative neuroanatomic details. The poor representation of information about brain evolution in disciplines outside the neurosciences is additionally limited by the considerable sophistication in comparative neuroanatomy and physiology that is 1 From the Symposium on Science as a Way of Knowing—Neurobiology and Behavior organized by Edward S. Hodgson and presented at the Centennial Meeting of the American Society of Zoologists, 27-30 December 1989, at Boston, Massachusetts. required to even begin to grapple with the questions in a meaningful way. The disturbing correlate of this is that speculative theories concerning brain evolution— especially human brain evolution—are widespread and often contain relatively little neuroanatomical or neurophysiological information. But even theories conceived by neuroanatomists and neurophysiologists often reflect numerous unsupported assumptions about the direction of evolutionary trends, the nature of natural selection affecting brain processes, the ways that brains can vary from one species to another, the relationship between structure and function within the brain and even the nature of intelligence itself. Although paleoneurology is unlikely to experience sudden advances in the years to come, many of the barriers to relevant neuroanatomical evidence have dissolved in the wake of the introduction of many new experimental techniques in recent decades. Now that many of the technical impediments standing in the way of detailed knowledge about brain structure and function have been removed, many of these hitherto unquestioned assumptions are now open to test. 629 630 TERRENCE W. DEACON Among colleagues in the neurosciences one sometimes hears the criticism that, unlike most other areas of neuroscience, the study of brain evolution is limited to theory because it is essentially beyond the reach of experimental approaches. Although brains of extinct species are not available for direct inspection and analysis, this does not necessarily mean that theories of brain evolution are empirically untestable. Indeed they are every bit as susceptible to experimental investigation and testing as are other theories of brain organization and function. The approach must necessarily be indirect, but it need be no less effectual nor any less scientific or experimental. We should remember that the vast majority of scientific data in any field is indirect, irrespective of whether the object under study is directly observable. From tracks left by subatomic particles in nuclear accelerators to the measurements of minute amounts of unseen biochemicals registered in scintillation counters, nearly all of the "hard data" generated in the laboratories of any field of the natural sciences are indirect and circumstantial. It is not the directness or indirectness of the data that is important, rather it is the repeatability of the findings and the coherence of many lines of evidence that are crucial to scientific knowledge. A good analogy to the study of brain evolution is provided by the study of gene evolution. Modern techniques for analyzing and comparing base sequences of DNA molecules from living organisms are beginning to provide a truly astronomical fund of information concerning both molecular and organismal evolution. Without analyzing a single fossil specimen of DNA we are nonetheless capable of reconstructing large fractions of the genomes of extinct species, characterizing major gene duplication and reorganization events of the distant past, and predicting the ancestral lineages of living species and the approximate dates of their divergences. Al this is available today despite the fact that only miniscule portions of the DNA in even the best studied species are actually known and virtually nothing is directly known about the DNA of most species. This level of analysis is made possible by the immensity and complexity of the existing genomes. In many cases even direct fossil evidence of apparent phylogenetic relationships has been abandoned in the face of contrary molecular information. As nearly limitless sources of correlative molecular evidence are fed into phylogenetic analyses in the near future they will become immensely more reliable for the determination of phylogeny than the best of all possible fossil finds. Living organisms are incredibly complex systems at all levels of scale. Each molecular and organ system within an organism embodies within its design the ubiquitous mark of its particular evolutionary history. In addition, the processes of embryogenesis that direct the construction of these systems are themselves products and symptoms of an evolutionary past that at various levels intersects with the ancestries of other species. Comparisons of the differences and similarities among molecular systems, organ systems and developmental processes in different species provide an almost limitless source of information for investigating the evolution of biological structures. This is ultimately the final arbiter of any analysis of evolutionary relationships—even for paleontological data— since the interpretation of fossils is only as accurate and complete as the information we have about living counterparts. The complexity of the vertebrate brain rivals or exceeds the complexity of all the other organ systems of the body considered together. Because of this we should expect that information derived from the brains of living species will be more than adequate to the task of investigating brain evolution, so long as we are willing and able to approach the task with the level of sophistication demanded by it. Given this complexity and our still primitive understanding of brain organization and function, we must be prepared to integrate information from a variety of subfields of neuroscience and evolutionary biology in order to begin to approach the problems of brain evolution with any clarity. Although numerous researchers since the RETHINKING MAMMALIAN BRAIN EVOLUTION nineteenth century have pursued the study of brain evolution, most have focused on a single source of evidence to support their theories, including: relative brain size (e.g., apparent trends in brain size increase); features of cortical surface morphology (e.g., the appearance or reorganization of sulci); relative sizes of macroscopic brain structures with respect to one another (e.g., the apparent enlargement of isocortex with respect to limbic cortex in presumed "advanced" brains); or cyto- and myeloarchitectonic features (e.g., the apparent enlargement of association cortex in the cerebral cortices of "advanced" species). But uni-dimensional approaches are almost certain to lead into one misleading cul-desac after another. This has been the fate of many past theories, just as it will surely also be the fate of the corresponding unidimensional theories of the present. The only hopeful approach is to integrate relevant information from many lines of neurobiological research that bear on the questions of the patterns of variation and constraint exhibited by the brains of different species. Experimental approaches A number of recent technical advances have significantly augmented the information previously available to comparative neuroanatomists. Unlike many other organ systems, the functionally relevant features of brain anatomy are entirely microscopic and for many decades were nearly impossible to distinguish even under the microscope. The axonal connections linking neuron to neuron, though visible for short distances in Golgi-stained material (available since the turn of the century), have only become amenable to study in recent decades. In the 1950s techniques were perfected for visualizing degenerating axons. With these techniques it was possible to identify the general patterns of long axonal connections in the brains of experimental animals. However, the resolution and sensitivity of these techniques were insufficient to resolve many of the finer details of axonal connection patterns. Beginning in the mid 1970s a number of axonal trac- 631 ing techniques were developed that took advantage of the in vivo uptake and axonal transport of amino acids, macromolecules and certain fluorescent dyes. These techniques have now made it possible to investigate the organization of axonal circuitry in full microscopic detail. In this regard the most basic functional anatomy of the brain has at last become available for study. We are still far from possessing a complete connectional characterization for even the best studied of mammalian brains, yet already the scattered details from comparative studies have begun to provide a remarkable array of new insights into the patterns of brain diversity. Now that tracer techniques have filled this crucial gap in information about basic neural functional anatomy, these data can be integrated with data from physiological and quantitative studies to provide all the pieces of evidence necessary for investigating the principles underlying brain evolution. However, it is insufficient to apply the analysis to adult brains only. Probably the most crucial information for evolutionary purposes is how connection patterns and structural differentiation are initially established in a developing brain. New techniques for labeling mitotic cells, marking cell lineages, and experimentally altering development in neonatal animals or in utero by removing or transplanting embryonic tissues are also beginning to provide detailed information about the developmental processes that shape neural circuits. Developmental information can play a crucial role in settling questions of homology. More generally, it can provide evidence for the range of possible mechanisms available for natural selection to modify and demonstrates the constraints that limit possible variation. Many scenarios of brain evolution conceived in the absence of critical information about the development of the structures in question turn out to be incompatible with these constraints. This rapidly growing body of neurobiological information is providing an unprecedented opportunity to discover new patterns of similarity and variation in brain evolution, and to test old and new hypoth- 632 TERRENCE W. DEACON eses about neural evolutionary processes. It also provides impetus for a critical reexamination of the dogmas and unanalyzed assumptions that currently dominate thinking about brain evolution. influence they exert over contemporary ideas about brain function in general. The first of these is the concept of homology— the relationship shared by structures by virtue of sharing a common ancestry. The second is the notion of evolutionary progress Conceptual problems or orthogenesis—the idea that evolution Most of the theories concerning brain proceeds in a particular direction of evolution in the early part of the 20th cen- improvement or development. The third tury focused on its most studiable features, is the significance of brain size—both in size, gross morphology, and cytoarchitec- absolute terms and relative to the body or ture. Crude connectional information was to component brain structures. The fourth available only from careful dissections and is the problem of identifying and explainwhat little could be discerned with Golgi- ing neogenesis—the evolution of new strucstaining. Despite the unavailability of cru- tures and functions. cial categories of information, theories of All four conceptual issues were familiar brain evolution have flourished. As a result to the 19th century pioneers in this field modern students entering this field will find whose major assumptions in all these areas the literature replete with numerous well remain dominant in many contemporary accepted dogmas about the general char- treatments of the subject. Contemporary acter of brain evolution espoused by some versions of these ideas are in the backof the century's most brilliant comparative ground of every theory of brain function neuroanatomists. Even more daunting is as well as every attempt to articulate a thethe fact that many of these dogmas have ory of brain evolution. Despite a century become seamlessly woven into the anatom- of advances in evolutionary thinking in ical and functional terminology of the rest other fields of biology these ideas within of the neurosciences as well. Terms like the neurosciences still carry the distinct paleocortex, neocortex, primary areas, imprint of late 19th century evolutionism. secondary areas, projection cortex and And despite a century of experimental association cortex all bear the stamp of an investigation of brain function these ideas evolutionary vision that appears beyond still reflect one or the other side of the 19th question, a part of the unspoken common century debates over associationistic and knowledge of the neuroscienres. But with holistic theories of mental function. Exorthe recent advent of new tools and a flood cising these influences is one of the central of new information concerning neural con- purposes of this presentation. nections, functions and ontogenetic proThe other major purpose is to present cesses, there has been a growing dissoan alternative approach to the study of nance between the new data and some of brain evolution that avoids many of these the well-established principles of brain evoa priori assumptions. Many of these lution. My purpose here is to play devil's assumptions have been derived from advocate; to question even the most well attempts to arrange the adult nervous sysfounded of these dogmas and adopt the tems of contemporary species in some sort heretical stance that many—if not most— of evolutionary sequence or cladistic denof the traditional assumptions concerning drogram so that any two may be linked via neural evolutionary processes are without some intermediate adult forms. While this foundation. Hopefully, the introduction of is a powerful heuristic it tends to imply the a healthy dose of skepticism will allow us misleading conclusion that the mechato look at the problem of brain evolution nisms for evolutionary change can be accuwith fresh eyes. rately described in terms of the modificaFour major conceptual problem areas will tion of adult forms. This, of course, misses be reexamined most carefully because of a crucial intervening level of analysis. Ultitheir potential for misdirecting the study mately, the mechanisms of evolutionary of brain evolution and also because of the change must be explained in terms of the 633 RETHINKING MAMMALIAN BRAIN EVOLUTION ontogenetic processes and developmental constraints that build brains. Explanations of evolutionary change that are not cast in developmental terms are merely disguised comparative morphological descriptions. Consequently, the reviews and criticisms presented will constantly appeal to developmental data to test the plausibility and consistency of some of the dominant theories of brain evolution. Finally, in the last two sections many of these developmental insights will be utilized to outline an alternative ontogenetically based interpretation of the processes underlying brain evolution in mammals. This interpretation of brain reorganization events—called the "displacement hypothesis"—suggests that there is an interdependent relationship between differential growth of particular neural cell groups and competitive-regressive processes in brain development that constrain patterns of brain evolution, often resulting in parallel or converging trends. Two particularly enigmatic cases are examined in the last section—dolphin and human brain evolution—and are interpreted in terms of displacement processes. Features of these brains that previously have been difficult to explain or seemed beyond study become understandable in terms of the displacement hypothesis. CLAD.STX COVPAR SON B CLAD STC HCVCLCSY C \ / \ / \ / [ fO \ PATRISTIC . / \ HOMOLOGY / \ / 0 PARALLEL HOMOPLASY CONVERGENT HOMOPLASY D \ / O I o V FIG. 1. Homology relationships. The basic homology relationships as outlined by Northcutt (1984). The arrows represent descent relationships. The vertical axis represents comparison over time or descent in evolution and the horizontal dimension represents comparison of contemporaneous species. The geometric shapes represent similar or different, ancestral or derived traits. In the case of parallel homoplasy it is unclear whether the parallel divergence from the ancestral condition is a consequence of internal (homological) or external (selectional) commonalities. These same relationships can be applied equally to comparisons between lineages or to homologous repetition of parts within an organism. (Redrawn from Northcutt, 1984.) applied to traits that exhibit structural or functional similarities but which are not derived from common ancestry. In other words, their similarity is the result of influHOMOLOGY ences extrinsic to the organism. The other, The concepts of homology and homoplasy parallel homoplasy, has traditionally been The concept of homology in some form termed parallelism and refers to cases where is essential to any study of evolutionary there is similarity in both form and commorphology. It defines the warp of evo- mon ancestry but where the formal similutionary continuity with respect to which larity between traits is not shared in the the weft of diverse adaptations can be common ancestral condition. In other understood. In a useful summary of the words, the formal similarity of the (patrisproblems of homology in comparative neu- tically) homologous parts is presumed to roscience Northcutt (1984) distinguishes have arisen independently in the two linpatristic homologies (the actual descent rela- eages after divergence from the common tionship between an ancestral form and a ancestor. In this case there is both a patrispresent form) from cladistic homologies (the tic homological relationship and a cladistic comparison of extant forms with respect convergent homoplaseous relationship to their possible common ancestral rela- involved. The parallel divergence of the tionships) and then contrasts these with two two descendent traits from the common forms of dishomology that may be con- ancestral condition is presumably the result fused with homology. One of these, con- of common extrinsic selection pressures. vergent homoplasy, corresponds to what has These relationships are schematized in Figtraditionally been termed analogy and is ure 1 (redrawn from Northcutt, 1984). 634 TERRENCE W. DEACON Where the ancestral condition is the unknown feature to be inferred from cladistic comparisons it can be quite difficult to distinguish homology from these two forms of homoplasy. Northcutt, following Wiley (1981) and others, suggests a number of guidelines for aiding this discrimination, including: (1) sharing deep similarities of form (as opposed to merely superficial resemblances), (2) sharing common epigenetic precedence (i.e., derivation from common ontogenetic precursor structures), and (3) the existence of a continuity of intermediate forms in species intermediate in relationship between the two being compared. All there criteria are versions of the identification of similarity in some form. In this discussion I will not review the various problems encountered in attempting to determine neural homologies in practice, nor will I discuss methodological strategies for circumventing these problems and the multiple criteria that must be satisfied to provide a convincing case. These have been well reviewed elsewhere (Campbell, 1982; Campbell and Hodos, 1970; Ghiselin, 1976; Northcutt, 1984). The point of this discussion is to analyze the concept itself as it is applied to problems of brain evolution because I think there is reason to be suspicious of the assumptions implicit in its common usage. It is not the empirical determination of homology that is at issue, but the concept itself. I will argue that there is something fundamentally wrong with the notion of homology as it is applied to the comparison of morphological features that can become especially troublesome in the analysis of brain structure. Problems with the concept of homology Homological relationships are most clearly exhibited in topological relationships. A focus on topological continuity for identifying homology dates back to the earliest pre-evolutionary theories about the vertebrate "Bauplan" (an insightful historical discussion is provided in Russell, 1916). Because organisms are spatially organized systems, position within a network of relationships is crucial to conti- nuity of function. Although the particular features of the individual components of the organism may change over evolutionary time the systemic relationships among parts, including their contiguity relationships, are relatively stable. Even when structures derived from a common evolutionary precursor have diverged in form so as to share no superficial resemblance their relationships to other structures within the organism, both in the adult form and at various stages of development, will exhibit sufficient similarity to indicate their homology. The usefulness of topological criteria for the determination of homological similarities derives from the fact that many morphological traits (although not the underlying genes themselves) are determined by systemic interactions between morphogenetic fields (or other converging morphogenetic influences) and not by independent local mechanisms. The information that determines a morphogenetic field inevitably derives from multiple genetic sources interacting with one another sequentially and simultaneously during ontogenesis. The resultant morphological trait is a bit like a node within a network that has no independent existence apart from its relative position. Such a node is defined by its unique convergence of relationships with other nodes. If some of these relationships are lost or new ones are gained, continuity with the previous state becomes ambiguous and depends on whether you focus on the relationships or the nodes. Analogously, a single morphological feature may become two if interdependent morphogenetic processes decouple in space or time, but two features may also fuse to become one if previously noninteracting morphogenetic processes become subsequently linked and interdependent. This possibility is more likely in the brain than in other organs by virtue of the fact that brain traits are defined in terms of two independent topological criteria: (1) cell lineage relationships of local populations that may determine local topological relationships, cell structure, and molecular and neurotransmitter characteristics; and (2) connectional relationships determined by RETHINKING MAMMALIAN BRAIN EVOLUTION axons that link numerous separated targets, each likely derived from different cell lineages, which may also influence morphological, cellular and functional characteristics of their various target structures. Both cellular and connectional attributes interact during development to determine the architecture and function of a brain region. Assuming that connectivity is capable of changing during the course of evolution it is not hard to imagine the kinds of difficulties that might arise in interpreting homologies. The position, cellular characteristics and even embryonic origins of a brain structure in a descendent species may be derived from a corresponding structure in some ancestor, and the descendent structure's connectional relationships may also be derived from connection patterns in that same ancestor. Yet the particular homologous circuit and homologous structure may not have been associated with each other in the brain of that ancestor. For example, it is conceivable that a series of evolutionary events can cause afferents of one brain structure to invade some other structure, replacing the "ancestral" afferents of the new target— similar effects can be induced experimentally (see below). In this event a connectional or circuit homology will have been maintained, probably retaining its functional characteristics, but the relationship between cellularly defined homologues and connectionally defined homologues will have become dissociated. The structural homology can no longer be defined with respect to position within a network and the connectional homology can no longer be defined with respect to the structures that are connected. Continuity with the ancestral form can nonetheless still be traced through the remaining descent relationships in each case, though the number of topological criteria used to identify this descent has diminished for each. Further deterioration of homology criteria can also be imagined. For example, if the connectional relationships play a significant role in determining the local cytoarchitecture and neurotransmitter characteristics of a target area (this appears 635 to happen in cerebral cortex, as indicated by heterotopic transplantation experiments) it might appear on these grounds that an ancestral target has simply become displaced to a new position, despite the fact that cell lineages and some connectional relationships did not follow this shift. With a large number of criteria in agreement, but cell lineage and a few connectional relationships do not follow this shift. With difficult to decide between the deafferented target or the invaded target as the appropriate homologue of the ancestral structure. At the level of the whole structure the judgment is ambiguous and yet each underlying trait has a homologous pair that can be traced in unbroken series to a common ancestral condition. It is not clear that shifting the analysis to these underlying traits can escape similar problems at a yet lower level. As we consider evolutionary "interventions" that might alter progressively earlier stages of ontogeny it is possible to imagine increasing loss of descent criteria in this manner. A similar complication can arise in the effort to identify homologous sulci in relatively convoluted brains. Prior to the development of techniques for unambiguously staining myelin or neuron cell bodies the interpretation of sulcal homologies in different species brains was considered the best clue to the structural homology of underlying regions, and this approach dominated throughout the early part of this century (Ariens Kappers et al., 1936). Although it has recently been abandoned as unreliable for most comparative work, it still remains the only evidence for paleoneurology (working with the casts of fossilized crania). In the study of human evolution this has been the source for continuing heated debates over the origins of "modern" human brain traits {e.g., Falk, 1980, 1983, 1989; Holloway, 1981, 1984, 1985, 1988). Most cortical sulci are probably the result of the interactions between the mechanical forces and constraints imposed by the cranial cavity, differences in growth rates of brain areas, and relative elasticity of different areas of the developing cortex and underlying white matter. If the underlying 636 TERRENCE W. DEACON neural substrate influencing the formation of a sulcus changes or becomes displaced with respect to cranial landmarks in subsequent lineages it may cause the position of the sulcus to follow. If this were the typical case sulci might be relatively good indicators of underlying brain structure homologies. Alternatively, changes in bone growth patterns of the skull or changes of the absolute size of the brain with respect to the skull in subsequent lineages may produce changes in mechanical forces influencing sulcal position and cause a sulcus to shift to a new location without any corresponding change in position of the original neural substrate. In this case the link between sulcal and neural homologies would be broken. However, if the appearance of a particular sulcus is dependent on the combined influence of both extrinsic mechanical forces and intrinsic growth processes of the neural tissue, then spatial separation of these two independent influences may cause a sulcus to disappear and then reappear in some later lineage in which these influences again become realigned. This atavism would still be a case of homology, despite the discontinuity in descent. Finally, if a particular sulcus can be induced by either influence alone then spatial separation of extrinsic and intrinsic factors may also produce two sulci where one existed previously. In this case, although each is patristically homologous with the ancestral condition, it is unclear whether they can be said to be cladistically homologous to each other. All of these possibilities demonstrate the dangers of treating sulci as definitive markers of underlying neural homologies. Similar problems with the strict descent interpretation of homology have been noted with respect to non-neurological comparative problems, causing some authors (e.g., von Cranach, 1976; Filler, 1986) to suggest that the homology concept should be entirely abandoned. But an alternative approach is suggested by these problematic examples. The crucial questions we are trying to answer by identifying homologies are questions about continuity of information (Van Valen, 1982). A morphological structure or any other manifest trait is only the surface expression of underlying information. This information is encoded both in gene sequence and in the topological and temporal conditions of their expression in the developing organism. The confluence of multiple independent sources and kinds of epigenetic information to form a particular structure implies that no particular individual thread of information constitutes an indispensable link between homologous structures. A homology exists so long as some relationships between the remaining sources of information are maintained. Alternatively, if separate threads of information are passed down from generation to generation independently and only brought into relation with one another in some descendent where their interaction produces a novel structure, we must consider the structure as emergent and neoplastic (the newly established relationships between these threads of information is itself a bit of information that is unprecedented) and yet also recognize the complete homology of underlying component morphogenetic processes. The relationship is diagrammed in Figure 2. Because ontogenetic processes are multileveled, homological relationships must also be multileveled (Alberch, 1982; Fasolo and Malacarne, 1988), with homologies at higher levels not necessarily reduceable to those at lower levels. In addition, homologies at every level above that of the genes are to some extent ephemeral, capable of dissolving and reconstituting in the course of evolution because they are determined only in relational terms. This also implies that the same bit of epigenetic information expressed in a different context within the same organism must also be understood as homological. Homologies between the different parts of a brain The interpretation of homology as common information is crucial to another classic use of the concept of homology: serial homology or homological multiplication of parts. Repeated similar parts in the segments of a worm, similar vertebrae in different positions along the spinal column, similarities in limb and digit structure, and bilaterally 637 RETHINKING MAMMALIAN BRAIN EVOLUTION symmetric parts of the body in general are all examples of homologous repeated parts. Although not descended from any single ancestral structure, such homologous parts undoubtedly inherit their similarities of form from a single ancestral source of developmental information. Within the central nervous system there are examples of classic serial homology in segmental spinal cord circuitry, bilaterally symmetric parts at all levels, and multiple homologous parts within every structure at many levels of organization. Starting on a small scale we recognize that nearly all neurons exhibit homologous parts: axons, dendrites, synapses, etc. Within the same structure there are classes of neurons with homologous patterns of dendritic arborization, axonal targets and neurotransmitters. Local circuit patterns of nearby neuronal groups also exhibit homologies, such as are found among cortical lamina and cortical columns in isocortex. Even distributed functional circuits linking separate structures may be serially homologous: e.g., projections from different thalamic nuclei to different cortical areas. Even structures that are superficially quite distinct may exhibit underlying homologies at some levels but not others. This might be the case for the relationship between the hippocampus and the isocortex, which exhibit many features in common at the cellular level and have homologous patterns of afferents and efferents yet very different laminar architecture. Homologies between different brain regions might possibly develop as a result of derivation from a common undifferentiated ancestral structure, but descent homology need not be defined at the structural level only. It may also result from independent expression of the same underlying epigenetic information. Similarly, during ontogeny homologies between cell types may develop by descent from a common embryogenetic cell lineage and homologies between complex structures may develop because they were each derived in a process of differentiation from some common embryological structure. However, because all cells share the same genetic information, it is also possible that onlogenetic interactions phenotypes morphological level epigenetic process level epigenetic mechanisms \ / \ ; ontogenetic interactions morphological homoplasy phenotypes epigenetic homologies intervening variables producing developmental plasticity FIG. 2. Developmental homologies. The multiple level problem of developmental homologies is depicted in a highly schematic form by representing interacting morphogenetic processes as arrows and the resultant morphological traits as geometric shapes. The upper figure shows that the homological relationships could be analyzed either at the morphological level by comparing morphological (or even behavioral) phenotypes or at the epigenetic process level by comparing epigenetic mechanisms. Both are phenotypes that indirectly represent underlying genotypes but the higher level analysis condenses information represented at the lower level by distinct processes and can thereby miss considerable underlying developmental homology. Nonetheless the higher level analysis also takes into account conserved or derived relationships between underlying developmental mechanisms that may themselves be homologous in two lineages but which produce non-similar diverging phenotypes. Of course the actual condition involves many more than two levels. The lower level "epigenetic mechanisms" are likely themselves the products of relationships between yet lower level cellular or molecular processes and the "phenotypic level" may also be a set of epigenetic mechanisms of a higher level of complexity. Hierarchic analysis cannot be avoided. homologous structures may appear simultaneously in development by independent expression of the same underlying information activated by some common molecular trigger or internal timing mechanism. Within a number of areas of the brain it is likely that cell lineage is not the only and perhaps not even the major determinant of cellular, structural or functional homologies. For a few brain areas there is now evidence that a single precurser cell can give rise to the multiple cell types within 638 TERRENCE W. DEACON that region (Rakic, 1988) and studies utilizing embryonic chimeras composed of two immunologically distinguishable genomes demonstrate that cells from both lineages are effectively scattered diffusely throughout all areas and representing all cell types (Goldowitz, 1987). Cell lineage probably determines many local biochemical characteristics of neurons (Fasolo and Malacarne, 1988) and certain structural architectonic features (e.g., Kuljis and Rakic, 1988) and it may provide gross areal differences distinguishing major morphogenetic fields, but at the present time there is little positive evidence available on this point and extensive evidence that extrinsic influences determine function and neural connectivity to a large degree (O'Leary, 1989). Timing of final mitosis and intercellular interactions also appear to play significant roles in determining neuronal cellular types and local structural and functional characteristics. In general, a major part of structural differentiation in the developing brain is based on distributed information that is embodied systemically in its spatial-temporal organization and dynamically in the axonal interactions between independently derived neuronal populations. The details of this process will be discussed in later sections, but in terms of homology this fact leads to an important conclusion. If the information distinguishing one region from others is not entirely embodied within the cells of that region, but is expressed only as those cells are contacted by invading axons and as its own axons establish efferent terminations, then different serially homologous structures within the brain (e.g., different cortical regions) do not ultimately determine their own distinctions of structure and function. Their unique specializations with respect to one another are instead derived from network relationships with other areas of the brain (both cortical and subcortical). Functional homology One last use of the concept of homology must be introduced at this stage before moving on to a discussion of some of the major theories of brain evolution: the con- cept of functional homology. It can be defined as the similarity and continuity that exist between functions as a result of homologies between their substrates. The evolution of new functions by the modification of old structures is a common theme in evolution. When the vertebrate forelimb evolved the capacity for flight in the evolution of birds, the skeletal, muscular and neural structures retained the general "Bauplan" of the ancestral terrestrial condition and also retained numerous functional constraints. These have all played a role in shaping flying behavior in bird species. Additionally, the peripheral motor neural architecture (Sokoloff et al, 1989) and even features of the central locomotor "patterns" (Kaplan and Goslow, 1989) exhibit strong similarities in birds and terrestrial quadrupeds, despite the other major functional differences that their exclusive adaptations demanded. With the differentiation of new neural circuits from ancestral circuits and the elaboration of corresponding new functional adaptations we can expect to trace functional homologies in the form of underlying functional similarities and constraints. Even in extreme cases in which neural structure is co-opted for new adaptations that are radically different than the ancestral function, the underlying homologies will likely exert a major organizing influence on the form and range of variability of the new function. This may even be true of such a novel adaptation as the syntactic structure of language (e.g., Reynolds, 1976; Lieberman, 1984; Deacon, 1990c), if some of the cortical systems that came to serve language functions in the course of human evolution had been antecedently adapted for other behaviors (e.g., motor planning). Anatomical evidence for such a view is presented by Deacon (1988a, 1990c). Functional homologies should also be exhibited by serially homologous structures within the same brain. For example the many homologous structural features shared by all regions of isocortex suggest that there should be strong functional homologies shared by all of its functional subdivisions despite the radical differences RETHINKING MAMMALIAN BRAIN EVOLUTION in modality of their input-output relationships (Diamond, 1979). The same may also be said of the different regions and subdivisions of the basal ganglia (Alexander and Strick, 1986). Presumably, the afferents to each homologous area transmit distinct forms of information that are subjected to some common neural calculation in each homologous area. For this reason, different scenarios for the phylogenetic ancestry of brain structures that suggest different ancestor-descent relationships bring with them different predictions concerning function. PROGRESSION The assumption of evolutionary progress The idea of progressive evolution is a product of the uneasy marriage between Darwinism and the scala naturae theories of the mid 19th century. It received its clearest expression in the theories of Spencer, Haeckel, Berg and Teilhard de Chardin among other influential writers. Although evolutionary biologists in recent decades have learned to rigorously avoid making such assumptions when thinking about a particular assemblage of fossils or a lineage of species, this habit of thought is not so well entrenched in the neurosciences, nor in anthropology, psychology or linguistics where theories and assumptions about human brain evolution are also likely to be found. The tendency is so pervasive that evolution is often considered synonymous with progress, whereas evolutionary change without progress, even when directional, is often not considered evolution at all, merely "drift." The ubiquity of the idea of progress in brain evolution can be traced to what we believe we already know about our own place in an intellectual chain of being. It apparently goes without saying that humans are the smartest species to have ever lived— never mind that we are not sure what we mean by "smartest"—and it is also popular knowledge that human evolution involved significant brain enlargement. Our brain must also be the most complex, if for no other reason than the fact that our abilities are the most complex of any species. Since 639 we have appeared only recently after a long period of brain evolution characterized by less intelligent species, our brain represents the pinnacle of some long evolutionary trend. From these few assumptions a great many predictions must follow, and so from the outset we feel confident in assuming the answers to a number of central questions: bigger brains are smarter brains; more complex brains are more developed brains; primates are smarter than other species; our closest relatives, the great apes, are smarter than other primates; there is an evolutionary trend toward increased intelligence; more intelligence is always a superior adaptation to less; brain evolution tends toward increasing complexity and increased relative brain size; earlier stages of brain evolution are characterized by more primitive, relatively less differentiated and relatively smaller brains than later stages; parts of the brain that are relatively undifferentiated are more primitive and parts that are more complex are more recent; brain structures that enlarged most in ourselves and our close ancestors are the most highly developed and most recent brain structures; the most recent human functions (i.e., language) must be controlled by the most advanced, complex and recent structures in the brain; etc. All we need to do is to find out how the data concerning brain size and brain structure diversity demonstrate these points! Presumably, whatever features of brain organization we use to compare brains of different species, Homo sapiens should represent the extreme high end of the scale (however this is defined in each case). I call this assumption the "Anthropocentric Maxim." The tenacious hold of anthropocentrism on our thinking about brain evolution is great. What is needed is a biological equivalent of the "Copernican Revolution" to finally shake it loose. Along with this implicit anthropocentrism we should also endeavor to root out the tendency to assume progressive trends in any aspect of brain evolution, unless and until all alternative explanations have been exhausted. There undoubtedly are progressive trends in brain evolution, but to clearly identify 640 TERRENCE W. DEACON them and to understand their significance we must demonstrate that they are not merely superficial correlates of other nonprogressive trends. To be able to do so requires that we first understand these other trends. The a priori assumption of "advancement" in evolutionary sequences is a source of many misunderstandings. Deacon (1990a) reviews many of the assumptions about brain evolution that derive from the notion of evolutionary progress in brain size. Even theories that do not specifically invoke the notion of progression nonetheless tacitly assume it in the process of identifying some structures as "advanced" and others as "primitive." A primitive to advanced ranking of living organisms or their structures must ultimately be based upon independent knowledge of the evolutionary trend in question; otherwise the argument is circular. But when faced with structures that leave no fossil evidence independent evidence is hard to obtain. One possibility is to assume that the progressive ranking of soft-tissue structures should correlate with other preserved indicators of the relative primitiveness or advancement of the organism as a whole. Overall similarity of traits from living species to those in early fossil specimens of some lineage might suggest that the organization of brain structures is also equally comparable. It is of course necessary to determine that the resemblances are not superficial and the result of convergent evolution. And even when this can be demonstrated there is never any guarantee that the brain structures in question have been as conservative as the rest of the morphology. Even the external morphology of the brain, as may be revealed by endocasts, cannot be taken as a reliable indicator of underlying cellular and connectional homologies. So a primitive external appearance of modern brains is an untrustworthy indicator of primitive brain organization. From simple to complex It seems unquestionable that simpler brain structure precedes more complex brain structure in the course of evolution, and that more highly differentiated brain structures are more advanced than more diffusely organized brain structures. Although we can probably assume that there are some recent brains that are more differentiated than any from fifty million years ago, we cannot safely invert the logic and assume that the most undifferentiated contemporary brains are the least derived. Confounding variables such as absolute size and specific sensory-motor specializations may influence relative differentiation, and problems in assessing homology as well as sampling biases inherent in the phyletic representation of species may introduce spurious correlates of differentiation that have nothing to do with primitiveness. In discussions of mammalian evolution small bodied living insectivores are typically treated as exemplars of the morphologies of ancestral mammals. These socalled "basal insectivores" are assumed to be "generalized" in their adaptation and "conservative" with respect to evolutionary trends, although caveats are usually suggested regarding the fact that each of these groups represents some rather specialized adaptations as well. The European hedgehog (Elliot-Smith, 1910; Ariens Kappers et al., 1936; Filimonoff, 1949; Diamond and Hall, 1969; Valverde and LopezMascaraque, 1981; Sarnat and Netsky, 1981) as well as moles, tenrecs and microchiropteran bats have all been cited as possessing conservative brain structure typical of an "initial" mammal brain (Sanides, 1969, 1970; Le Gros Clarke, 1971; Glezer et al., 1988). There are unfortunately a number of circular assumptions in the concepts of "primitive survivor" and "basal insectivore" (Martin, 1973) that also afflict the concept of an "initial brain." Fossil specimens suggest that it is likely that the eutherian mammal ancestor which gave rise to the Paleocene-Eocene radiations was of relatively small body size and probably bore at least a superficial resemblance to modern shrew-like insectivores. In this regard there is considerable justification for selecting insectivores as exemplary of the ancestral condition. The presumption that the common ancestor was somehow "generalized" or even that mod- RETHINKING MAMMALIAN BRAIN EVOLUTION ern basal insectivores are "generalized" species seems a little more puzzling, although it is widely claimed. In many ways members of these groups represent some extremes of specialization. Consider, for example, the echolocation specialization of microchiropteran bats, the fossorial or nocturnal specializations of many shrews, moles and hedgehogs, the aquatic specializations of some exceptional shrews, and of course the insectivorous specialization itself. These facts must certainly relate to their neurological adaptation. Of course there is every reason to suspect that the common ancestor of eutherian mammals was also specialized in some interesting ways, but given the radical difference in faunal context and likely niche specialization there may be no corresponding specialization represented in modern species. The tree shrew Tupaia has been suggested by some as an appropriate living model for a Paleocene precursor to primates (Le Gros Clark, 1971;Cartmill, 1972, 1974). In terms of its size and many of its non-neurological features it too might serve as a reasonable stem mammal model. But it is usually disqualified as an "initial brain" model because it possesses a number of "advanced" brain features, including moderate encephalization and a differentiated striate cortex and visual association cortex. The cortex of Erinaceus, the European hedgehog, is often treated as a model of an ancestral mammalian cerebral cortex. Figure 3 depicts some of the known areal divisions of the hedgehog cortex along with an even more "primitive" tenrec brain. Some notable features of the isocortex of these species as compared to "advanced" brains include: relatively small size compared to olfactory and limbic cortex, poorly distinguishable lamination, low level of myelination, poor differentiation from area to area, lack of a clearly distinguishable agranular motor area, poorly granularized koniocortical sensory areas, vagueness of boundaries between architectonic areas, the apparent adjacency of sensory-motor projection areas with little interdigitated association cortex, and a relatively thick layer I (a limbic cortex characteristic) in all areas of its isocortex. It seems unquestion- 641 able that these brains are near some extreme in the spectrum of cortical organization among eutherian mammals, but this may not be conservatism. In fact, on the basis of brain traits selected for their value in determining cladistic relationships, Kirsch et al. (1983) find that hedgehogs do not appear to exhibit a preponderance of conserved traits, but just the opposite, they appear to possess one of the most derived mammal brains (Johnson, 1988). It is clearly not the structure of the hedgehog body that motivates its choice as an exemplar. It exhibits highly specialized spiny hairs for predator protection and has developed the ability to role into a ball with only its spines exposed, it has relatively short, stubby limbs specialized for digging, it has very rudimentary visual abilities with clearly reduced eyes that are appropriate to its nocturnal-fossorial habit, and it has a well developed specialized snout and presumably highly specialized olfactory abilities for insect predation. Campbell (1988) remarks that if the hedgehog were otherwise the same but possessed a larger more differentiated brain it would never have been considered an exemplar of the "initial brain" pattern. Gould (1977) notes that in general it is unwise to choose the most undifferentiated extant member of a group as a representative of its stem ancestor precisely because small bodied fast breeding forms are likely to be highly derived r-selected species. The choice of species with small undifferentiated brains is not so much motivated by external similarities with known fossil types as by a priori assumptions about what is primitive and what is advanced. To carry this paradigm to its logical extension, the hedgehog is probably not the most extreme case that could be cited. Zilles and Rehkamper (1988) point out that Erinaceus is actually somewhat advanced with respect to some other basal insectivores and therefore might not be the ideal exemplar of the "Grundtypus" for mammalian brain organization. They note that the brains of the tenrec Echinops and the geogaline Geogale exhibit even less encephalization and exhibit fewer progressive fea- 642 TERRENCE W. DEACON dorsal view dorsal view brain of a tenrec brain of a hedgehog Centetes Erinaceus FIG. 3. Hedgehog and tenrec brains as seen from above and the side labeled to show approximate positions of the major sensory and somatomotor fields. Isocortex is indicated in gray in the left hemisphere of each and limbic and olfactory cortex is white in the same hemisphere. Since most of the cortical representation is unknown for the tenrec and only partially known for the hedgehog specific boundaries between areas are not indicated. There is no intent to imply either undifFerentiated cortex or the existence of only single sensory/motor fields. Note the low ratio of isocortex to limbic-olfactory cortex in these brains, especially the small tenrec brain. tures than that of Erinaceus. These authors conclude that Erinaceus is probably "not a typical representative of a real basal insectivore" (Zilles and Rehkamper, 1988; emphasis in the original). Only in a context where evolution is presumed to progress from simple to complex, from least encephalized to most encephalized, and from generalized, inflexible and inefficient in function to specialized, flexible and highly efficient in function, can the search for the absolutely simplest mammalian brain be equated with the search for the ancestral brain. There are two general attributes shared by essentially all the basal insectivores considered primitive in brain organization that should cause us to be cautious about generalizing from them. First, each of the candidate exemplar species inhabits a nocturnal-fossorial niche. This is probably no accident. This adaptation has likely produced secondary reduction or dedifferen- tiation of the visual system and a correspondingly heavy reliance on the olfactory system. Evolutionary reduction or degeneration of an essentially unused sense modality may induce dedifferentiation, but this does not likely follow an exactly reversed phyletic trend and may produce structural features that are quite distinct from ancestral features. How can we be sure that the relatively undifFerentiated state of the cortex of these species is representative of a retained primitive state rather than a recent specialization? Second, these exemplar species also represent the very lowest limits of mammalian brain size. This is a problem because many measures of structural complexity appear to be strongly correlated with brain size (Tower, 1954; Haug, 1987; Deacon, 1990a; and see the following section). Nearly all the attributes of "primitiveness" of mammalian brains are also typical attributes of very small brains, while those of "advance- RETHINKING MAMMALIAN BRAIN EVOLUTION ment" are only expressed in relatively large brains. Progressive trends measured with respect to these small insectivore species are significantly confounded with the effects of differences of scale. Also if there has been prolonged selection for size reduction in these species there may also be simplifications of brain structure of a secondary character which do not necessarily follow a reverse phylogenetic trend. Cladistic approaches The cladistic approach to identifying evolutionary trends offers some hope of resolving these ambiguities and avoiding the trap of implicit progressionism. By replacing the assumption of evolutionary development and increase in complexity with a simpler empirically defined dichotomy between conserved and derived conditions one can arrive at a relatively unbiased criterion for identifying evolutionary trends. The particular characteristics of the trait are irrelevant, only its presence or absence in different groups is important. By pairwise comparison of the presence or absence of traits between species in progressively more distant outgroups it is possible to decide which traits can be operationally defined as derived and which can be defined as ancestral or conservative. Cladistic analysis has wide acceptance as a means for reconstructing phyletic relationships between lineages, but it has also been used extensively to trace the ancestry of specific traits. It has been particularly useful for deciding between alternative accounts of a trait's evolution because it provides a measure of parsimony. For example, Northcutt (1984) uses the number of mutational events that must be postulated in order to explain the distribution of certain vertebrate brain traits according to different theories to decide which of these theories provide the most parsimonious accounts. The Achilles heel of this approach with respect to brain evolution is that it will inevitably tend to favor identifying relatively undifferentiated forms as more primitive and differentiated forms as more derived. A structure lacking differentiating features will tend to be glossed as sim- 643 ilar across a wider range of species than one exhibiting a number of easily discriminated features. As a result, despite its apparently unbiased definition of polarity, the cladistic approach may be biased, so as to pick out more generalized and less differentiated traits as characterizing a common ancestor. Evolutionary regression in certain lineages is potentially a source of misleading bias as is the correlation of absolute brain size with structural complexity. Additionally, this approach is sensitive to the effects of convergent or parallel evolution. It will be argued below that parallelism is a major feature of mammal brain evolution. Nonetheless, the cladistic approach is in some ways self-correcting in this regard. It can be useful in discerning some of these biasing influences by using multiple sources of information pooled to establish most parsimonious descent relationships and then reanalyzing individual trends. For example, the relative primitiveness of the "basal insectivore" brain can be tested with respect to three outgroups of mammals whose phylogenetic affinities are well known through other cladistic analyses: the marsupials and the two living monotremes, the platypus Ornithorhinchus and the echidna, or spiny anteater, Tachyglossus. Many of the characteristics of Erinaceus' brain, including apparent adjacency of projection areas, minimal association cortex, high ratio of olfactory-limbic cortex to isocortex, poor laminar distinction, poor granularization and poor differentiation of architectonic areas, are not exhibited in the brains of larger marsupials and monotremes. Have the apparently more advanced traits also found in these outgroups evolved independently in the larger brains of all the mammalian lineages? The more parsimonious interpretation is that many of these traits were present in some form in the common ancestor of all mammalian groups long before the recent eutherian radiations. That they fail to be exhibited by some of the brains in the eutherian lineage (e.g., basal insectivores) and some brains in the marsupial lineage (e.g., Didelphis virginiana) is not sufficient evidence to assume that they are derived. 644 TERRENCE W. DEACON Kaas (1989) applies an implicit cladistic approach to determine which cortical areas in all mammals can be traced via descent from a common ancestor. He notes that in all the major mammalian lineages (eutherian and metatherian) there are distinct visual, auditory and somato-motor projection areas within isocortex. He concludes that the common ancestor for all these lineages likely also possessed these differentiated projection areas and not just an undifferentiated protoisocortex. Based on this evidence he rules out a widely cited theory of cortical evolution proposed by Sanides (1970) that is based on the assumption that generalized undifferentiated isocortex preceded specialized sensory-motor projection cortices in the course of cortical evolution. However, to be more explicit, what has been demonstrated is that discrete somatic, auditory and visual projection areas are expressed in mammal brains under all existing conditions and sizes, whereas some areas, particularly many association areas, fail to be expressed under many conditions, specifically in small brains. The classic view that association cortex is new in comparison to projection cortex in part derives from the apparent lack of association cortex in basal insectivore brains (but this assumption is criticized below) and its progressive domination of the cortical surface in "advanced" mammals. However, some of the larger marsupials and even the echidna appear to exhibit significant expanses of association cortex in addition to primary sensory-motor projection areas. Apparently, under similar developmental conditions—large brain size—this trait is expressed in every lineage of mammals. The common conditions required for expression of this trait in all three lineages also lends confidence to the claim for homology as opposed to parallel homoplasy. The failure of basal insectivore brains to exhibit distinctly segregated association areas is not sufficient evidence to deny that this trait is a shared ancestral trait. Nonetheless the appearance of segregated visual, auditory and somato-motor areas in all mammal brains is sufficient evidence to consider them as shared ancestral traits. Lack of positive evidence is not sufficient to deny homology but the availability of positive evidence is sufficient to establish it. This can also be applied to questions concerning the origins of somato-motor areas. Lende (1969) demonstrated that in Didelphis the somatosensory responsive cortex and the electrically excitable motor cortex exhibited complete overlap and that in Erinaceus there was a large region of overlap. More recently some degree of overlap has also been demonstrated in rats (Donoghue et al., 1979). In carnivores and primates (and probably ungulates) these areas are adjacent but completely segregated into distinct parallel somatotopic and musculotopic maps. Lende also argued that there was even some overlap of visual and auditory cortical areas in the opossum (although this finding has not been replicated). This suggested to him that the ancestral state of cortex was characterized by poor areal differentiation in which all the sensory modalities exhibited nearly complete overlap with one another. However, at least one larger Australian marsupial, the brush-tailed opossum Trichosurus, exhibits considerable segregation of somatic and motor fields (Haight and Neylon, 1978, 1979) and the monotremes appear to exhibit complete segregation of somatic and motor areas. All of these facts argue against assuming that the primitive condition was undifferentiated and completely overlapping and suggest that at least some degree of segregation of these functional zones characterized the common ancestor of all mammal groups. But negative evidence can be cited to support the view that the segregation of somatic and motor modalities is a convergent trait. This evidence comes from variations in somatotopy of the sensory and motor maps in the different groups. In most eutherian mammals studied the two fields are arranged as mirror images of one another with respect to their common border, and exhibit this pattern even in species where there is considerable overlap of the two areas. However, in edentates and marsupials the two maps appear to be arranged in parallel as well as overlapped (Dom et RETHINKING MAMMALIAN BRAIN EVOLUTION al., 1971; Lende, 1963, 1969; MagalhaesCastro and Saraiva, 1971; Royce et al., 1975; Saraiva and Magalhaes-Castro, 1975), and in a megachiropteran bat {Pteropus poliocephalus) the somatic map appears inverted from that typical of most other eutherian mammals (Calford et al., 1985). The monotremes appear to exhibit characteristics found both in some eutherian and in some marsupial brains (Bohringer and Rowe, 1977). Furthermore, the pattern of thalamocortical connections to these areas differs in eutherian and marsupial brains. This negative evidence is inconclusive because the differentiation of map orientation could occur independent of the segregation of somatic and motor areas. The unique status of the fox bat and edentate somatic maps in comparison to other mammalian groups suggests that this is the case. Map orientation appears to be a derived condition in these species. Variation of this trait occurs against the background of somatic and motor map segregation as an apparently older and more conservative trait. The only placental and marsupial mammals that do not exhibit segregation of somatic and motor projection areas also have relatively small brains. This further suggests that this is a derived condition contingent on small size and not the ancestral condition. Problems with these comparisons of cortical areas stem from the fact that the traits under consideration are not simple and the variables that correlate with the differential expression of these complex traits have not been controlled for in the analysis. The most important of these variables is brain size, but other factors are also clearly involved with regard to more subtle features, such as map topography. Failure to control for these factors inevitably leads to their being confounded with descent relationships despite the fact that cladistic analysis itself does not prejudge the primitive or advanced status of a trait. The differential expression of a large number of traits with respect to brain size or the differential expression of traits in brains with respect to sensory specializations can be a serious problem for cladistic analyses because it 645 vastly increases the probability of convergence and parallelism. In fact, many of the "advanced" traits of eutherian mammal brains could have been inherited from the common ancestor of eutherian mammals even if that ancestorfailed to exhibit any of these traits. All three mammalian groups have likely inherited neural developmental constraints and tendencies from a common ancestor that are expressed differentially in different contexts. It is possible that the "initial" eutherian brain possessed the developmental information for these traits but failed to express them because certain other conditions of their expression were not met. Below it will be argued that the small size of these brains precludes the developmental expression of cortical parcellation processes necessary to produce multiple highly differentiated cortical areas. Also the regression of the visual system in basal insectivores may further undermine these processes. Despite the fact that ancestral traits might not be expressed in an intervening lineage there need be no interruption of their descent to subsequent lineages, and their disappearance or reappearance in certain lineages cannot be attributed to distinct mutation events and treated as distinct derived conditions. We should be especially wary of this possibility in the choice of traits included for cladistic analysis. This again underscores the importance of thinking of homologies in informational terms. Even if we were to miraculously learn the details of morphology of the brain of the true eutherian ancestor we would perhaps still be a long way from understanding the initial conditions embodied in that initial brain that still influence the structure and the evolution of modern brains. What we ultimately want to know is what information was embodied in the initial brain and in the developmental mechanisms that built it. Recapitulationism During embryogenesis there is a definite progression from smaller poorly differentiated structures to larger highly differentiated structures. Large species with rel- 646 TERRENCE W. DEACON atively differentiated brains must inevitably pass through developmental stages in which their brains are small and poorly differentiated. Consequently, the embryonic stages of larger more differentiated brains will inevitably bear some superficial resemblance to the adult stage of small poorly differentiated brains. Structures that differentiate later in development will thus appear to be added to an otherwise common substrate. Recapitulation assumes that adult structures in primitive species are homologous to embryonic structures in advanced species. Subsequent modifications of brain structure have been added in the more "advanced" species by extending the ontogenetic process to include additional later stages. This process of terminal addition was presumed to link the evolution of species to development in such a way that a scale of increasing complexity in evolution was the inevitable outcome of a progressive increase of developmental information. Evolution is explained as an augmentation of ontogenesis and more "advanced" species are literally assumed to be further developed. Despite the enormous theoretical power of this synthesis, the weight of comparative and ontogenetic evidence that has accumulated against this doctrine in the last century is overwhelming. Ontogenetic differences that distinguish different lineages may occur at any stage of development and are clearly not constrained to occur in linear sequence with increasing phylogenetic divergence. Although larger creatures tend to exhibit longer ontogenesis than smaller creatures, larger creatures do not have additional stages added on and highly derived members of a lineage do not show more developmental stages than highly conservative members; differences in development appear at many corresponding stages. However, despite the patent failure of this paradigm, numerous unrecognized recapitulationist assumptions still persist in the literature about brain evolution, primarily in the form of tacit terminal addition assumptions. Early recapitulationist theories of brain development suggested that as children matured they passed through stages of cognitive and emotional development that corresponded to distinct "grades" of animal consciousness from reptilian to mammalian, from primitive mammal to primate, from primate to primitive human, and finally through the ascending stages from "primitive savagery" to modern civilization (e.g., Spencer, 1870; Baldwin, 1895). Various primitive human societies and criminals were viewed as arrested at some prior stage of development. Although the recapitulational structure of the brain was assumed by many prominent 19th century and early 20th century neurologists (e.g., Paul Broca, John Hughlings Jackson, Ivan Pavlov, John Sherrington, and Sigmund Freud) probably the major catalyst for the formation of a comparative anatomical version of this theory came as a result of the synthesis of two sources of neuroanatomical evidence just subsequent to the turn of the 20th century. Paul Flechsig's (1901) analysis of myelinstained tissue from human fetuses at various stages of development demonstrated a progression of myelination of isocortical areas that began with primary sensory and motor areas, continued to belt zones around these areas and culminated in relatively late myelinating association areas (see Fig. 4). This sequence was presumed to correlate with developmental trends in which basic sensory-motor abilities mature early in childhood and the "highest" intellectual abilities only appear late in development. It was also presumed to correlate with an evolutionary sequence from species with only crude sensory-motor habits and responses, to species with the capability of complex and flexible associational learning abilities. The most developed associated areas were assumed to be the newest in evolutionary terms and the last to develop in ontogeny. At roughly the same time comparative anatomists, using preparations that visualized neuron cell bodies, produced maps of the distinguishable cytoarchitectonic areas of the cerebral cortex for a number of mammalian species which appeared to demonstrate homologues for primary pro- RETHINKING MAMMALIAN BRAIN EVOLUTION 647 jection areas in all species, but no homolateral view logues for many human association areas in monkeys and no homologues for many monkey association areas in other mammals (e.g., Campbell, 1905; Brodmann, 1909). It clearly appeared as though the developmental trend recapitulated a sequence of additions of new cortical areas leading up to the human brain. New, more developed association areas were apparently added to the brain of succeeding species at the end of their maturational development. The unusually long postnatal brain development of humans could also be explained on the basis of the extra stages of brain development that were appended to human ontogeny. These assumptions corresponded with the neuropsychological doctrine of the time. It was assumed that the moment to moment processing of sensory input retraced this same hierarchy, developing from the crude registration of sensory information in projection areas, to the construction of a perceptual gestalt in sensory psychic areas, and finally to the elaboration of multimodal associations with respect to different remembered perceptions, actions and emotional experiences in association medial view areas. Primitive animals and young chilFIG. 4. Flechsig's myelogenesis figure of the human dren only progressed through the initial brain is redrawn from the original leaving out Flechstages of this cognitive hierarchy. Although sig's numerical designation of myeloarchitectonic the developmental recapitulation assump- fields. The darker areas represent the areas that myetion has been abandoned, a more elaborate linate earliest in development and the white areas version of this basic hierarchic functional represent the areas that myelinate latest. Insular coris partially exposed. Note the progression from interpretation remains the dominant con- tex primary to secondary to association areas of cortex. temporary theory of sensory processing Note also the early myelination of limbic cortical areas (e.g., Mishkin and Appenzeller, 1987; and pathways. Maunsell and Van Essen, 1983), despite many growing inconsistencies and the availability of alternative interpretations (e.g., Brown, 1988; Deacon, 1989a; Dia- will primarily address the issue of the mond, 1982; Optican and Richmond, developmental sequence, but will return to 1987). the issue of the terminal addition of corHowever, in hindsight we can see fun- tical areas in a later section. damental flaws in both forms of evidence An alternative explanation for the prifor this correlation of ontogenetic devel- mary-secondary-tertiary hierarchy of myopment with apparent phylogenetic devel- elination of cortical areas can be derived opment. Ultimately I will argue that both from a correlation between the total level derived from a failure to control for factors of adult myelination achieved by these having more to do with brain size than with structures and their apparent developphylogenetic progression. In this section I mental schedule. The early myelinating 648 TERRENCE W. DEACON areas are also the most heavily myelinated in the adult and the latest myelinating areas are the least myelinated in the adult (Bishop, 1959; Sanides, 1970). This rule also applies to noncortical structures: the relatively poorly myelinated reticular formation of the midbrain exhibits one of the latest and longest cycles of myelination of any system (Yakovlev and Lecours, 1967) despite the fact that it is without doubt one of the most conserved structures in the vertebrate brain. This fact clearly contradicts the recapitulationist assumption. There are two possible interpretations of this correlation between total amount of myelination and the developmental time course of myelination. First, since these assessments of myelination are based on myelin-stained tissue sections in which more densely myelinated tissue stains darker, there may be a level of nearly complete staining opacity reached at an early developmental stage in areas that exhibit maximal myelination whereas this level of staining opacity may never be achieved by very poorly myelinated areas. In this case the apparent heterochrony could be largely illusory. Alternatively, the deposit of myelin on axons destined to be heavily myelinated could take place at an absolutely faster rate (which appears to be indicated by the data of Yakovlev and Lecours, 1967) and could begin earlier. It is probably an important corollary that the most heavily myelinated fibers are also often the largest diameter axons that project relatively long distances or to highly specific targets. There is also an important correlation between brain size and primitive-advanced comparisons. Small insectivore and rodent brains appear to be generally less myelinated overall and exhibit less myeloarchitectonic differentiation from cortical area to cortical area than the larger brains of primates (Sanides, 1970). Therefore if myelination is to be used as a ruler of progression then the least myelinated areas should be considered the more primitive areas and the most myelinated areas the more advanced cortical areas (Bishop, 1959). This runs exactly counter to the apparent developmental progression. This apparent phyletic myeloarchitec- tonic trend as well as other architectonic trends led Sanides (1969, 1970, 1972) to propose that the terminal addition of cortical areas in mammalian evolution might actually be the reverse of that proposed by the traditional theorists—progressing from association areas to secondary areas to primary areas as the most highly developed. Although not a recapitulationist theory, because it makes no claims for development, it does nonetheless assume terminal addition in a phylogenetic sense, and an implicit progression from primitive to advanced forms based upon the addition of more highly differentiated structures. The neuropsychologist Jason Brown (1977, 1988) has elaborated a self-consciously recapitulationist theory of cortical function that is based upon Sanides' model of phylogenetic progression, in which "microgenetic" processes of sensory analysis proceed in a series of stages from limbic to association to specialized cortical areas rather than the other way around, as is suggested in traditional models. Both interpretations are based on misleading correlations that confound a number of factors. The correlation between the density of myelination in adults, the initiation and rate of myelination in development, and the overall level of myelination in brains of different sizes suggests that all of these apparent comparative trends may have less to do with any evolutionary sequence and more to do with certain conservative functional and metabolic relationships between axons of differing sizes and the glia that form their myelin sheaths. The developmental differentiation of the brain occurs in the process of increasing the size of the brain. The evolution of increasingly differentiated mammal brains also correlates with the evolution of increasing brain size. The similarities between brain development and brain evolution may largely be the result of these parallelisms. In both, the confusing role of brain size and its many correlative structural scaling relationships underlies many of the misidentifications of evolutionary progress. The nature of some of these underlying correlations will be the subject of the next section. RETHINKING MAMMALIAN BRAIN EVOLUTION BRAIN SIZE The influence of anthropocentrism The significance of brain size is at once the most broadly debated issue in the study of brain evolution and probably also its most ubiquitous, misunderstood and troubling feature. More has been written about brain size than about any other topic concerning brain evolution. Like the notion of evolutionary progress, interest in brain size owes much to its apparent importance for understanding human brain evolution. With a brain roughly three times larger than a primate of our size should possess, it is natural to assume that brain enlargement must hold the key to human uniqueness. But is brain enlargement symptom or cause in this transformation? Is relative brain size alone the significant difference or is it a superficial consequence of more fundamental changes in brain organization? Size is also the easiest feature of the brain to study and so lends itself to broad comparative studies and studies of other possible correlates to brain size that might be of interest to researchers who otherwise have little neuroscientific training. But the tendency to terminate the analysis of brain difference with measurements of brain size is a significant impediment to progress in the investigation of brain evolution, precisely because issues of brain size are inseparable from issues of function and internal organization in some very fundamental ways. 649 the largest mammals on earth. If we think of the brain as a computer and the body as the many users that are on line, vying for processing time and memory storage, then it becomes obvious that the effective information processing capacity available for any particular function is constrained by the number of competing demands from other sources. This has suggested to many that the ratio of brain to body is a more significant measure of available information processing capacity. Despite the fact that they have absolutely larger brains, elephants and whales have a much lower ratio of brain to body than humans. But this observation is also unsatisfactory. Very small birds and mammals have a higher ratio of brain to body size than humans and an even higher ratio of neuron number to body size. A satisfactory account of comparative brain size that ranked humans on top (and thereby preserved the intuition underlying the Anthropocentric Maxim) was discovered at the end of the 19th century in the form of allometric analysis. Since that time the use of "subtraction criteria" based on empirical brain and body size trends have been assumed to define that portion of the brain mass that is functionally correlated with information processing demands of the body. Deviations from these trends have been assumed to respectively indicate excess or dearth of mental capacity (but see criticisms below). The importance of allometric analysis for the investigation of patterns of relative growth and the secondary correlates of differences in size is paramount, not just as a criterion of subtraction, but as a tool for drawing attention to the ways that biological processes can be affected by changes of scale. Allometric analysis has produced some remarkable insights into the problem of relative growth and has demonstrated some remarkably regular patterns of size-related brain variation from species to species and from evolutionary epoch to epoch. The role of brain size in brain evolution appears deceptively simple. If the brain is a computing device of some kind, then an increase in component processing elements should correlate with an increase in information processing capacity. This intuition appears to be borne out by the unsystematic observation that species with brains at the small end of the size range for vertebrates exhibit mostly simple and stereotypic behaviors compared to those with brains toward the large end of the spectrum. This observation is not totally satThe more detailed pursuit of allometric isfactory. Some mammal brains exceed the relationships has shown that differences of human brain in total volume and neuron brain size have consequences at every level number—for example, elephant and whale of neuroanatomical and neurophysiologibrains. However, these species represent cal organization. Not all features of the 650 TERRENCE W. DEACON brain scale isometrically with size changes in brain evolution. This has serious functional consequences that have yet to be appreciated—much less understood—and serious methodological consequences for comparative studies because it immensely complicates the task of determining homology. A failure to appreciate the numerous architectonic and functional consequences of differences in brain size lies at the heart of numerous misunderstandings about brain evolution, including issues of progression and the question of brain size evolution itself. Is there a trend toward increasing encephalization? One of the earliest discoveries concerning brain evolution in mammals was that mean brain size and relative brain size have increased with respect to our reptilian ancestors and have increased since the beginning of the great eutherian mammal radiations. This seems to be a clearly progressive trend and suggests that brain size itself may be an adaptation under selection. But is it? If it is, then is there selection for total brain size or for relative brain size? And what kind of evidence would be necessary to demonstrate one or the other? The accepted answers to these questions have been phrased in terms of the evolution of intelligence, and have changed little in form since well before the turn of the century. Brain enlargement, both in absolute and relative terms, has typically been referred to as encephalization. Beginning with the work of Dubois (1913) the term became associated with a specific mathematical index: the measure of the relative deviation of the ratio of brain to body size in a species from the expected ratio for an average animal of the same body size, based on trends for a given taxonomic group or for some baseline comparison group (Bonin, 1937; Dubois, 1913; Gould, 1966; Jerison, 1973; Stephan, 1969). Two major applications of these measures of encephalization are the assessment of taxonomic and phylogenetic differences of relative brain size and the assessment of species differences in intelligence. Whether or not there is any meaningful relationship between rel- ative brain size and intelligence is open to serious question. There are distinctly different brain-body trends for different mammalian taxa that can all provide equally valid—but not concordant—measures of encephalization for an individual, the somatic fraction of brain size that is presumed to be allometrically "subtracted" in the estimation of encephalization cannot be a simple linear factor as is often assumed, and deviations from the empirical trend cannot automatically be assumed to correlate with mental adaptation as opposed to metabolic, ontogenetic or somatic adaptations (Deacon, 1990a, b). More importantly, the assumption that intelligence— much less comparative intelligence—is a single measurable scalar quantity is highly dubious from either a neurological or an evolutionary perspective and has never been adequately supported by comparative intelligence testing (MacPhail, 1982; Gardner, 1983; Hodos, 1988; Deacon, 1990a). This issue has been extensively discussed and debated elsewhere and so will not be reviewed here, except to point out both the obvious progressionist and anthropocentric assumptions that are inextricably bound up with the entire enterprise. Only the issues surrounding phylogenetic interpretations will be considered here. Since the appearance of the first vertebrates, brains and bodies in many lineages have enlarged by orders of magnitude. The relative sizes of brains and bodies have also changed. For an animal of a given body size the ratio of brain to body size has also increased in a number of lineages, though not all. These general trends are capped by the evolution of mammal brains. The largest brains ever to have existed are now possessed by whales and the most extreme values of encephalization ever to have existed are now exhibited by dolphins and primates—particularly humans. Within any taxonomic group of living vertebrates there is a negative allometric relationship between brain size and body size, such that adult individuals with larger body sizes tend to exhibit lower ratios of brain to body size than smaller individuals. The scaling relationship is approximately linear when rendered in logarithmic coordinates but the particular slope and y-intercept of this line RETHINKING MAMMALIAN BRAIN EVOLUTION 5.0 3.0 r B 4.0 -s f 2.0 -9> 3.0 CD .£ 2.0 co .a en 1.0 o domestic dog breeds CO 1.0 o E = encephalization S = somatization polygons for four vertebrae classes from Jerison (1973) i 0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 2.0 log body weight approximate t i m e d birth O i 3.0 i i 4.0 i 5.0 i 6.0 log body weight L . postnatal brain & body growth g '2 i 'CD fetal brain & body growth range of adult sizes for a large species C CO .Q D) O range of adult sizes for a small species log body weight log body weight FIG. 5. Allometric patterns of comparative vertebrate brain size and mammalian brain growth. Graph A, on the upper left, depicts convex polygons that enclose all points of brain and body size for four major vertebrate classes (from Jerison, 1973) along with an approximation of the polygon that would have enclosed the stem mammals. Note the incredible mammalian brain and body size expansion from this precursor group. Note also the overlap of teleost fishes and lizards and of birds and mammals and the lack of a scala naturae trend from fish to mammals. Graph B, on the upper right, shows the trend line for carnivores and for the domestic dog breeds. Arrows indicate measures of encephalization or somatization for a small dog with respect to the carnivore trend. This is intended to show that "encephalization" differences do not necessarily imply selection on brain traits. Graph C, on the bottom left, shows an ontogenetic developmental trajectory for brain and body growth in two typical mammals of different body size. The prenatal phase overlaps completely for most species. Graph D, on the bottom right, shows ontogenetic curves for different mammalian species and their relation to interspecific trends. Note that ontogenetic lines overlap for different size species during the early ontogenetic phase. The shift of ontogenetic curves that distinguishes primates, cetaceans and elephants from other mammals is shown in gray. differs depending on the taxonomic group under consideration. Proposals for explaining the regularity and slope of these trends include the possibility that brain size tracks body surface (Snell, 1891; Dubois, 1913; Jerison, 1973), that brain size is constrained by metabolic capacity which is also negatively allometric (Martin, 1981; Armstrong, 1983), that because brain is derived from embryonic ectodermal tissue, the same growth control mechanisms may control mitosis in brain and body surface structures (Deacon, 19906), or that target body size is controlled by neuron number via a mechanism common to all mammals, and possibly all vertebrates (Deacon, 1990(5). Ultimately no single explanation can account for the substantial differences in scaling relationship exhibited at different taxonomic levels of analysis. Within a species the allometry is strongly negative with slope on the order of 0.1 to 0.2, whereas within a whole order or whole vertebrate class the allometric slope is often in the range of 0.6 to 0.8. See Figure 5a and b. The brain-body allometries of the different living vertebrate classes appear to 652 TERRENCE W. DEACON be distributed bimodally. The homeothermic classes—birds and mammals— tend to scale together and the poikilothermic classes—fish, amphibians and reptiles—tend to scale together, with homeotherms exhibiting a much larger percentage of brain to body at any given body size (see Fig. 5a). In the evolution of birds and mammals there has clearly been an increase in encephalization over the ancestral reptilian condition as represented by modern reptiles. But using modern species as exemplars for ancestral relationships it cannot be said that there has been a steady increase in encephalization from fish to amphibians to reptiles to birds and mammals. In fact, some of the most "primitive" fishes, the sharks and rays, comprising the Chondryichthes, exhibit levels of encephalization that exceed all other fishes, amphibians and reptiles, and overlap the ranges for birds and mammals (Bauchot et al., 1979). There is no clear scala naturae of encephalization. In contrast, relative conservatism of the encephalization relationship is demonstrated by the extensive overlap of encephalization in different vertebrate classes. Discerning the progressive encephalization of mammals relative to their reptilian ancestors involves more subtle distinctions, but the major step across this gap appears to have already been taken by the time of the common ancestor of metatherians and eutherians (Ulinksi, 1986). In comparison with other mammalian lineages the marsupials and the insectivores appear to occupy the low end of encephalization, but the monotremes appear on a par with the mean for eutherian mammals. If the small brained basal insectivores and marsupials are characteristic in this regard of most stem mammalian groups then most other mammalian lineages have exhibited progressive encephalization. Such a trend is demonstrated in the fossil record and has been used as support for the claim that there has been a progressive trend toward increased intelligence in all these lineages (Jerison, 1973). The relatively lower encephalization of many small bodied forms including basal insectivores and rodents, may reflect ges- tational trade-offs involving large litter sizes and rapid reproductive rates characteristic of small r-selected species. These reproductive specializations have been correlated with gestational constraints that affect brain growth (Martin, 1983; Deacon, 1990ft). Similar factors may also be important for understanding marsupial brain size development. It is possible that low estimates of "basal" encephalization may be misleading if they incorporate superimposed developmental trade-offs that secondarily reduce encephalization. We should expect that progressive removal of these constraints in lineages radiating into K-selected, larger-body-size niches might result in an apparent "rebound" of encephalization. A similar "rebound" effect has been suggested by Gould (1975) and Deacon (1990ft) in response to intense selection for increasing body size in evolution. Breeding experiments demonstrate that selection on body size produces body size increase in successive generations with little correlated increase in brain size (Atchley, 1984; Riskae/a/., 1984). Deacon (1990ft) argues that after an initial rapid evolution of increased body size effected by modifications of peripheral hormonal mechanisms, continued stabilizing selection would tend to produce complementary brain size increase as a more stable central determiner of target body size. Thus the rapid radiations into more K-selected, large-body-size niches that characterized many mammalian lineages in the Eocene may have provided a biased sample of species for comparison to more modern lineages. The case for an increase in encephalization in primates, elephants and cetaceans is strengthened by independent evidence. From very early in embryogenesis all these species exhibit approximately double the ratio of brain to body size found in any other mammal group at a comparable developmental stage (Count, 1947; Sacher and Staffeldt, 1974; Martin, 1983; Deacon, 1990ft). This difference is evident at the earliest stages in which brains are discernable in the developing embryo and so rules out explaining this encephalization in terms of the terminal addition of neural tissue RETHINKING MAMMALIAN BRAIN EVOLUTION 653 late in development (see Fig. 5c, d). Curi- Body size is a highly flexible and ecologiously, the trajectory of brain-body growth cally significant variable, whereas brain size for all three groups closely overlaps for the is a relatively inflexible and as yet poorly entire fetal period, suggesting a common understood variable that may or may not brain growth mechanism. The brain-body correlate with differences in behavior. The growth trajectories of the remainder of the search for changes in brain structure that eutherian mammals also all appear to share correlate with addition or subtraction of a common fetal trajectory, suggesting that neural tissue in evolution in order to they all share a different common mech- account for encephalization is for this reaanism for determining brain growth. As son probably misguided; we might just as with the encephalization difference well look for addition or subtraction of the between homeotherms and poikilotherms, many parts of the rest of the body. this embryological encephalization difference distinguishing primates, dolphins and Disentangling allometry and progression elephants from the remainder of the When organisms get larger, either dureutherian mammals appears as a distinct ing development or in the course of evodiscontinuity without intermediates. lution, all features of the organism are not As a final comment on the encephaliza- scaled up isometrically. Even more troution issue it should be pointed out that the blesome for the comparative anatomist is term itself reflects an underlying bias that the fact that homological relationships can is part historical and part theoretical. Early appear to change with evolutionary changes writers often did not clearly distinguish in size. Different structures may radically absolute brain size from the relation of change size with respect to one another or brain size to body size in their discussions alter their relative position, structures may of brain evolution (Gould, 1981). The ulti- radically change shape due to unequal mate interest has of course all along been growth rates among their parts or single the explanation of human brain size. But structures may divide or differentiate to measures of brain size with respect to body become two or more distinct structures. size are inherently relational. Increased This is the source of one of the most insidencephalization is also decreased somati- ious problems in evolutionary theory: the zation, and vice versa (see Fig. 5b). One confusion of size related changes with evoneed not necessarily assume neurological lutionary advancement. The secondary explanations for differences in this rela- effects of change of size can include the tionship. Although breeds of dogs differ apparent addition of new structures to old enormously in degree of encephalization or the appearance of increased complexity (from small highly encephalized dogs to in existing structures. It may be difficult to large poorly encephalized dogs) no one tell whether an increase in size has caused doubts that body size is the selected vari- old structures to differentiate and subdiable and brain size the relatively less flex- vide or whether the addition of new strucible parameter. In fact, breeding experi- tures has caused an increase in size. ments selecting progeny on the basis of As D'Arcy Thompson pointed out in his either extremes of brain size or extremes classic treatise on the effects of growth on of body size demonstrate that selecting for form (1917), most mechanical forces, body size produces a poor correlated material properties and structural relationresponse in brain size whereas selecting for ships do not change isometrically with brain size produces a highly correlated changes in size. Geometric effects are most response in body size (Roderick et ai, 1976; obvious—e.g., surface area to volume—but Fuller, 1979; Atchley, 1984; Riska et al., also there are changes in the relative vis1984; Kruska, 1987). Given that a large cosity of fluids, diffusion rates of molefraction of the variance in "encephaliza- cules, structural plasticity or rigidity of tion" within a species can be a consequence materials, rates of chemical reactions, etc. of selection on body size, why should this Many of these non-geometric scaling allonot also hold for cross-taxa comparisons? metries result from the fact that ultimately 654 TERRENCE W. DEACON some components of organisms are of fixed sizes (e.g., molecules and cells). In order to maintain isometry of functional properties across major changes in size it is nearly always necessary for structures to enlarge at different rates. Allometric analysis can help control for the influence of size, allowing one to compare quantitative traits with the effects of size subtracted. This "criterion of subtraction" is most often assumed to indicate that some functional relation has been maintained in the face of the change in size. This does not mean that such changes are merely passive effects induced by the mass of the organism. They are inevitably "internal" facultative or genetic adaptations to the imbalances or weaknesses induced by the change in size. Many of these secondary adaptations may be encoded in the genome of the organism. However, a trait that is somehow expressed facultatively, in a size graded manner, would have obvious advantages over one that is specific to a given range of sizes. In these cases size change should be considered the "primary" adaptation and the correlated reorganizations of structure can be considered "secondary." A genetically encoded, size-correlated trait that has evolved in response to the functional demands of size change is a paradigmatic example of a derived condition. But it also represents a conservative feature to the extent that it is necessary to preserve some ancestral functional relationship. Functional homology is maintained at the expense of structural homology. The reverse scenario is also possible— structural homology maintained by virtue of change in supportive functions to keep pace with the effects of size. Finally, it is also possible for size change to be the "secondary" adaptation. A change in size can be secondary to the production of some correlated effect that has itself become the trait of primary adaptive significance. This latter possibility will be suggested in the case of human brain size enlargement (see last section). In cases where size change is primary it would be inappropriate to consider the many secondary adaptations as progressive trends. The fossil evidence clearly demonstrates that with the demise of the dinosaurs, small mammal species rapidly adapted to fill niches for large bodied forms. The mammalian radiations can be seen as markedly asymmetric with respect to body size (and correlated brain size). The lower limit of mammalian body size has probably not been significantly altered since the Paleocene but the upper limit has probably been extended about a millionfold compared to a typical basal insectivore! Brain size necessarily followed this trend, although the extension of the upper limit of brain size, due to its negative allometry with respect to body size, has probably not exceeded ten thousand times that of a typical basal insectivore brain. Parallel enlargement trends characterized numerous lineages of mammals. I will argue that it is this remarkable parallelism, and not some progressive selection for increasing intelligence, that is responsible for many pseudoprogressive trends in mammalian brain evolution. Larger whole animals were being selected—not just larger brains—but along with the correlated brain enlargement in each lineage a multitude of parallel secondary internal adaptations followed. Allometry of brain traits at many levels Mammalian brains range in weight from around a gram to nearly ten thousand grams. Even within a single lineage like the anthropoid primates, in which individual species share many strong similarities in brain structure, there is more than a hundredfold difference between the smallest and largest adult brains. Yet most microstructural features change little in size from brain to brain. The maximum sizes of neuron and glial cell bodies increase slightly from the smallest to the largest brains, but nowhere near the thousandfold scaling of the brains they comprise (Haug, 1987). The functional constraints on cell volume no doubt set an asymptotic upper bound on cell size that the largest neurons are likely approaching. The apparent tetraploidy of the giant Betz cells of the human motor cortex likely indicates that these cells are already forced to come up with unusual RETHINKING MAMMALIAN BRAIN EVOLUTION ways to circumvent certain functional limitations of their large size. The constraints on neural size also affect the scaling of higher-order multi-neuronal structures. For example, the diameter of cortical columns as well as the number of neurons within each column seems to remain almost constant across brain size variation (Rocket et ai, 1980). This may also be the reason that many larger scale morphological features like cortical thickness increase only slightly from the smallest to the largest brains (Rocket et ai, 1980). As a consequence the disparity between microstructure organization and macrostructure organization grows incredibly with increasing size. Since the macromorphology of the brain, including distinct homogeneous structures and their various functional subdivisions, is derived by ontogenetic processes that function at the microscopic cellular level, this growing scale difference is inevitably reflected by changes of large scale structure. Some of these morphological changes may reflect distinct adaptations to these new microstructural demands, but it is likely that the majority are simply the inadvertent consequences of the same ontogenetic processes operating in a vastly larger brain. 655 projection neyon ce!;body local circuit neuron (e g granule cell) cytoarchitectonic and myeloarchitectonic allom- FIG. 6. Local relationships contributing to cytoarchitectonic allometry are depicted in this figure from Deacon (1990i). The top figure depicts the situation in a relatively small brain and the lower figure depicts these same relationships in a slightly larger brain. In small as compared to large brains projection neurons possess short, small diameter axons, with relatively less myelin, smaller cell bodies, lower neuron to glia ratio, higher neuron densities, lower ratios of local circuit neurons to projection neurons, smaller size differences between the smallest and largest neurons, etc. etry (Deacon, 1990a). The linear and volumetric increase in scale of the brain with respect to relatively more conservative limits of cellular structure impose new constraints on neural and glial functions. Consider what must happen to homologous long projection neurons in brains of increasing size. In order to maintain similar transmission velocities and transmission integrity longer axons need to have larger diameters and thicker myelin sheaths. If the target area has also expanded in volume (as is typically the case as well) then the terminal arbor of a typical axon must also increase. This can be a significant factor given the exponential differential between linear dimensions and volumes since it requires a tremendous increase in length and number of branchings of an axon to fill a larger volume with the same density of synapses. To keep pace with the metabolic and neurotransmission demands of such an enlarged axonal volume and surface area the cell body of the neuron must also be enlarged, but since it depends on the glial cells surrounding it for its metabolic support the relative number of glia must also increase. The same constraints are not experienced by small local circuit neurons. Since local circuits remain relatively constant in volume (increasing no more than a few hundred percent across huge brain size differences) these neurons should have to change relatively little to compensate for size (see Fig. 6). Given these two extremes, it becomes obvious that the local cyto- and myeloarchitecture of many brain structures will reflect the influence of size. In general, in large brains as compared to small brains This is illustrated by what can be called 656 TERRENCE W. DEACON there should be a number of regular trends: In large brains there should be (1) some much larger cell types, but also a much greater difference between the smallest and largest cells, (2) a higher glia to neuron ratio in most regions, (3) a decreased mean density of neurons but an increase in the range and variation of densities in different areas and subareas, (4) a significant increase in axonal and dendritic arborization to fill the slightly increased volume of local circuits, (5) higher levels of myelination in general but a greater difference from the most to least myelinated areas and subareas, and (6) since some areas may be specialized for longer projecting cells with large soma and heavily myelinated axons and other areas only for short projecting cells or cells with small diameter, poorly myelinated axons these differences will be magnified between brain areas. The net result will be greater architectonic differentiation within an area and between areas in large brains as compared to small brains. Few if any of these changes are likely the result of the evolution of new ontogenetic mechanisms, but merely the local dynamical responses of cells trying desperately to do what they would do in any brain. We can conclude from this that an increase in of a network might be the average number of nodes that must be passed through to find a link between any two arbitrary nodes. In all but the smallest or lowest connectivity networks the number of connections tends to vastly outnumber the nodes. If one wants to increase the number of nodes in a network while maintaining the same average connectivity, then the number of connections that have to be added with each new node will grow factorially with each addition. A factorial increase of this sort will rapidly lead to astronomical numbers, particularly in large highly connected networks, but even in networks with relatively low connectivity very large changes in size will produce the same result. Except in minimally connected networks, it will become increasingly difficult to continue increasing the number of nodes within a network and retain the same level of connectivity. This concept in diagrammed in Figure 7. Now consider these facts about the central nervous system: One neuron may be connected to thousands of others, according to some estimates; even in small mammalian brains there are probably billions of neurons; and there is a nearly tenthousandfold difference in volume between the architectonic complexity with size is not a reli- smallest and largest mammalian brains. able measure of progression and advancement, These simple statistics make it clear that either for comparison of the brains of different network allometry must be one of the major species or for comparisons of different areas factors responsible for the differences in organization and function that distinguish within the same brain. However, there are also a number of size large and small brains. But the brain is not related changes for which architectonic a maximally interconnected network. Even reorganization will be unable to compen- in a brain with a billion cells each consate. Probably the most significant among nected to a thousand others at random the these is a factor that can be called network average number of nodes separating any allometry (Deacon, 1990a). Network allom- two will be on the order of twenty, and etry is essentially a geometric principle brains are not nearly so diffusely orgaanalogous to surface to volume allometry. nized. Probably the majority of connecThe size of a network is a function both of tions between areas of mammalian isocorthe number of nodes in the network and tex and other cortical or subcortical areas the number of connections between nodes. are reciprocal, and the connections within Networks with every node directly con- the local circuits of the isocortex are probnected to each other node can be consid- ably highly re-entrant and relatively selfered to exhibit a high level of connectivity contained within columnar modules. Also, whereas networks with each node directly given the relative comparability of local connected to only one or two others can circuit structure of isocortex from species be considered low connectivity networks. to species, it is likely that intracortical conOne measure of the average connectivity nections between columns may be limited 657 RETHINKING MAMMALIAN BRAIN EVOLUTION to neighboring columns and to specific target columns in other areas. It may then be a bit more useful to think of connectivity within the cerebral cortex in terms of columnar modules as nodes rather than in terms of individual neurons as nodes. A separate network allometry might apply at the columnar level since columns increase in volume, number of axons and dendrites but not neurons with increasing brain size. Nonetheless, given the immense differences in scale that must be considered, even a relatively poorly interconnected cortical network will have to contend with connectivity trade-offs in order to compensate for network allometry. There is clearly a significant increase in the proportion of white matter to gray matter in brains of ascending size, but this is doubtless nowhere near what would be required for connectional parity to be network allometry maintaining local connectivity only network aQometry maintaining global connectivity o maintained. In order to evolve to significantly larger sizes brains must decrease connectivity. This trade-off undoubtedly has its costs. The two most obvious costs of decreasing connectivity with increasing size are reduced integration of distributed functions and significantly increased transmission and processing times. Larger brains are not necessarily more efficient and more powerful than smaller brains. In fact, these new functional costs of increasing size will demand new secondary adaptations in order to compensate in other ways. If a functional area of cortex becomes enlarged in the course of evolution the mean interconnectivity of its columns will decrease. This will decrease the homogeneity and integrity of activity patterns that can be maintained within it and increase the time necessary for neural "calculations" involving the whole area to be completed. These costs can be minimized by breaking the one large area into two relatively independent subareas capable of processing the same information in parallel, so long as they can be partially integrated with one another by specific interconnections. Although this reorganizational strategy may compensate in part for loss of local integration and processing efficiency it cannot entirely compensate. And, if size continues to increase, additional parcellations into multiple areas FIG. 7. The problem of network allometry is represented by the example of a very simple network (figure from Deacon, 19906). A series of nodes (depicted as spheres) is connected reciprocally to each other (depicted by double arrows) in different size networks with different extremes of connectivity. Networks on the left exhibit low connectivity and those on the right exhibit maximum connectivity. N = total number of nodes; C = total number of reciprocal connections (note that in the nervous system reciprocal connections are separate connections); Xn = the number of other nodes to which any one node is directly connected; Xc = the mean number of connections intervening between any two arbitrary nodes. The growth of connections to nodes is a factorial function of the number of nodes in a fully connected network and a linear function of the number of nodes in a minimally connected network. Both low and high connectivity networks require major functional and structural trade-offs with size increase. are necessary and both transmission time and integration costs will continue to mount. Of course there may also be advantages, including the increased specificity and reliability afforded by parallel redundant processing, or alternatively, the possibility of subspecialization of different subdivisions. The evolution of new structure and function as a result of such pro- 658 TERRENCE W. DEACON cesses will be discussed further in the next section. We can conclude that network allometry may force a variety of secondary reorganizations of cortical architecture, including parcellation and multiplication of functional areas, as brains enlarge during evolution. This essentially forces large brains to alter functional strategies for information processing from those effective in small brains. A further factor to be considered is the fact that the receptor systems projecting to these cortical areas are also enlarging along with body and brain size, although these probably exhibit a negative allometry with body size. This must also play a significant role in determining at what level of scale there will likely be breakup and parcellation of cortical areas. It is far from clear to what extent there is net gain due to new functionality and increased information storage or net loss of efficiency and integration due to increasing loss of connectivity as brain size increases. We will need a better understanding of this trade-off before we will be able to think clearly about the question of comparative intelligence and its relationship to brain-body allometry. Size and parallelism in mammalian cortical evolution These complex allometric considerations complicate the evolutionary interpretation of comparative brain morphology. It is not a simple matter to track morphological changes and assign them independent evolutionary causes. If in fact many "emergent" architectonic changes associated with brain size evolution are simply the effects of common underlying cellular mechanisms compensating for the effects of size, then it can be misleading to treat them as "new" features. The only difference in the information utilized in the ontogenetic process is difference in size information—in the form of larger axonal volumes, greater variance of metabolic demands for different cell types, etc. If we focus on the deep informational homologies rather than on the surface structural homologies it is clear that developmental mechanisms have not been altered, rather one of the contextual variables to which these mechanisms is sensitive has changed value. These changes might be referred to as secondary facultative adaptations to distinguish them from secondary adaptations that actually involve the evolution of new genetic and developmental information. There is neither progression nor addition in this sense, and the parallelisms that result are not properly thought of as parallel homoplasy. It is possible that the ontogenetic mechanisms utilized in parcellation of cortical areas are sensitive to the demands of network allometry. The fact that axonal competition plays the major role in determining area parcellation and afferent and efferent relationships within the developing cortex (discussed in the next two sections) suggests that facultative mechanisms may account for a considerable portion of the structural and connectional response to this functional demand. But it is likely that specific genetic adaptations also become available to streamline the facultative response (via genetic assimilation) to these demands during the course of evolution. The addition of new secondary adaptations (derived conditions) is in order to maintain the same function (conserved condition), and can be seen as a sort of "Red Queen effect" to the extent that the system is working harder and harder to try and stay in the same place. The parallelisms that have evolved to maintain functional homology across a large range of brain sizes are likely the result of a combination of underlying ontogenetic homologies shared by all mammals and specific genetically encoded biases that modify the responses of these ontogenetic mechanisms differently in different species. But it is not necessarily safe even to consider these microallometric changes as facultative adaptations with respect to size change. All that is demonstrated is a form of morphogenetic plasticity that is affected by size. In a related context, Smith-Gill (1983) distinguishes two general classes of developmental plasticity that clarify this point. The first he calls developmental conversion and the second he calls phenotypic modulation. In developmental conversion, RETHINKING MAMMALIAN BRAIN EVOLUTION environmental cues activate alternative genetic mechanisms that are expressed in the organism's development. These different genetic expressions may produce alternative morphs by activating or inhibiting growth processes affecting the structural development of certain tissues, by changing cell surface affinities or messengerreceptor site relationships in intercellular communication, or even by inducing regressive processes, such as programmed cell death. In phenotypic modulation environmental cues modulate but do not select among or alter genetic programs. This produces variation and adjustment of the expression of genetic information but not the selection of different alternative genetic programs. Smith-Gill notes that phenotypic modulation does not necessarily imply an adaptive response, ". . . adaptiveness of phenotypic modulation cannot be assumed unless specific genetic mechanisms can be demonstrated." It is unlikely that the ontogenetic responses of neural tissues to the influence of size are produced by specific genetic alternatives, since these would have to differ for each range of size and for each brain region. Rather, the facultative plasticity of neurons in response to the local effects of size must be a case of phenotypic modulation. We cannot necessarily assume that all aspects of this plasticity are adaptive, even in the broad sense of adaptive with respect to local metabolic and information processing demands. We can only assume that significantly maladaptive plastic responses will be strongly selected against. In conclusion, the evolution of mammals is clearly characterized by a trend toward increasing body size with a correlated increase in brain size, but it is unclear to what extent there has been additional independent selection for increased brain size and brain differentiation in different lineages. Even if there is not independent selection for brain size in a particular lineage, body size correlated increase in brain size can be expected to produce a series of architectonic and functional changes due to the plasticity of developmental processes at the neuronal-synaptic level. In general, with respect to brain structure, we should 659 question the assumption that size increase is caused by addition of new parts, since the plastic responses of neural development to size change inevitably produce differentiation and subdivision of existing structures at all supracellular levels of organization. Although the majority of architectonic trends in brain organization in mammalian lineages give the appearance of increasing differentiation and complexity, we cannot disambiguate this from the effects of local cellular plasticity and secondary facultative adaptation which cannot be considered progressive in any sense. However, precisely because plastic phenotypic modulation is not necessarily adaptational, it cannot be assumed that it will preserve any semblance of functional isometry. And even if it is adaptational in most cases it may fail to be so at the extremes of size, where otherwise predictable metabolic and information processing demands may significantly diverge from ancestral patterns. If such inadvertent departures from functional isometry contribute useful capabilities or potentialities they may contribute to directional trends in evolution. Alternatively, if the adaptability of facultative responses induces genetic assimilation of nongenetic phenotypic modulation mechanisms into genetically based developmental conversion mechanisms, the changes in response to size may produce irreversible evolutionary changes. In this way secondary adaptations to size may inadvertently provide the raw materials for the evolution of new functional systems. NEOGENESIS: T H E EMERGENCE OF NEW STRUCTURE The assumption that new adaptations require new structures In many ways the fundamental question that evolutionary theory purports to answer is how new species with novel structures and functions come into being in the course of time. If anything can be called evolutionary progress it is the creation of totally new adaptations, not just the augmentation of existing adaptations. New brain areas with distinct cellular architecture and con- 660 TERRENCE W. DEACON nectivity appear in some lineages but not others, and it is almost certain that over the course of mammalian brain evolution the number of discrete brain areas in the most complex brains has steadily increased. This has suggested to many that new adaptations and the augmentation of existing structures are accomplished by the addition of new structures to an already functioning brain. But new structure may also evolve by co-opting or reorganizing existing structures in some way. In this case the resulting structure may be radically different than its antecedent, and yet combine both novel attributes and pre-existing features at different levels of organization. How is it possible to distinguish between uniquely derived structural additions and previous structures that have become radically modified? The history of vertebrate evolution in general, including mammalian evolution, exhibits a trend toward diversification of adaptations. The invasion of new niches inevitably requires adaptation of perceptual, behavioral and cognitive processes to meet the new demands. To some extent these are acquired at the expense of modifying previous neural systems, trading one function for the other. But the acquisition of new abilities could also be achieved by addition of new functions to old with a corresponding elaboration of the brain. Each species is the culmination of a phylogenetic sequence of adaptive changes that leave their traces in the structure of its body and brain. There is a natural tendency to envision this as an accretionary process that progressively adds new structure to old. This impression is supported by the apparent increase in brain complexity that correlates with the scala naturae hierarchy of species leading from fish to mammals and from "primitive" mammals to "advanced" mammals. The definition of evolutionary progress is at every step completely dependent upon the identification of neogenesis. One outstanding difference that is presumed to distinguish primitive from advanced mammalian brains is an increasing number of architectonically and functionally distinguishable cortical areas. The increase in numbers of cortical areas has often been cited as an example of both neogenesis and of progressive evolution, and is presumed to correlate with the increased behavioral and cognitive abilities of advanced species. The acquisition of new functional abilities is also a central feature of human mental evolution. We conceive of ourselves as possessing all of the cognitive abilities of other species and then some. Particularly novel in the course of evolution are human linguistic abilities. In a behavioral sense this capability is clearly an addition—functional neogenesis. It is tempting to assume that the addition of such an unprecedented function necessarily implies the genesis of novel neurological structures. With respect to apparent neogenetic trends in the evolution of cortex in other mammals, the addition of human language areas would seem to be a most recent step in a long series of additions. In this context it is clear that the assumed ubiquity of neogenetic processes derives in part from its presumed importance for human mental evolution. Additive theories of cortical evolution The most well known theory of mammalian brain evolution is the triune brain hypothesis proposed by Paul MacLean (1970, 1973). It is an attempt to explain the difference between mammal brains and nonmammal brains and how this difference arose in the course of evolution. MacLean argues that the mammalian brain can be subdivided into three functionally and evolutionarily distinct regions. The first division, including the spinal cord, brain stem, midbrain, diencephalon, corpus striatum and olfactory apparatus are considered a core structure common to all terrestrial vertebrates. He calls this the reptilian brain or R-complex because, he argues, this comprises the entire brain in reptilian species. During mammalian evolution two additional structural levels are added in sequence: the paleomammalian brain, composed primarily of limbic cortex and its associated forebrain nuclei and connections, and the neomammalian brain composed of the neocortex. With the accretion of each of these systems in the course of evolution comes the emergence of new RETHINKING MAMMALIAN BRAIN EVOLUTION cognitive abilities and behaviors. With the paleomammalian brain come complex parental care, vocal communication, play, and the "higher" emotions of bonding and caring. With the neomammalian brain come higher order perceptual, motor and generally enhanced learning abilities that free the organism from reliance on fixed action patterns and simple template based perception. The scheme is hierarchic and accretive. The reptilian brain is whole and complete in and of itself (and presumably can reassert itself as an autonomous force in cases of high excitation or weakened control from higher brain systems, as might occur with brain damage), and therefore the addition of new systems does not require major reorganization, simply superimposition of new axons into the circuitry of" this otherwise complete system. The additional connections need merely play an inhibiting and modulatory role with respect to the pre-existing substrate. Although this brain model has become widespread in the popular literature and in some psychological and educational theories, its influence in comparative neuroanatomy is tenuous at best. The limbic cortical areas that presumably comprise the paleomammalian brain have been homologized to cortical structures in nonmammals by comparative anatomists since early in the century (e.g., Johnston, 1906; Crosby, 1917; Elliot-Smith, 1919; Dart, 1934; Abbie, 1940). In addition, more recent investigations have demonstrated that nonmammals also exhibit forebrain structures and connections that undoubtedly have homologues in mammalian neocortex (e.g., Ebbesson, 1980; Karten and Shimizu, 1989; Ulinski, 1983). It also provides a simplistic view of behavioral differences between mammals and nonmammals. It seriously underestimates the considerable perceptual, motor and learning abilities of nonmammalian species—particularly birds— and ignores the many elaborate social behaviors, modes of communication and parental care that have been observed in nonmammals. Nonetheless, the triune brain theory has enjoyed a wide audience in large part because it captures a central anthropocentric intuition: that terminal 661 addition of higher order brain structures must correlate with the appearance of increasingly sophisticated cognitive abilities and more flexible behaviors in the course of evolution. Ever since it first became possible to easily differentiate one cortical area from another on the basis of cell architecture or myelin content it was recognized that the cortex of some mammals exhibited many more divisions into distinct areas than did others. Efforts by the early comparative anatomists, including Brodmann, Campbell, Elliot-Smith, and others, to determine homologies between these cortical areas in different species suggested that apparent homologies could be identified for a number of areas in most mammalian brains (see Fig. 8). Apparently, the primary projection areas for vision, audition, and tactile senses could be homologized in all mammals but the multitude of interdigitated "nonprojection areas" that were evident in the human brain could not all be homologized to areas in monkey brains, and many of the "nonprojection areas" in monkey brains could not be homologized to yet smaller and more "primitive" brains. Flechsig's (1901) demonstration that this precedence of areas was also approximately paralleled by maturational trends (see Fig. 5) completed the evidence for a grand synthesis. The evolution of ever more complex associational abilities in mammals was enabled by the elaboration of additional higher order association areas of cortex that were both progressively further removed from direct peripheral input and more interconnected with each other. Just how these new areas of cortex became interdigitated between old areas was not clear to these authors, but the determination of homologies appeared to require the progressive addition of new structures in a particular order of appearance. One obvious way to account for new structure and new functional abilities appearing in the course of cortical evolution is simply to hypothesize their insertion into an already complete and functioning cortex. The prevailing neuropsychological theory of the early 20th century could accommodate such a view. Like MacLean's 662 TERRENCE W. DEACON flattened Owl monkey cortex electrophysiological map flattened mouse cortex electrophysiological map rostral dorsal-medial dorsal-medial limbic rostral caudal caudal B ventral-lateral ventral-lateral rostral caudal rostral dorsal-medial flattened mouse cortex cytoarchitecture caudal D ventral-medial flattened macaque cortex cytoarchitecture FIG. 8. Comparisons of the number of cortical areas of small and large mammalian brains. The four drawings depict unfolded views of mouse and monkey cortical surfaces. A (mouse cortex) and B (owl monkey cortex) are redrawn from Kaas (1989) and depict area maps determined electrophysiologically in these species. C (mouse cortex) and D (macaque cortex) are redrawn from Caviness and Frost (1980) and Jouandet et al. (1989), respectively, and depict cytoarchitectonic divisions (Krieg's numerical designations following Brodmann). The differences in configurations largely represent different "unfolding" techniques, some of which minimize area distortion at the expense of total map integrity whereas others maintain continuity of the map at the expense of area distortion. All are drawn to equal size for comparability. subsequent Triune Brain hypothesis, the theory of the accretion of new cortical areas necessitated a hierarchic conception of brain organization and function. Flechsig (1900) and Campbell (1905) clearly articulated this complementarity between the orderly addition of cortical areas in evolution and the progressive elaboration of sensory motor processes in "higher" mammals. The associationist assumption at the center of this theory was that higher order mental associations are built from lower RETHINKING MAMMALIAN BRAIN EVOLUTION B 663 D stem mammal 9 primitive mammal prosimian relative size Campbell, Brodmann, Flechsig Sanides, von Bonin Lende, Poliakov FIG. 9. A graphic depiction of Campbell/Brodmann/Flechsig, Bonin/Sanides and Lende/Poliakov scenarios for the addition or differentiation of new cortical areas in mammalian evolution. Although none of the individual schemes is exactly identical with any other (and may not exactly correspond with those depicted) they have been grouped into three distinct categories for depiction because of their underlying theoretical similarities. The relative sizes of these brains are depicted in column A and show that the increase in distinguishable cortical areas is not independent of size. Column B shows an accretive scheme in which projection areas are primitive and association areas are added derived characteristics in later brains. Column C shows a progressive differentiation scheme in which association areas are considered most undifferentiated and therefore primitive and more specialized sensory and motor areas are assumed to be later derived conditions that have differentiated in a series of stages out of previous levels of association cortex. Sanides additionally argues that there is a dual origin of most major sensory/motor fields that determines differentiation at the intersection of a dorsocaudally originating archicortical and a ventrorostrally originating paleocortical trend. This is not depicted here. Column D shows a progressive differentiation scheme based on the parcellation and retraction of initially diffuse overlapping projection fields into eventually discrete non-overlapping fields. The boundaries of the initially overlapping projection fields are depicted by dashed lines. Later parcellation and reduction of diffuse projections within each projection field is depicted as progressively darker regions of gray. The notion of reduction of diffuse projections within a field is from Poliakov and is not discussed as a possibility by Lende, whose theory focused only on the earliest stages of cortical evolution in mammals. order simpler associations, and complicated and flexible skilled responses are constructed by associations between simpler reflex responses. They argued that association areas of cortex only received information from sensory projection areas or other association areas and served as the locus for higher-order associations between sense data and motor programs from primary areas. Association areas ultimately are envisioned as an additional higher-order reflex arc superimposed upon and elaborating existing lower level reflex arcs (Luria, 1980; Sherrington, 1906). Therefore association areas could be added as evolutionary after-thoughts with minimal rewiring of other brain circuits. Each new association area was one step removed from the previous association area in the hierarchy and provided an additional layer of association processes superimposed upon an already complete functional brain (see Fig. 9). Vestiges of this view are still widespread. The pinnacle of this conception of the cortical hierarchy is of course the addition of language areas in the human brain— inserted into an otherwise complete and functional ape brain. The assumption that Broca's area for speech is a new association area peculiar to the human brain has been represented in a number of schemes by the identification of some region within the third frontal convolution of the human brain that is assigned no homologous counterpart in the monkey brain. The possibil- 664 TERRENCE W. DEACON ity that Broca's area has no prehuman homologue and could have been simply added on to an otherwise complete brain seems to be taken for granted in a number of recent discussions of human brain evolution (Passingham, 1981; Tobias, 1981; Falk, 1983) and language evolution (e.g., Chomsky, 1972). Galaburda and Pandya (1982) and Deacon (1984, 1988a, 1990c) provide architectonic and tracer evidence that a homologue for Broca's area exists in the monkey brain, despite the fact that it plays no apparent role in vocal communication in monkeys. A similar argument was presented by Geschwind (1964) and has been reasserted by a number of later writers concerning the inferior parietal lobule of the human brain. Following the classic associationist model of language processing, Geschwind argues that this area plays a fundamental role as an "association area of association areas." Its placement at the temporo-parieto-occipital juncture seemed to ideally suit it for associating the outputs of association cortices from different sensory modalities. Since Geschwind conceived of word meanings as complex associations between word sounds and a multitude of other sensory associations abstracted from sensory experience, an association area of association areas would have to be the necessary substrate for semantic processes. Posterior temporal-parietal-occipital damage often results in disturbances of semantic language comprehension. Geschwind argued that no homologue to this highest order association area was evident in monkey cortices. According to this logic the addition of this area during human evolution was a necessary condition for language evolution. Language evolution becomes the natural end point of a progressive process of adding association areas on top of association areas in the course of mammalian brain evolution. This non-homology claim has also been contradicted by subsequent tracer experiments (e.g., Mesulam et al, 1977; Seltzer and Pandya, 1980) and animal behavioral studies (e.g., Jarvis and Ettlinger, 1977). Recently, a more sophisticated version of an accretion theory has been suggested by Allman (1990) and Kaas (1987, 1989). They argue that the multiplication of cortical areas in many mammalian lineages might be explained by the duplication of existing areas. They suggest that a new population of neurons could appear interdigitated in position between older populations as a result of some genetic accident that caused redundant production of the neurons from one of these regions (it is compared by Kaas, 1987 and 1989, to the addition of an extra body segment in the evolution of lobsters; Gregory, 1935). Recent studies of genetic mutants in fruit flies has demonstrated that at least in these species genetic mutations can cause the production of whole duplicate body sections or limbs interdigitated between existing structures. Allman (1990) suggests that similar genetic mutations might underly areal duplication events in mammalian cortical evolution. In fact, to account for the number of such events that have occurred this would have to be a somewhat common sort of mutation. It is unquestionable that new cortical areas have evolved in some of the larger mammal brains and that different lineages (e.g., cats and monkeys) exhibit different spatial arrangements and functional specializations of the cortical areas that have been added (Kaas, 1989). But is it reasonable to imagine that these areas have been literally inserted between existing cortical areas by the addition of new neural tissue in that position? This claim depends on the possibility that functional areas of cortex are modular in their construction. The neurons that comprise a new cortical area would need to be added along with specifications regarding intrinsic circuitry and afferent and efferent connections with neighboring areas and subcortical sites. This requirement is contradicted by developmental evidence that the afferent and efferent connections of a cortical area are not specified by the information that is intrinsic to the cells in that area, but rather by competitive interactions among competing axons from many areas. Even the transplantation of a section of cortex into a novel position within a fetal brain prior to the development of cortical con- RETHINKING MAMMALIAN BRAIN EVOLUTION nections will not bring with it the functions and connections appropriate to its site of origin (O'Leary and Stanfield, 1989; Stanfield and O'Leary, 1985; see discussion in the next section). The transplanted area will develop functions and connections appropriate to its new position. Thus, even if some new area of cortex miraculously appeared interdigitated between older cortical areas in a developing brain it would not bring with it any new connectional or functional information. Is there an evolutionary sequence of new cortical areas? An alternative approach is to conceive of new cortical areas as differentiating out of old areas rather than being created independently adjacent to them. This has the attractive property that new areas should continue to bear some functional and connectional interrelationship with their parent areas. Because there is a parent-descendent relationship between areas there will also be an implicit sequential order of regional evolution, depending on assumptions about the "initial brain." Correlated with the order of the proposed evolutionary sequence may also be an implied hierarchy of increasing functional differentiation and complexity. The comparative cytoarchitectonic studies of Brodmann (1909) and others appeared to corroborate associationist assumptions that association cortex was newer than projection cortex and that some association areas could only be identified in the most "advanced" brains. The order of regional evolution of the cerebral cortex thus appeared to begin with primary sensory receptive areas and motor output areas and involve the progressive differentiation of ever higher association areas. In the initial state sensory and motor areas were thought to be directly connected to one another to form direct reflex arcs. With the differentiation of new areas inserted between these primary areas the reflex arc becomes more indirect and reflex action gives way to more complex and variable associations between sensation and action. The more intermediate stages, the more complex the analytic capabilities and the 665 less driven by reflex and habit. The newer the area then, the more indirect its link with the input and the more complex its functional properties. In this way the differentiation of association areas from association areas could be correlated with an additive functional hierarchy as well. It turns out that many of the assumptions that initially supported this hierarchic scenario have been subsequently undermined. The first difficulty to crop up was the discovery that association cortex did not lack extensive subcortical connections (Diamond, 1982; LeGros Clark and Northfield, 1939; Rose and Woolsey, 1949). Association areas cannot be conceived as added on top of a complete working system, sending and receiving information only from adjacent lower level cortical areas. They have independent inputs and outputs and are thereby completely integrated into the whole brain at every level every bit as much as are "projection" areas. More problematic still is the nature of these connections. The "highest" association areas exhibit extensive connections with limbic cortical areas (Pandya and Yeterian, 1985), whose evolution is presumed to predate the evolution of primary projection areas in all theories of cortical evolution. Recent tracer studies have also suggested that association areas project to core midbrain and tectal areas and receive indirect projections from these areas relayed through the thalamus. They may even exhibit connectional topography that corresponds more to midbrain maps than to cortical sensory or motor maps (Deacon et al., 1987). These features are also incompatible with the assumption that the "highest" association areas are only connected with areas at the next hierarchic level down. In many ways their principal links are to some of the most primitive brain structures. Finally, there is the problem of architectonic and functional specialization. Probably the most extreme architectonic and functional specializations anywhere in cortex can be found in the primary visual and motor areas of anthropoid brains. The primate striate cortex, for example, exhibits highly derived cytoarchitecture 666 TERRENCE W. DEACON with distinct sublamination of layer IV, complex mosaic distribution of different visual submodalities into a microscopic matrix of architectonically distinct patches or "tufts" (Livingstone and Hubel, 1988), and approximately double the number of cells per cortical column of any other cortical area in any other mammal (Rockel et al., 1980). These are clearly recently derived conditions that indicate a high degree of functional and architectonic specialization. The architecture of association areas is generally less variable from area to area and species to species than is the cytoarchitecture of specialized primary areas (Sanides, 1970). This indicates that structural and functional specialization is not limited to and probably is less often exemplified in "higher order" association areas. Some of these arguments against the classic view have also served as support for an almost exactly inverted view of regional cortical evolution. Roughly inverted scenarios have been proposed by Bishop (1959), von Bonin and Bailey (1961) and Sanides (1969, 1970, 1975). Probably the most ambitious and most widely adopted of these is Sanides' theory of progressive waves of cortical differentiation or "Urtrends." Sanides infers the sequential evolution of cortical areas from a trend toward increasing architectonic specialization that culminates in the evolution of specialized primary projection areas (see Fig. 9). Following the lead of Abbie (1942) and Dart (1934), Sanides argued that isocortical areas evolved from undifferentiated periallocortical zones along the borders of primitive hippocampal and olfactory cortex in a series of stages of increasing differentiation. The trend is envisioned as a series of "growth rings" (Sanides, 1970) constituted by progressively more specialized cortical areas differentiating out of relatively less differentiated areas. Areas that correspond to the highest level association cortices in the classic view comprise some of the most primitive ancestral areas in this view. The most highly specialized areas in advanced brains are envisioned to be the result of specialized core areas developing within more generalized areas, and subsequently, further specialized core areas developing within these specialized areas, and so on. The ancient status claimed for association areas could account for their extensive connections with limbic structures and their prominent links to midbrain systems. The progressive stages of differentiation also appear to correlate with connectional relationships between cortical areas (Pandya and Yeterian, 1985). If it is assumed that cortical areas only retain connections with their immediate precursors but not second generation precursors, this model could explain the links between association areas and limbic cortex as well as the lack of connections between limbic areas and either primary sensory-motor areas or their adjacent belt areas. As noted earlier in this discussion, the most serious argument against Sanides' evolutionary sequence is the apparent existence of specialized visual, auditory and somatic projection areas in all mammal brains, even in marsupials and monotremes (Kaas, 1987). Even the primitive cortex of turtles exhibits representation of visual and somatic responses in distinguishable regions crudely appropriate to their topological position in mammalian cortex. An ancient status for these specialized sensory areas contradicts Sanides' model. One possible counter-response is to argue that the apparent homology between the specialized primary sensory areas of advanced brains and the primary sensory areas of ancestral and conservative brains is incorrect. For example, von Bonin and Bailey (1961) argue that the presumed homology between the hedgehog visual cortex and monkey primary visual cortex is not supported by the cytoarchitectonic criteria Brodmann (1909) originally suggested. They conclude that it is far more similar to the more generalized areas of visual association cortex. This interpretation is supported by the fact that in the visual systems of the opossum and hedgehog the thalamocortical projections of the lateral geniculate nucleus (corresponding to primary visual cortex projections) and pulvinar nuclei (corresponding to extrastriate association cortex projections) extensively overlap (Diamond et al., 1985; RETHINKING MAMMALIAN BRAIN EVOLUTION Kaas et ai, 1970). Although the so-called projection areas are specialized for receiving relatively more direct information from peripheral sensors, all adjacent areas comprising a single modality receive independent sensory afferents from the thalamus and all share some thalamic connections in common (Caviness and Frost, 1980; Diamond, 1982). Hierarchic scenarios of cortical evolution are appealing both because of their agreement with tacit assumptions about mental progress and mental processes, and because of the way they simplify the assumptions about connectional and functional integration associated with the addition of new parts to a complex brain. Adding the new parts to the terminal end of a growing hierarchy limits the presumed problems of integration with all lower levels. It also provides an explanation for the evolution of complex structures by demonstrating plausible intermediate steps in complexity, differentiation and specialization. One serious problem with both hierarchic schemes for explaining regional evolution of cortex is the constraint of the linear sequence itself. Ultimately, both theories are terminal addition theories. The arguments in support of both are analogous to those for terminal addition in general: addition of new structures to systems that were complete in an adult of the preceding evolutionary stage; avoiding the complication of inserting structural changes in the middle of a complex process; and the assumption that additional structures augment the function of the preceding structures. As a result they are prone to similar criticisms. It is not at all clear that cortical areas within any one brain are organized according to simple linear hierarchies, nor is it obvious how cortical areas in different lineages can be homologized with respect to a strict number of steps in the evolutionary sequence. For example, the primate visual system exhibits at least two, and probably three or four, distinct processing pathways for different aspects of visual perception that diverge from striate cortex into distinct groups of association areas. Like a branching tree structure, one would assume that multiplication 667 of areas should be highest at its terminal end. If areas are capable of differentiating out of other areas in the course of evolution there should be an increasing number of bifurcations as the process continues. Each sense modality contains multiple association areas at the same level of the cortical hierarchy but only one primary sensory area. This would not be likely if these specialized areas represented a terminal end in the evolutionary differentiation process. And yet the extreme other end of the spectrum—paralimbic association areas— is not the level of the highest multiplicity of cortical areas either. In the visual modality it appears that the most diversity and multiplicity of cortical areas is found at middle levels in the processing hierarchy. This is likely true of other modalities as well. This pattern is implausible in either of the two general terminal addition scenarios. Strict hierarchical terminal addition is not the only possibility, nor is it necessarily less complex than are non-hierarchic scenarios. Given that most cortical areas are connected to more than one other area and that all are connected with distinct subcortical structures, there is no obvious sense in which a new cortical area can be thought of as superimposed on an already complete and functioning system—it must inevitably emerge in the middle of a complex integrated network. Terminal addition contributes no additional explanatory power toward solving the problem of the preestablished integration of new areas. Models of cortical evolution that make no assumptions about the order of appearance of cortical areas have been outlined byAllman(1982, 1990) and by Kaas (1987, 1989). They each argue that the multiplication of cortical sensory areas of the visual system can be explained by duplication of existing cortical areas followed by subsequent differentiation of function in the new area. Presumably the new area will initially share the same connections and cell types as its older twin and gradually will come to gain or lose connections and exhibit modifications in cellular architecture associated with its changes in function. It is often the case that adjacent cortical areas serving the 668 TERRENCE W. DEACON same sensory modality also exhibit connections with similar structures elsewhere in the brain—sometimes to separate divisions of these structures, at other times overlapping in connectivity, and it is not unusual that neurons in common afferent sources will send collateral branches of the same axon to adjacent cortical areas. Duplication of this sort would also account for the many striking homologies between all isocortical areas. The duplication of an existing area is presumed to be a relatively innocuous accidental mutation. However, the availability of redundant areas frees one of the two from the constraints of the primary adaptation so that it is able to develop some additional, complementary visual function. In the primate visual system it is clearly the case that distinct visual areas seem to be specialized for different submodality functions in vision, such as color, form and movement perception. Thus, by duplication and subsequent differentiation of function the entire collection of interdependent visual areas could have been produced. The apparently hierarchic arrangement of these areas is not explicitly explained in either model, but probably it could be argued that the one area that retains the ancestral function becomes the more primary area and the differentiated one becomes more secondary. Progression from one to the next in sequence could then be explained simply on the basis of the influence of adjacent areas. Allman (1990) and Kaas (1987, 1989) assume that new areas are added to an otherwise complete visual system, inserted between existing cortical areas. This is consistent with their focus on advancement and augmentation of function as the prime mover in the evolution of new cortical areas—duplicated areas become recruited to some new adaptive function that augments or complements existing functions. This argument is used to explain how distinct cortical visual areas have become specialized for distinct visual submodalities, such as color, movement, or form perception. A major criticism is that the separate functional specializations of the different visual areas, which Kaas (1989) suggests might be too complex to be handled efficiently by a single large visual area, do appear to be handled by only a few visual areas in small brains. There is no evidence that "new" functions have evolved, only that existing functions have become segregated and distributed to parallel visual processors. If processing all these modalities together in a single area is merely an efficiency problem, we should expect that at least some very small brains would also segregate visual functions into the almost two dozen visual areas that endow large primate brains and that at least some large brains would collapse visual processing into only one or two visual areas, but this is not seen. There are clearly size factors involved. Accretion assumptions are not essential to explain the appearance of new cortical areas in mammalian evolution. A scenario for the early stages of mammalian cortical evolution was presented by Lende (1963, 1969) that does not make any assumptions about evolutionary precedence of cortical areas. He argues that the pattern exhibited in the common ancestor to all mammals (including marsupials and monotremes) included cortical projection fields that were extensively overlapping and therefore poorly differentiated from one another. By a gradual process of differentiation over the course of evolution each projection field retracted with respect to the other until in most modern species each projection field is exclusive of all others. In Lende's view the marsupial and basal insectivore cortices represent a state where the retraction into separate territories is nearly complete, with only somatic and motor areas still overlapping. Because it is purely a differentiation model there is no addition of areas and no distinction between old and new cortical fields, just old and new patterns. A closely related theory of the evolution of connectional differentiation has been proposed by Ebbesson (1980, 1984) and will be discussed in the next section. Lende's model was only intended to explain the earliest stages of mammalian cortical evolution (better resolution physiological recording techniques have largely contradicted his claims about the lack of differentiation in primitive brains; Kaas, RETHINKING MAMMALIAN BRAIN EVOLUTION 669 isocortical evolution by areal differentiation FIG. 10. Depiction of a scheme of progressive differentiation of cortical areas from one another that does not assume an evolutionary sequence in which some coexisting areas are older than or ancestral to others. The figures are meant to represent flattened cortical hemispheres with limbic cortical areas representing the white perimeter of each and isocortical subareas represented by the gray areas contained within. Below each of the three brains of increasing size is a block diagram of the ancestor-descendent relationship for the progressive generation of new cortical subdivisions. The use of diverging shades of gray is intended to represent the differentiation of both descendents of a subdivided ancestral area from the architectonic and functional characteristics of this ancestor. 1987). However, a simple differentiation theory suggests some interesting alternative interpretations of area multiplication problems in more differentiated mammalian brains. In these brains all the projection areas are differentiated from one another and, with the exception of the somatic and motor areas, they have also been separated by interdigitated association areas. The idea of progressive differential retraction of previously diffusely overlapping projections might account for differentiation of previously undifferentiated association areas within each sensory modality. A single sense modality might be conceived as becoming progressively segregated into differentiated submodalities. This hypothesis (represented in Fig. 9) does not necessarily predict that the interdigitated cortex between primary areas should be any more or less complex than projection areas nor that it should be performing any higher function. The assumption that one structure has to be older or more conserved than another is actually not even a necessary premise for hierarchic terminal addition scenarios of regional cortical evolution. If multiple new areas result from the differentiation of previously unitary areas it is not necessary that one of the resulting areas be considered the homologue of the ancestral area and the other or others be considered derived. If we assume either that a new cortical area results from duplication of an existing area or that it differentiates out of some sector of a pre-existing area and eventually takes on a function that is somehow complementary to that of the other area, then it should follow that both areas will be changed in the process. In this regard, the primary visual cortex in primates cannot even be strictly homologized to the primary visual cortex in the squirrel because in the squirrel many of the visual functions handled in the primary visual area are in the primate partially distributed to some of its many more numerous nearby visual areas. In a hypothetical "initial brain" with only one visual area all the distinct submodality analyses would have to be performed within that area— 670 TERRENCE W. DEACON luminosity, movement, color, form, spatial relationships, local features, etc. There is no visual area in a brain with many visual areas that performs all these functions. Even a duplicate area and its progenitor should be expected to change with respect to one another, in the course of subsequent evolution, so that neither resulting area will be directly comparable to the original .Thus it is probably more accurate to view the multiplication of cortical areas in terms of progressive differentiation of all areas with respect to one another. Both areas created after the subdivision of some previous area will differ slightly from one another and from ancestral structure, and these differences will likely increase over time (see Fig. 10). From this perspective it does not matter where in a cortical hierarchy the new division appears, the differentiation pattern will be essentially the same. It cannot automatically be assumed that there is any strict hierarchy from functionally and architectonically simple to complex cortical areas or from phylogenetically old to new cortical areas. A ranking of areas with respect to relative phylogenetic age or functional complexity is not unambiguously reflected in neuroanatomical data and may not be consistent with developmental considerations of cortical differentiation. Nor are we justified in assuming that the addition of new functional subdivisions of cortex correlates with an enhancement of function. The addition of new cortical areas still needs to be examined with respect to the influence of brain size. Extensive multiplicity of cortical areas is never seen in small brains, and the most extensive multiplicity of areas appears only in very large brains (e.g., the human brain). This suggests that areal multiplication might be the result of facultative developmental responses to brain size and not distinct genetic adaptations. Reorganization and the neogenesis of neural circuits Whether we explain area multiplication and functional differentiation in terms of addition or differentiation there is still a major problem area that must be addressed: How are the neural connections of these areas determined? This rewiring problem is most troublesome for addition or duplication hypotheses. If an area is completely new its connections must presumably invade territories in other structures that are occupied by projections from pre-existing areas, and it must itself be invaded by axons from other structures that would otherwise have found other targets in the brain. But area duplication does not escape these problems. A duplicated area may have all the afferent and efferent specificities appropriate to the original area but there still must be overlap in efferent projections from the duplicate and the original area as well as a dividing or sharing of afferents. Any subsequent differentiation of the duplicated area from its progenitor likely also involves changes of connections, including both the loss of many of those shared with the original area as well as a shift of its efferents to new targets. The possibility of connectional reorganization also suggests other options for neogenesis. If connectional organization can be altered then it is not necessary for new structure to be added for new functional areas to emerge, it is only necessary for their underlying connections to change. Since ultimately the patterns of connection determine function within the nervous system, all theories of neogenesis of cortical structure and function must address this issue. A theory that fails to explain how underlying connectional reorganization takes place is fundamentally incomplete. The most obvious hypothesis for explaining the evolution of new connections is that they are simply added. Because this hypothesis requires axons to enter novel target areas that are occupied by other connections and ultimately displace some of those connections or form new synapses in that area, it has been called the invasion hypothesis. The basic features of this process are diagrammed in Figure 11. Cells from one area either change their target or produce collateral branches that invade a new target area. This idea has had a long history in comparative neuroanatomy (e.g., AriensKappersrta/., 1936; Herrick, 1920) and seems essential to explain the appearance of certain neural pathways that occur RETHINKING MAMMALIAN BRAIN EVOLUTION simple accretion hypothesis before duplication-addition after duplication-addition parcellation hypothesis diffuse connectivity before parcellation differential connectivity after parcellation axonal invasion hypothesis before axonal invasion after axonal invasion equivalent cell population hypothesis before cell migration after cell migration FIG. 11. Four commonly cited or assumed rewiring hypotheses are depicted with source nuclei indicated by shaded ellipses, cortical (or other nuclear) targets indicated by shaded boxes, and connections indicated by black arrows connecting them. Antecedent and consequent conditions are shown in neighboring boxes. The simple accretion hypothesis is depicted at the top left, in which a new structure is added to the brain including its own new connections. The invasion hypothesis is depicted at the top right, in which a new set of connections, or connections originally targeting some other area, invade and establish synapses in a novel brain structure. The parcellation hypothesis is depicted at the bottom left, in which previously diffusely interconnected structures lose some of their diffuse connections in a complementary fashion so as to produce subdivisions of each that are connectionally distinct. The "equivalent cell" hypothesis is depicted at the bottom right, in which cells from one region migrate into another structure and attract their afferents to this new structural position. in relatively recent lineages but not ances- comparative evidence demonstrates a tral lineages (Northcutt, 1984). Invasion remarkable conservatism of connection was long thought to be the explanation of patterns within all vertebrate brains despite the progressive development of telence- radical differences in size, morphology and phalic specializations in vertebrate evolu- differentiation. The overwhelming majortion. However, many of the telencephalic ity of major pathways seem to be evident connections that were formerly thought in all vertebrates and probably represent absent in anamniotic vertebrates have an inheritance from the common protoverturned out to be demonstrable with axonal tebrate ancestor. Ebbesson argues that tracing techniques (Ebbesson, 1980; there are no developmental or comparaNorthcutt, 1981). In support of this tive cases that would require the assumphypothesis Northcutt (1984) argues that the tion that axons must have invaded an presence of a spinothalamic pathway in all unusual target (although his own theory reptiles, birds and mammals but in no other has been called unfalsifiable in this regard). group but cartilaginous fishes and the pres- All connection changes in the course of ence of palliospinal pathways only in birds vertebrate evolution can be explained and mammals are each best explained by entirely in terms of changes in the relative invasion. numbers of preexisting connections— However, Ebbesson (1980) questions the sometimes complete loss of connections— plausibility of invasion on the grounds that in prior lineages. Ebbesson (1980) refers 672 TERRENCE W. DEACON to this as the parcellation theory of brain evolution. Invasion, he argues, either simply does not occur or else is extremely rare. A schematic depiction of the parcellation hypothesis is presented in Figure 11. Parcellation theory presumes that the ancestral condition consists of a relatively undifferentiated pattern of connections between two given structures. It is proposed that progressive loss or retraction of a selected subpopulation of these fibers is responsible for subsequent differentiation of each ancestral structure into two, connectionally and functionally distinct subdivisions. In the course of time further parcellations of these separate projection systems can result in yet further functional specialization. This process would account for increasing multiplication of areas and increased differentiation of functions in the course of brain evolution. It also explicitly predicts many details of connectional organization that should correlate with areal multiplication and differentiation in cortex. Because subdivided areas originate from a single area they each will inherit most but not all of the connections of that ancestral area. For example if an extrastriate visual area in some ancestral lineage becomes subdivided in its descendents we should expect that the lateral posterior (pulvinar) thalamic source of the ancestral afferents would be the same for the descendent subareas although perhaps subdivided into new subdivisions, and we should expect that the tectal targets of its efferents should likewise be the same structure or some new subdivisions of that structure. These predictions appear to be reflected in the organization of afferent and efferent connections of brains with relatively few visual areas as compared to those with many visual areas (Diamondsa/., 1985; Kaasand Huerta, 1988), as well as in other systems. Ebbesson further argues that the evolutionary history of parcellation events is recapitulated in developmental processes. Parcellation must occur at some particular stage in the development of existing connections in any organism and subsequent further parcellation processes must be preceded by this first parcellation. Following von Baer's principle of character prece- dence {i.e., that members of two or more closely related taxa will follow the same course of development to the stage of their divergence), this would mean that two species' developmental patterns would coincide to the point of the first parcellation event that distinguished them, and therefore that parcellation events that follow others in evolutionary time, and further parcel the same projection system, should also be expected to occur subsequent to the first in development. This predicts that early stages of brain development should be characterized by the presence of diffuse axonal projections and later stages would proceed through progressive parcellation processes. In general terms this pattern of progressive culling of initially diffuse connections is indeed exhibited in brain development. Initial axonal projections appear to be less selective and more exuberant than the projections that survive to adulthood, and as a result fetal axons contact many more targets and a much wider variety of targets than do adult axons. During subsequent development these superfluous connections are culled, resulting in connections that are far more specific and topographically organized (Jacobson, 1978; Purves and Lichtman, 1980, 1985). The details of this process will be discussed in the next section. Ebbesson (1980) argues that this developmental pattern recapitulates the ancestral sequence of differentiation of each brain area and its connections. These transient connections within the fetal brain are therefore viewed as "fossils" of earlier patterns of brain organization. Viewing certain transient events in neural development as "fossils" may also help explain the appearance and subsequent elimination of whole classes of neurons during development that serve as transitory targets for projections ultimately destined for other targets. However, this strictly recapitulationist interpretation is not critical to the parcellation hypothesis and weakens its generality and predictive power. Although Ebbesson's (1980) initial examples compare the adult organization of connections in primitive fish to embryonic connections RETHINKING MAMMALIAN BRAIN EVOLUTION in terrestrial vertebrates (and therefore have earned the criticism that it assumes a crude scala naturae view of living species as well as strict terminal addition), the underlying assumptions of parcellation theory do not require terminal modification. The relative timing of fetal parcellation events need not be an exact recapitulation of evolutionary events and the hierarchic dependency of one parcellation event on another during development does not necessarily imply a corresponding phylogenetic order as well. A more flexible version of the theory can be articulated if we assume that a new parcellation process can affect connections at any stage of brain development during the course of evolution. Parcellation processes that alter connections early in ontogeny will operate on less differentiated connection patterns than parcellation processes that occur later in ontogeny. Presumable parcellation processes during development depend on earlier parcellation processes, and so a subtle change at an early stage might have radical consequences for adult structure. A wider range of reorganizational effects should result from mutations that influence parcellation early in development, particularly if subsequent parcellation events are undermined or biased by the earlier changes. But if changes in parcellation processes can occur at any developmental stage, then brain development cannot be a strict recapitulation of the order of phyletic events in brain evolution. A very early change may have been very recently acquired whereas a relatively late parcellation change may date to a much earlier evolutionary epoch. Recently acquired parcellation changes that affect an early stage of development could fundamentally alter the pattern of all subsequent developmental events so that they no longer reflect any meaningful phyletic series. The parcellation hypothesis is the inverse of the invasion hypothesis both in its mechanism and in the assumptions that it makes about ancestry-descent relationships of brain structure. In more general terms, the invasion hypothesis is an example of an additive theory and the parcellation hypothesis is an example of a differentia- 673 tion theory. Each of these two mechanisms for connectional change correspondingly supports additive versus differentiational theories of brain structure evolution. The parcellation hypothesis is most consistent with area addition scenarios involving differentiation of new areas from old areas and is inconsistent with simple addition hypotheses that presume the invasion of new axons. It is probably also inconsistent with area duplication scenarios, since it would reject arguments about projectionmap duplication as invasion hypotheses. But in addition, parcellation is also inconsistent with many of the existing differentiation theories of cortical evolution because it does not predict that ancestral cortical areas can remain unchanged as new areas differentiate out of them. For this to happen the older of the two areas would have to maintain all of its previous connections and the newer area would be distinguished only by its lack of certain connections and not by the possession of connections not also present in the older area. Considering either thalamic afferents or subcortical efferents, it is clearly not the case that there are cortical areas that possess all the connections of any of their neighbors; each has slightly different although often partially overlapping afferents and efferents. The adjacency of afferent sources and efferent targets of neighboring cortical areas as well as the partial overlap in some of these is strong support for the parcellation hypothesis. Invasion is a necessary hypothesis for both additive and recapitulational theories of regional cortical evolution. Structural addition is doubly challenged by the parcellation hypothesis because the latter denies both the possibility that new structures can arise from nothing and the possibility of invasion of new structures by axons that did not contact it ancestrally. Even though they do not assume structural addition, sequential differentiation theories, such as the traditional evolutionary hierarchy from projection to association areas or Sanides' inverted hierarchy from association areas to specialized sensory and motor core areas, are also both tied to the assumption that axon invasion events have been common- 674 TERRENCE W. DEACON place. If Ebbesson is correct in assuming that these are rare events in evolution then all of these hypotheses must be abandoned. It is important to recognize that the parcellation hypothesis is a stronger claim about what can and cannot happen in brain evolution. Invasion hypotheses do not deny the possibility that parcellation may play a major role in brain evolution. Invasion is an additional assumption beyond parcellation. One difficulty with denying any possibility of invasion is that to do so often requires hypothesizing many more connectional changes in order to explain a phylogenetic difference that can otherwise be explained by only one or two invasion events (Northcutt, 1984). Although Ebbesson's argument for the ancient status of preset axon-target affinities is also corroborated by evidence of the ancient and highly conservative nature of molecular cell communication and recognition mechanisms (Fasolo and Malacarne, 1988), such specificity may be determined by complex relationships of timing and hierarchically organized interactive effects that render this specificity somewhat degenerate (Edelman, 1987). A more fundamental criticism of parcellation theory in its strong form is that it is preformationist (see numerous commentaries following Ebbesson, 1984). Either some initial protovertebrate brain (possibly long antedating the vertebrates) must be considered to have been completely undifferentiated in connectivity and therefore totipotential from an evolutionary standpoint, or some of the initial connection patterns must have been preformed from the start and require explanation in some other way, e.g., invasion. The first possibility is more consistent with the exclusivity assumption of parcellation theory. There is no stage of brain development that exhibits a corresponding totally interconnected undifferentiated state. Although there is some degree of initial overproduction of projections and poor differentiation of target sites in the fetal brain these connections are neither totipotent nor completely undifferentiated. Most pairs of brain structures never pass through a stage where they are connected. Target sites are underdetermined but probably not undetermined by genetically pre-established molecular affinities between growing axons and potential target cell substrates. During embryogenesis axons do in fact grow out from their cells of origin to invade different structures distantly located in the brain. En route to their genetically underdetermined target zones they also may pass through structures with which they establish no synaptic contacts or only transient contacts. Both the active "exploratory" nature of embryonic axons and the prespecification of initial target zones are potentially troublesome for parcellation theory. However, permanent loss of many connectional affinities over the course of vertebrate evolution is entirely consistent with the theory. Both the invasion hypothesis and the parcellation hypothesis can probably be used to explain almost any possible arrangement of connections in the living vertebrates. The invasion hypothesis makes many more assumptions regarding each individual rewiring event, whereas the parcellation hypothesis makes some other rather troubling assumptions about the initial condition of vertebrates and often requires considerable theoretical circumlocution to explain what could be explained by a single invasion event. Each handles certain examples better than the other. A third alternative hypothesis, that to some extent can serve as a bridge between these two polar opposites, is the equivalent cell hypothesis suggested by Karten (1969; Nauta and Karten, 1970). On the assumption that the connectional relationship between two different cell groups within the brain is specified by some features specific to those cells, their connection should be maintained even if one of these cell populations became displaced or actively migrates to an entirely different location. Karten (1969; Karten and Shimuzu, 1989) proposes this as a possible explanation for the apparent homologies between thalamic projections to the dorsal ventricular ridge in birds and to the isocortex in mammals. He argues that cells which in avian and reptilian brains are destined to form the RETHINKING MAMMALIAN BRAIN EVOLUTION dorsal ventricular ridge may take a different migratory pathway in mammals and ended up in cortex. In the process they would also attract their thalamic afferents to this new target site. The general form of the equivalent cell hypothesis is schematically diagrammed in Figure 11. With respect to mammalian cortical evolution it has the advantage of also accounting for the significantly increased number of cells that occupy mammalian as compared to bird and reptile cortex. This hypothesis could thus explain the apparent "invasion" of new thalamic afferents into the cerebral cortex and also the apparent "invasion" of new subcortical targets by cerebral cortical cells without requiring the appearance of any new connections or any change in the underlying topology of connection patterns. However, the theory makes strong assumptions about the specificity of developing connections that may limit its generality. Increasing the amount of comparative data will not by itself be able to help us choose between these alternative hypotheses. The homological ambiguities limit our ability to clearly discern what is a new connection and what is a modified ancestral connection. And since each of the different hypotheses are sufficiently flexible to account for nearly any pattern, we have only parsimony assumptions to rely upon. Unfortunately, the capricious nature of the evolutionary process suggests that we should not place too much confidence in parsimony arguments. Each of the alternative theories proposed to account for connectional reorganization depends on specific assumptions about the ontogenetic processes that ultimately determine connectional specificity. We can thus turn to the ontogenetic data to look for developmental constraints that can rule out certain versions of these hypotheses and developmental patterns that might suggest alternative mechanisms. ONTOGENY CONSTRAINS PHYLOGENY Ontogeny of neural populations, connections and functional areas There are clear trends in mammalian brain evolution. However, the evidence for 675 these trends and the theoretical assumptions about homologies, progression, size increase and cortical neogenesis have been shown to be seriously flawed. Although patterns are evident it cannot be decided on the basis of comparative evidence whether they are merely secondary architectonic adaptations to differences in brain size, adaptations for sensory-motor specialization, or independent macroevolutionary tendencies for increased cognitive complexity and diversity. However, the predictable relationships between these trends and differences in brain size as well as the parallelisms exhibited in distant lineages suggests that many of these patterns may instead be the result of underlying developmental homologies that are expressed differentially with respect to brain size or certain sensory-motor specializations (e.g., regression of vision in echolocating bats). A considerable body of new data is emerging concerning the development of the brain that can help sort out which evolutionary hypotheses are tenable and which are not. Obviously brains do not evolve from one adult form to another, although this is often how brain evolution is portrayed. In fact, most of the well established theories of cortical evolution make no assumptions at all about brain development processes (except possibly the erroneous assumption that they should recapitulate phylogenetic trends). But phylogenetic change in brain structure is the result of changes in the process of brain development and ultimately must be explained in terms of developmental mechanisms. The importance of ontogeny to the understanding of brain evolution is not that it recapitulates phylogeny—it almost certainly does not—but that it constrains the possible modes of variation that phylogenetic changes can exhibit (Alberch, 1982; Katz, 1982; Smith-Gill, 1983). If there are only certain ways that cell populations, functional areas or connections can develop within a brain, then patterns of phylogenetic change will tend to be limited accordingly and the possibility of parallel evolutionary trends arising in independent lineages will be increased. 676 TERRENCE W. DEACON one area of a donor's cortex and transplanted to a different area of a recipient's cortex (Fig. 12). In the mature brain the transplanted cortical tissue takes on the sensory or motor functions, assumes the cytoarchitecture and even develops the appropriate afferent and efferent connections characteristic of its new cortical position rather than its place of origin (O'Leary and Stanfield, 1989; Stanfield and O'Leary, 1985). This relatively late, interactive determination of cortical structure and mature connections function has very significant implications for the evolution of isocortical subdivisions. The evolution of a new cortical region must therefore be a systemic proFIG. 12. Fetal cortical transplant experiment in rats in which either a frontal cortical sector is transplanted cess and not the result of the isolated local to the posterior cortex of a host or a posterior cortical expression of genetic mutations. sector is translated to the anterior cortex of a host. In either case, when the animal matures it is found The structural and functional differenthat the cortical efferents from these transplants proj- tiation of any cortical area is thus not specect to the appropriate targets for their new position ified by the specific local cell lineages that rather than the target specified by their original positions (i.e., rostral sensory/motor areas send efferents constitute it, and takes place considerably after neurogenesis has been completed for to the brainstem and spinal cord whereas posterior visual areas send efferents to the tectum). all cortical areas. The determination of what constitutes a distinct functional area, where its boundaries are, how big it is with A number of features of the neural respect to neighboring areas, the local spedevelopmental process in mammals are cializations of its myelo- and cyto-architecincompatible with scenarios of cortical evo- ture and its connections with other areas lution which assume that cortical areas can are all largely independent of which cells appear, differentiate, or even change their constitute that area. New cortical areas that relative sizes as independent units during appear in the course of evolution cannot the course of evolution. This is because the have been added as whole units correinformation that specifies the size, archi- sponding to specific populations of new tectonic organization, afferent and effer- cells. ent connections and therefore the basic Classic theories of additive cortical evofunction of a cortical area during its devel- lution are clearly inconsistent with this opment is determined by factors extrinsic constraint of cortical development because to the cells that comprise that area. The it undermines any possibility of discrete neurons that comprise the fetal cortex are terminal addition. It also undermines addinearly all produced prior to the stage at tive theories of differential area expansion which the cortex is invaded by thalamic as well (e.g., differential addition of cells to afferents and prior to the stage at which association cortex with respect to primary axons originating from cortical neurons cortex during cortical advancement). The reach their targets. At this stage the cell addition of cells to one sector of cortex groups in different positions on the cortical rather than another does not determine mantle are effectively totipotent with which of the cells will or will not be included respect to their ability to assume any of the within the functional regions that develop different functional roles exhibited by cell in that sector. The enlargement of the corgroups in the adult cortex (O'Leary, 1989). tex cannot be thought of in piecemeal The most striking evidence for this comes terms. Addition of new cortical areas canfrom fetal transplantation studies where not be the direct cause of the addition of sectors of fetal cortex are removed from new brain mass. Given the fact that there fetal cortical transplants RETHINKING MAMMALIAN BRAIN EVOLUTION is no specific topographic information represented in the developing cortex, cortical expansion must be considered as a whole and area by area size determination must be a consequence of secondary processes. The expansion of the total cortical mantle is probably not determined by a simple increase in neurogenesis either. The target size of the cerebral cortex is likely determined prior to neurogenesis for the structure as a whole. During neurogenesis precurser cells in a germinal layer deep to the developing cortex divide to produce neurons that leave this zone and migrate along the length of special radial glial guide cells that extend from the germinal layer to the surface of cortex. These guides effectively limit tangential migration (although see Walsh and Cepko, 1988, for discussion of exceptions) and serve to align succeeding cells along this radial column occupying ever more superficial positions. Radial guides and the germinal zone at their base probably form distinct proliferative units, or ontogenetic columns (Rakic, 1988). All cell types within that columnar unit of cortex are derived from a common polyclonal precurser. Earlier it was noted that the number of cells within a column of cortex of the same tangential dimensions is approximately the same in species with very different size brains (Rockel et al., 1980). This indicates that neurogenesis within an ontogenetic columnar unit is invariable across species and irrespective of brain size. Cortical expansion must therefore be understood in terms of the addition of more ontogenetic columns as opposed to the increase of cell production within these columns (Rakic, 1988). Since the multiplication or germinal precursors which will establish ontogenetic columns occurs prior to the terminal differentiation of neurons from these precursors, the determination of the size of the cortical mantle must be determined prior to neurogenesis within the cortex. The determination of area dimensions and boundaries within the developing cortex must occur subsequent to neurogenesis by virtue of other mechanisms. This appears largely to be the result of afferent and efferent interactions. In the discussion 677 of parcellation theory it was noted that the first axonal connections during development are over-exuberant and relatively unselective. These initially diffuse projecting cortical afferents and efferents compete for dwindling synapses within their target areas. This competition results in a significant culling of axons and axon collaterals and some cell death. The relative correlation of the neural activity of an axon with others in the near vicinity which relay similar information to an area is thought to play a significant role in determining which axons will be retained and which will be eliminated (Purves and Lichtman, 1985). The resultant parcellation of connections largely determines which structural and functional characteristics will develop. This is demonstrated dramatically in the case of cortical efferents. In an infant rat, cells in layer V of all regions of the isocortex appear to give rise to axons that extend into the spinal cord, but in the adult rat only rostrally located somatomotor areas contain cells with spinal projections. More caudally located areas, specialized for visual or auditory modalities, lose these spinal connections but retain connections to the tectum (see Fig. 13). This explains why heterotopic cortical transplants take on the connections and functions appropriate to their new cortical position. A similar pattern of overexuberant and relatively undifferentiated connections followed by later culling of a large number of these connections has been extensively documented for thalamocortical projections and for corticocortical connections, among many other systems (see reviews in Jacobson, 1978; Purves and Lichtman, 1980, 1985; Purves, 1988). Connectional interactions must also play the major role in determining the size that a source or target structure will attain. The elimination or reduction of afferent connections to a cortical area during fetal development can cause it to be reduced in size, can cause neighboring areas to become enlarged, and can cause a corresponding displacement of the cortical boundary between them (Rakic, 1988). Deafferentation does not seem to diminish the numbers of cells per cortical column 678 initial axon migration TERRENCE W. DEACON nections. It appears that alternative projections inevitably will substitute for the lost afferents. However, some degree of cell death—particularly at early stages— may play a role in cortical parcellation and distinguishes certain cortical areas from others (Finlay and Slattery, 1983). The displacement hypothesis An alternative general model of connectional reorganization processes that takes into account both allometric effects and the competitive parcellation process that sculpt cortical areas can be derived from these developmental considerations. As a result of investigating different problems in comparative neuroanatomy, Deacon mature (1984, 19886), Finlay et al. (1987), Purves connections (1988) and Wilczynski (1984) have each suggested that ontogenetic factors play a central role in the reorganization of neural FIG. 13. Exuberant efferent cortical projections and circuits in response to differences in neural culling of connections in development is depicted with populations, regressive processes and perrespect to frontal somatomotor areas and posterior turbations of maturation schedules, or visual areas. Early in the development of an infant rat homoplaseous changes in peripheral senpyramidal cells from layer V of nearly all cortical regions have axons that reach these targets. These sory or motor structures. These views are exuberant axons are later culled in a parcellation pro- similar enough to be capable of synthesis cess driven by dynamic interactions between com- into a single general model of the strucpeting axons and their targets. tural reorganization processes underlying most brain evolution. in that area, only the number of columns The displacement hypothesis, as it can be that are contained within a projection area. called, argues that loss of connections, In other regions of the brain, more limited acquisition of additional connections or in their possible sources of afferent input replacement of one class of connections by or efferent targets, a loss of connections another occurs when competitive axonal can induce significant cell death. This interactions are biased by contextual events appears to be the case with many periph- during development. This can happen as a eral afferent targets (e.g., the lateral genic- result of changes in relative size relationulate nucleus, Rakic, 1988) and efferent ships (an extreme example of which might sources (e.g., brain stem motor nuclei, Alley, be complete cell death for a particular tar1974; Sohal, 1976). This cell death is pre- get or source), changes in the amount or sumed to play a role in the functional intercorrelation of afferent information to matching of peripheral afferents to target one system as opposed to another, or neuronal populations without the need for changes in the relative importance of inigenetic changes in order to specifically tial axon-target affinities, or changes in match afferents and cell populations in developmental timing. Four possible modes every instance of size change in evolution of connectional displacement are depicted or of peripheral homoplasy (Cowan, 1973; in Figure 14. Although the figure depicts Cowanria/., 1984; Finlay et al., 1987; Wil- size relationships, this can be understood czynski, 1984). The multipotentiality of metaphorically to represent competitive cortical cells both for afferent and efferent biases of all kinds. For example, synchroconnections probably accounts for the lack nization of target cell maturation and of significant cell death due to loss of con- axonal arrival would increase the likeliimmature connections 679 RETHINKING MAMMALIAN BRAIN EVOLUTION axonal connectivity after efferent target expansion axonal displacement hypothesis axonal connectivity after efferent target reduction m ancestral condition As 1 1 1 axonal connectivity before allometric reorganization ( > axonat connectivity after afferent source expansion 1 1 1 6 4 N axonal connectivity after afferent source reduction FIG. 14. The displacement hypothesis is depicted with four possible interpretations of invasion-like effects. In each case either the effective enlargement or reduction of targets or afferents (depicted by the size of the structure) is invoked to explain the source of bias driving the displacement of connections from one target to another. An analogous pattern could be produced by relative increases or decreases of axon-target affinities or by heterochronic advantages of some afferents over others that mature at different times. These could also be depicted in this manner by assuming that the relative size of the structures depicted represents afferenttarget biases in general. Although displacement can also explain parcellation processes, these are not depicted here. They would roughly follow Ebbesson's schema with the added provision that parcellation is not spontaneous, but must be induced by a change in the relative numbers of target synapses with respect to competing axons or changes in axon-target affinities. Even if enlargement of both afferent and target populations during generalized size increase is isometric, there may be limited collateral extent of correlated axon activity such that diffuse overlap of connectivity could not be maintained and axons from the same source would have a better chance of eliminating interdigitate axons from more diverse origins. hood of synapse formation with respect to axons arriving out of synch with cell maturation. As a result heterochronous changes in developmental time schedules for different systems may be a source of developmental bias analogous to differences in size of competing projections. The increased affinity for synchronously arriving axons should have the same effect as relative enlargement of one source or target area with respect to another. Both invasion-like and parcellation-like processes are explainable in this way. What is different about displacement hypotheses is that they propose that all such events are driven by competitive biases between different axon populations and their prospective targets and not by instructional processes such as might be encoded in molecular affinities. The strong form of the displacement hypothesis denies both the possibility of spontaneous axon invasion and also the possibility of spontaneous parcellation. But like the parcellation hypothesis it assumes that the basic molecular affinities between initial connections and their targets are essentially conservative, and if anything, only change in response to prior displacement events, under selection to stabilize a newly adaptive circuit against the regressive influences of competing biases. What is missing from both invasion and parcellation theories is a cause. Displacement theories introduce cause in the form of regressive processes (e.g., cell death or reduction of a peripheral sensory or motor system) or differential growth processes (e.g., unequal or hyperplasic neuron production, expansion of some peripheral organ, or heterochronic change in maturation schedules for different structures). Finlay et al. (1987) suggest that regressive events during development, such as cell death and axon retraction, may account for total brain size variation, the elaboration of specialized sensory, motor or cognitive adaptations, and allometric dispro- 680 TERRENCE W. DEACON portions of specific systems during brain evolution. Widespread cell death appears to be a normal developmental mechanism for sculpting cell populations of interconnected structures. To a limited extent cell death may be exaggerated or eliminated by variations in afferent populations or efferent associations. These effects are, however, buffered in systems with multiple afferent sources and efferent targets, and so can be expected to be most significant in systems with highly limited connectional relationships. Neural populations of peripheral sensory and motor projections are generally entirely dependent on peripheral structures as afferent sources or efferent targets, respectively, and provide the most notable examples of variation in cell death. A number of the changes in CNS organization in response to the evolution of novel sensory organs or motor systems may thus be the result of such a sculpting process. Wilczynski (1984) reviews evidence for the neural reorganization of CNS cell populations and connections in response to some major vertebrate sensory and motor specializations (e.g., auditory, electrical and infrared reception) that show relatively subtle differences centrally in response to major changes of the periphery. Despite homoplaseous peripheral changes, central reorganization often recruits homologous systems for similar perceptual processes. He argues that the interlocking of peripheral and central reorganization in these cases arises out of competitive developmental processes that match peripheral functional requirements to central functional predispositions and match cell populations to one another. Although there may be major changes in cell number in peripherally specialized nuclei as a consequence of cell death there appear to be no "cascading" effects on cell death throughout the remainder of their functional connections within the CNS. The main point of the cell death hypothesis proposed by Finlay et al. (1987) is to account for quantitative allometric changes in the brain and brain structures. However, there are a number of reasons why cell death is unlikely to be a significant fac- tor in major allometric changes. First, in order to play a significant role it must be able to account for at least a major part of the many thousandfold differences in brain size. Small brains are simply not analogous to large brains that have experienced 99% cell death. The role of cell death is clearly limited to secondary "fine tuning" of independently developed functionally interdependent systems (although it may reach 80% in peripheral receptors). Second, as compared to peripheral systems, the evidence suggests that the total amount of cell death is relatively small in most forebrain structures, even if peripheral structures relaying information to them are significantly reduced (Rakic, 1988). This probably correlates with the fact that forebrain structures receive afferents from and send efferents to diverse cortical and subcortical structures rather than just one, as in peripheral structures. The cell death reported in areas like cortex appears to be associated with cells maintaining transient synapses during early phases of development that may serve a preliminary organizing role for later stages. If there was significant cell death in the normal development of cerebral cortex it would have to be relatively uniform because of the remarkable uniformity of cell numbers per area in all areas and all species. The initial production of neurons (or the initial production of "ontogenetic units" with fixed neuron production patterns) is probably much more important in determining populations in most structures. Finlay et al. (1987) also point out the possible significance of heterochronous maturational processes for both cell death and connectivity patterns. They argue that earlier maturation or delayed maturation of areas may introduce competitive biases in normal axonal competition. Since some competitive processes may extend for only a few days, significantly delayed or premature connections may be left out of the competition, with cell death or connectional replacement resulting. Although Gould (1977) argues for the widespread presence of heterochrony in other systems (e.g., somatic growth and puberty) there is little evidence concerning variance of mat- RETHINKING MAMMALIAN BRAIN EVOLUTION uration schedules in the mammalian nervous system at this time. However, time scale effects may be significant in mammalian brain evolution. The maturation of a small mammal brain may be completed within the space of weeks whereas that of a large brain may take many years. This means that the absolute time scale of competitive-regressive events during maturation can differ enormously despite the likelihood that at the synaptic and cellular scale the trophic processes that underly these effects are the same for all mammal brains. The prolongation of these events in larger species might affect variability, degree of differentiation or sensitivity to extrinsic influences. In non-mammalian vertebrates where neurogenesis may persist throughout the lifespan heterochrony may be a more significant factor. In previous papers I have also proposed that axonal competition and other regressive processes play crucial roles in brain evolution (Deacon, 1984, 19886, 1990c), but I have focused largely on the possible influences of size relationships. If the determination of initial cell number in most structures takes place prior to major axonal invasions, the major role of competitive and regressive processes must be the subdivision of these neurogenetic fields with respect to each other. Even though no cell death nor substantial cell saving may result from increases or decreases of specific afferents or efferent targets of a multiply connected structure within the CNS, such changes can substantially alter local axonal competition processes. Rather than axonal competition determining the size of brain structures via cell death (probably only significant for peripheral structures), the relative sizes of interconnected brain structures should be a major determinant of patterns of axonal connection. Allometric effects are probably the most common sources of bias, given the enormous range of brain sizes and the great ranges in the relationships between central and peripheral systems. These effects are not limited to unusual reorganization events. Deviations from isometry with phyletic size increase is the rule among brain structures as in peripheral organs (e.g., 681 Armstrong, 1985; Campos and Welker, 1976; Deacon, 19886; Gould, 1975; Passingham, 1975; Sacher, 1970; Stephan, 1969). The systematic differentials in neuronal production in different structures in brains of different sizes should determine correlated differences in structural parcellation throughout. For example, the regular increase in proportion of visual association cortex with respect to visual koniocortex in brains of increasing size may reflect a growing competitive disadvantage for primary projections in the recruitment of cortical space determined by a growing disproportion between the retina and its potential thalamic and cortical targets. As we examine species differences in neural organization we should expect to see certain necessary correlations between changed connection patterns and the allometries of the various structures involved. For example, in cases where an invasion event is suspected to have taken place one would expect to find some corresponding deafferentation of the new target by a former projection source that has regressed in size with respect to its target, or some unusual size increase in the new source structure relative to its normal target, or significant regression of its normal target. In cases where loss of connection is suspected either cell death in the source or target or, alternatively, displacement by a projection from a disproportionate competing afferent source would be expected. Failure to find these correlates either in adult brains or during development would falsify a displacement interpretation. Displacement hypotheses are falsifiable in ways that parcellation or invasion hypotheses are not because a displacement explanation is an account of a mechanism not merely of a change in structure. The displacement hypothesis is essentially an extension of well studied mechanisms of developmental axonal plasticity. The production of topographic functional and connectional organization within a developing area induced by reduction or over-exaggeration of input from some outside source is the limiting case for developmental displacement. The extension of this concept to incorporate allometric influences as a 682 TERRENCE W. DEACON major source of bias on major projection patterns completes the synthesis of allometric effects, neogenetic processes and developmental processes. Displacement interactions can also conceivably account for true invasions of axons into targets that even exuberant projections would not otherwise contact. It is not necessary to assume any changes in the actual affinities of axons for their targets, only the reduction of the specificity requirements for target affinity. This may occur under some extreme conditions. In an earlier section it was noted that the initial target specificity of many neural connections is significantly underdetermined. This has been best documented for cortical afferents and efferents but has also been noted widely in the developing nervous system. As a result, initial axonal projections invade a multitude of diffuse targets and may establish numerous transient synapses that will later be eliminated. There probably are some predetermined affinity gradients involved because these initial projections are far from entirely random. Edelman (1987) has argued that this initial affinity between axons and potential target cells is the result of specific cell surface molecules that exhibit a range of interaction or "recognition" strengths (analogous to immunological binding relationships). In order to produce distinct connections these affinities need not be highly specific so long as there is either a significant threshold difference between nearby potentially competing projections or a means for dynamic parcellation of relatively nonspecific projections, as is found in cortex. Extremely weak axon-target affinities can likely only exhibit themselves when all competing affinities are essentially eliminated or when extremely strong competitive biases are introduced. Elimination of alternative stronger affinity competitors can occur if the majority of normally occurring transient and permanent afferents to an area are eliminated, or if a normal target is essentially eliminated, forcing axons to compete for alternative low affinity targets. Strong biasing may also occur if a weak affinity afferent source becomes disproportionately large with respect to both its normal target and nearby low affinity targets. An experimental example demonstrates this possibility. Frost and Metin (1985) and Sur et al. (1988) have demonstrated the possibility of inducing optic afferents to project to inappropriate thalamic nuclei and thus relay inappropriate sensory information to their cortical target areas. To accomplish this in a developing rat they destroyed all the normal targets of the optic projections (including lateral geniculate, superior colliculus and visual cortex) and additionally deafferented another thalamic nucleus {e.g., either the ventrobasal or the medial geniculate nucleus) by cutting ascending (spinothalamic or tectothalamic) fibers. One of these procedures is diagrammed in Figure 15. Despite the fact that the misrouted connections innervate anomalous thalamic targets which project to non-visual cortical areas, cells in these areas exhibited response properties appropriate to visual cortex. This demonstrates that fundamental rewiring is achievable by displacement and that the new connections thereby established can differentiate their targets appropriate to their new functions. However, it may be significant in these cases that the alternate thalamic and cortical targets are homologous with the normal targets at some level. Similar natural experiments appear to be exhibited by different breeds of Siamese cats. These cats all have abnormal routing of ipsilateral projections to the contralateral lateral geniculate with the result that the visual field maps are misaligned. When the lateral geniculate projections reach the cortex they are dealt with in one of two ways depending on the breed: they are either inactivated so as not to interfere with the remainder of the map or form an isolated independent map that is inserted adjacent to the otherwise normal map (Kaas and Guillery, 1973; Guillery, 1974). What factors bias the axonal competition toward one or the other option are not known. Analogous competitive processes may underly the evolution of new cortical areas. Simple invasion is astronomically unlikely because it can only occur when there is a significant loss of target affinity in one set RETHINKING MAMMALIAN BRAIN EVOLUTION of axons and simultaneously a significant increase in affinity for that same target area by other axons that have also simultaneously lost affinity for their own target. Each of these conditions involves an independent mutational event that alters the respective cell surface molecules or causes certain whole classes of cells to die. In contrast, invasion by displacement need not involve any changes in affinity or significant cell death. The only conditions required are either significant allometric disproportions between areas or the elimination of some target area or the elimination of some projection as a result of some degenerative event in evolution. These conditions are probably not at all uncommon in the course of evolution. Significant allometric changes in proportions between different structures and projection systems is the rule in all mammalian and nonmammalian lineages where brain size has changed by many orders of magnitude. Such a principle may account for the parcellation trends in neocortical areas seen in larger mammalian brains. Som DISPLACEMENT THEORIES OF CORTICAL EVOLUTION: FOUR EXAMPLES Multiplication of cortical areas and their differential allometry The enlargement of the entire cortical mantle with increasing brain size may influence cortical differentiation indirectly by altering competitive interactions among cortical afferents. There may be limits to the size of a single projection field determined by the number of specific afferents that are available or by intrinsic functional constraints. If changes in the size of the cortical mantle and different thalamic nuclei are not isometric in the course of evolution then there may be correlated changes in the relative size of corresponding projection fields. Changes in proportion may also be influenced by network allometry influences that impose functional costs on enlarging areas. Such constraints might contribute to the break-up and duplication of cortical fields in brains of increasing size. The multiplication of areas and the differential expansion of some Rerouting of connections after fetal lesions of visual targets maintains visual function in aberrant targets. FIG. 15. Misrouting of axons by target elimination is demonstrated by experiments in which the normal targets of one projection are eliminated by lesion in fetal development and the projections to a different (serially homologous) target are prevented from forming. In the case depicted here from Frost and Metin (1985) the targets of the optic nerve—the lateral geniculate nucleus of the thalamus [LG] and the superior colliculus [SC]—and the target of the lateral geniculate nucleus—the visual cortex [Vis]—were damaged by fetal lesions as were the ascending somatic sensory afferents of the medial lemniscus which would normally synapse in the ventrobasal nucleus of the thalamus [VB]. Consequently, the optic fibers were thereby induced to invade the ventrobasal nucleus of the thalamus. The otherwise normal projection of this nucleus to the location of somatic cortex induced this area to behave as though it were visual cortex. 684 TERRENCE W. DEACON cortical areas with respect to others may visual association projections (Rakic, 1988). also be influenced by the total size of the The relative negative allometry of the proentire cortical mantle with respect to the jection nucleus of vision (the lateral genicsizes of other brain structures that are con- ulate nucleus) with respect to the volume nected with it. This indirect influence is of the corresponding visual associational suggested by the predictable allometric nuclei (the pulvinar complex) as well as with scaling of the sizes of cortical areas with respect to the rest of the thalamus (Armthe total size of the isocortex across many strong, 1979; Hopf, 1965; Stephan el al., species (Passingham, 1975; Deacon, 1988*). 1981) lends further support to a displaceProbably the most significant determin- ment explanation for this evolutionary ing factor in such cases is the relative size trend. Figure 16 diagrams the major feaof the afferent projection as compared to tures of this hypothesis in comparison to its cortical target zone. A cortical target deafferentation experiments. Deafferenarea that has expanded with respect to its tation as a result of adaptational loss or afferent projections is in some ways anal- reduction of a peripheral sense organ—as ogous to a cortical area with a reduced in blind cave dwelling species, or to a lesser afferent projection. Either should produce extent in fossorial or nocturnally speciala decreased density of adjacent correlated ized species—should also produce this sort inputs which may impair the ability of spe- of effect in cortical areal architecture, but cific inputs to successfully out-compete and in a brain that is unusually small for this eliminate diffuse inputs. In the case of pattern in that modality. Studies of such depleted afferents (Rakic, 1988) the size of naturally deafferented species has demthe differentiated area is reduced and space onstrated reduction of cortical represenis given up to neighboring areas. Neigh- tation of these sensory areas but the issue boring cortical areas would also face the of projection area to association area ratio same difficulty. Parcellation of afferent has not been investigated. projections to form duplicate adjacent proIt is possible that the process of area subjections may thus be a result of reaching division may be a gradual evolutionary some threshold of competitive instability event in areas with relatively diffuse topodetermined in part by the size of the affer- graphic organization. Area boundaries may ent map (which may itself be matched in not be discrete and connectional differsize to its peripheral representation by cell ences may exhibit a gradient-like organideath) and in part by the independently zation in these cases. Such incipient area growing information processing costs of divisions should be more likely in associanetwork allometry within the cortex. tion areas lacking clear sensory or motor The increasing proportion of association topographic organization and we should cortex to projection cortex that correlates expect to see increased individual variation with increasing brain size could reflect pro- in these areas if this is the case. This patgressive competitive disadvantages for tern should contrast with that of cortical direct peripheral projection systems in areas that map topographically organized some modalities, both in recruiting tha- representations of some peripheral modallamic targets and in recruiting cortical tar- ity. In these cases area differentiation gets via these thalamic projections. This should tend to be more discrete and prewould follow if the proportion of periph- dictable. The border between visual area eral axons to central axons competing for 17 and 18 and between 18 and 19 is easily targets declined with size. This kind of tar- distinguishable and correlates with funcget expansion would have parcellation tional map boundaries but the multiple effects analogous to partial deafferenta- retinotopic maps within area 19 of the tion. Visual deafferentation experiments monkey cortex are not easily correlated in monkeys have demonstrated both a with any architectonic borders. The apparreduction in striate cortex area and an ent tendency for middle level cortical assoexpansion into this territory by adjacent ciation areas to exhibit the greatest level RETHINKING MAMMALIAN BRAIN EVOLUTION FIG. 16. Displacement theory of association area expansion is depicted for visual areas in two hypothetical brains of different sizes but receiving input from eyes that differ little in size. The geniculo-striate pathway is depicted by solid black arrows and dark gray targets and the tecto-pulvinar-extrastriate pathway is depicted by dashed gray arrows and light gray targets (assuming homology of the pulvinar and lateral posterior nucleus). In the expanded brain of B there has been an expansion of the thalamus and the cortical target field potentially available for both projections but because the direct retino-thalamic projection is not significantly larger it is not capable of recruiting a correspondingly larger LGN from the expanded thalamus and may also be at a competitive disadvantage in competition for space within the superior colliculus as well with respect to other possible competing inputs. However, the size of the afferents to the pulvinar are appropriately enlarged and recruit a large portion of the thalamus. The consequence for cortical parcellation is that the geniculo-cortical afferents are at a disadvantage in the competition for cortical territory with respect to pulvinar afferents and so the striate cortex will occupy a reduced proportion of the entire visual projection field. The additional 685 of subdivision and functional diversity from species to species may be a correlate of the relative fluidity of these divisions. If the tendency for cortical circuits to subdivide and differentiate their cortical targets with respect to one another in development is exaggerated by brain size increase and brain size increase is correlated with the evolution of increased body size, then neither selection for augmented specialized functions nor selection for generalized brain size increase (and increased general intelligence) needs to be involved in order to explain cortical enlargement and complexincation. Multiplication of cortical areas might be accounted for, not as augmentation of function, but as a response to a growing size differential between peripheral projections and centrally originating projections as well as a response to deterioration of integration and processing efficiency caused by the concomitant reduction of connectivity in a larger cortex. Advancement of function is not necessary to explain the multiplication and differentiation of cortical areas. Functional adaptation is not precluded, but to demonstrate it requires more than demonstrating an increase in cortical areas and differentiation of functions within those areas. These other correlates of size must be "subtracted" before a proper assessment of functional advancement can be made. Nonetheless, with the addition of duplicate areas or with the differentiation of functions into independent component processes, new possibilities for specialization arise that could not coexist in a common area. This must certainly be a rich source for "preadaptations." Once adaptive alternative connection patterns are established by whatever means there may be selection for changes in axon affinities and other biasing factors that limit expansion of the pulvinary projection field may further induce its parcellation because of increased regional differentiation of correlated activity, but also possibly because the information arriving in the pulvinar may include inputs that reflect the effects of partially displaced retino-tectal projections. 686 TERRENCE W. DEACON variability. Because of these developmental biases, parcellation patterns will become increasingly resistant to reversions, even if the conditions that originally induced parcellation are undermined. This suggests that size-induced parcellation patterns may persist even if brain size decreases in subsequent lineage. Retention of cortical features consistent with a much larger brain has been documented in a number of dwarf species (e.g., Warren and Carlson, 1986). This suggests that there may be functional costs associated with brain size reduction in evolution. The possibility for irreversible changes and corresponding asymmetrical selection against size reduction brings us full circle to a possible progressive or directional tendency in brain evolution. Laminar segregation of afferents: Implications for areal parcellation and the origins of cortex Connectional patterns between cortical areas appear to parallel the cytoarchitectonic differentiation of cortical areas. Because of this regularity they may provide some insights into the connectional displacements, invasions and parcellations that constitute area differentiation in evolution. Tracer studies of corticocortical connections in monkey brains have revealed characteristic laminar origin and termination patterns that seem to be generalizable to many if not all regions of cortex (Barbus, 1986; Deacon, 1985; Galaburda and Pandya, 1983; Jones et al., 1978; Maunsell and Van Essen, 1983; Primrose and Strick, 1985; Rockland and Pandya, 1979; Tigges et al., 1973, 1977). In general, connections that originate from association areas and project to areas more specialized for a peripheral sensory or motor function tend to originate largely from cells in layer V of cortex and terminate in layers I and VI. Connections that originate from specialized (e.g., primary) sensory and motor areas and project to association areas tend to originate largely from cells in layer III of cortex and terminate predominantly in layers III and IV (see Fig. 17). There are also subtle gradation differences that also seem to respect the general "level" of cortical area. Both origin and termination patterns are more diffuse across lamina in association areas (Barbus, 1986; Deacon, 1989a). Similar laminar connection patterns have also been identified in some areas of cat (Bullier et al., 1984), tree shrew (Semsa et al., 1984) and rat cortex (Deacon et al., 1989), but there is too little information for non-primate species to be sure of its generality. The consistent association of termination patterns with the architectonic and functional gradient between association areas and sensorimotor areas clearly indicates that this hierarchy, which has been the central feature in all additive theories of cortical evolution, must also be accounted for in terms of parcellation and displacement processes in evolution. Elsewhere (Deacon, 1989a) I have referred to these reciprocally directed pathways as centrifugal (limbic-association-sensory/ motor cortex) and centripetal (sensory/ motor-association-limbic cortex) projections because they are oriented with respect to areas specialized for peripheral information at the one extreme and areas concerned more with internal states of arousal at the other (see Fig. 18). This hierarchic chain of cortical areas within each functional modality increases in number of areas and corresponding synaptic links as brains enlarge in evolution, yet replicates the same systematic pattern of laminar connectivity with each addition. A number of researchers have linked this asymmetric reciprocal connectivity pattern to Sanides' evolutionary sequence of cortical differentiation (e.g., Barbus and Pandya, 1982, 1987; Galaburda and Pandya, 1983; Pandya and Yeterian, 1985). This asymmetry is presumed to be explainable as a terminal addition process whereby new areas are always connected to their immediately adjacent ancestral area by one sort of laminar connection pattern and are reciprocated by its complement. Despite the rejection of Sanides' theory on a number of grounds, the correlations it suggests must be accounted for. With the repudiation of theories claiming terminal addition or terminal differentiation of cortical areas, we are forced RETHINKING MAMMALIAN BRAIN EVOLUTION 687 parcellation of cortical laminar connectivity in the process of areal parcellation of isocortex corticocortical laminar connectivity before parcellation paralimbic/assn. • • • • A koniocortical • corticocortical laminar connectivity after parcellation FIG. 17. Laminar segregation of corticocortical connections due to functional parcellation of cortical areas is depicted on the assumption that both the ancestral and developmentally prior state are an undifferentiated laminar termination pattern. The hypothesized undifferentiated state is depicted in the upper figure as a single cortical area with intrinsic connections. The subsequent loss of selected classes of connections with area parcellation is depicted in the lower figure. Note that the culled connections are asymmetric with respect to their directional orientation. Possible sources of competitive bias that might drive this asymmetric parcellation during development are discussed in the text. to explain this correlation between architectonic gradients, asymmetrically patterned reciprocal connections, inverse maturational gradients, and the apparent functional hierarchy of cortical areas in terms of competitive biases and displacement processes in cortical development. At the present time there is no developmental information concerning corticocortical laminar differentiation processes. Nonetheless, speculation concerning the possible mechanisms involved can be concentrated on a few plausible factors. For corticocortical connections within a cortical area there does not seem to be this level of laminar specification (Rockland and Pandya, 1979). The differentiation of a new cortical subdivision out of a single ancestral area must therefore correlate with a loss of projections to certain cortical lamina. Furthermore the loss is different depending upon whether the projection is in the centrifugal or centripetal direction. Since the appearance of a new cortical division must be a consequence of the competitive parcellation-displacement processes, these systematic connectional losses likely correlate with competitive asymmetries between different afferent populations. This suggests that we should look for corresponding biases, either in terms of heterochronic, allometric or functional differences, that distinguish association areas from specialized sensory/motor areas. In fact, all three possible sources of bias can be identified and are probably not independent. Another important clue concerning the particular cortical lamina that distinguish these different cortico-cortical projections comes from the finding that different classes of thalamo-cortical projections also appear to segregate according to terminations in these same lamina. Multiple thalamic nuclei project to each cortical area but tend to terminate in different lamina within the same area. It appears that principal thalamic projection nuclei, whose projections are generally limited to a single architectonic area, tend to terminate in columnar fashion within layers III and IV, usually coinciding with the distribution of granule cells in those layers (Diamond, 1982; Frost and Caviness, 1980). Intralaminar thalamic nuclei, which exhibit relatively non-specific projections to many cortical areas, tend to terminate in layer VI (Frost and Caviness, 1980; Herkenham, 688 TERRENCE W. DEACON centripetal centrifugal FIG. 18. Centrifugal and centripetal corticocortical projections are depicted on idealized flattened maps of the cerebral cortex of one hemisphere. Areas depicted in darkest gray are koniocortical areas or specialized motor cortex, those in lightest gray are association cortex, and the white perimeter represents limbic cortex. The arrows represent multisynaptic pathways from area to area extending either from koniocortex (or motor cortex) to intermediate association cortex to paralimbic association cortex to limbic cortex (centripetal) or from limbic cortex to paralimbic association cortex to intermediate association cortex to koniocortex (or motor cortex). The terms centripetal and centrifugal are chosen not because of their spatial connotations (which may be somewhat confusing in this depiction) but because of the orientation of these projections with respect to the gradient between areas specialized for peripheral systems and those representing regulation of internal 1980; Rausell and Avendano, 1985). Other nonspecific nuclei that project to many areas within the same modality and midline "limbic" nuclei, which also exhibit widespread paralimbic cortex and association cortex projections, tend to terminate in layer I (Diamond, 1982; Friedman et al., 1987; Frost and Caviness, 1980; Rausell and Avendano, 1985). The non-specific and limbic nature of thalamic projections to layers I and VI and the specific projections to layers III and IV can be interpreted as functionally analogous to their counterparts among cortico-cortical projections in a number of ways. Middle layer projections appear always to introduce information associated with a source that is more directly connected with the peripheral nervous system than are their target, whereas deep and superficial layer connections appear to convey information that ultimately derives from systems involved more with the regulation of internal state, as well as attentional and emotional arousal (Deacon, 1989). Two important heterochronic maturational factors differentiate cells and axons in these cortical lamina. These correlated heterochronic differences may account for which lamina tend to be associated with which afferents by virtue of their biasing influence on axonal competition. Neurons occupying positions that would be superficial to layer II and deep to layer VI in the adult brain mature before the cells of the cortical plate and form a single primordial cortical layer. Cells in the outer layer called Cajal-Retzius cells exhibit two large "extraverted" dendrites that extend up toward the pial surface and laterally, whereas cells in the deep layer called Martinotti cells are of a distinct bipolar shape with dendrites extending more superficially and deeper. Cortical plate cells migrate into position in an inside-out pattern between these two cell layers. The earliest cortical plate cells to mature are the deep layer V and VI pyramidal cells and the very last cortical plate cells to mature are the most superficial pyramidal cells of layers II and III and the granule cells of layer IV. Prior to the appearance of the cortical plate neurons both primordial cell types appear to establish transient synapses RETHINKING MAMMALIAN BRAIN EVOLUTION with early afferent projections (Marin-Padilla, 1978). Although there is some disagreement on the ultimate fate of these early maturing cells most argue that they are eliminated by programmed cell death in most isocortical areas (although they appear to persist in entorhinal cortex and in paralimbic isocortex in small species, and in all cortical areas in cetaceans; see next section). Although it is not known what structures give rise to the afferent projections that synapse on the transient cells above and below the developing cortical plate, it is reasonable to assume that they arise from the same structures that will later innervate the corresponding deep and superficial lamina in the adult. The fact that the thalamic afferents terminating in layers I and VI are nonspecific projections and do not appear to respect cortical boundaries (Cavinessand Frost, 1980; Diamond, 1982) may reflect their arrival prior to cortical plate afferents that subdivide the cortex into discrete functional regions. If the transient cells to which these early axons contact are later eliminated, these axons may be displaced onto adjacent pyramidal cell dendrites in the deepest and most superficial lamina of cortex (see Fig. 19). Since different target cells within the cortex mature at different times and different projections arrive at different times, temporal correlation may play an important role in biasing laminar connection patterns from both thalamic and cortical sites. Early maturing cells located deep and superficial to the cortical plate may correlate with the early maturing projections and late maturing small cortical plate cells in layers III and IV may correlate with relatively late maturing projections. Differences in the relative maturation times of cells and axons from one cortical area to another might additionally contribute to areal differences in laminar organization. Another possible heterochronic bias that may influence the asymmetric directionality of these projections can be discerned in the differential myelination of thalamocortical fibers from principal thalamic nuclei projecting to these areas. Since myelination appears to precede from specialized areas to association areas the compe- 689 tition for synapses in the middle cortical lamina may be biased by earlier myelination of areas that are more peripherally specialized. An allometric bias is reflected in the expansion of association areas relative to sensory/motor specialized areas in larger brains. As noted above, this suggests that primary projection areas, which are more directly linked to peripheral systems, are competitively constrained by the size of their afferent projections, whereas afferents to association areas have no such extrinsic constraints. This may also be the reason these areas appear to exhibit less clearcut cytoarchitectonic divisions. The gradient in architectonic specialization is one of the primary bases for Sanides' argument. Functional differences are also consistently correlated with this gradient. For example, neurons in striate cortex appear to be precisely "tuned" to specific, highly localized stimulus attributes and are organized according to precise retinotopy whereas neurons in inferotemporal visual association areas, at the extreme opposite end of the hierarchy, seem tuned to global stimulus attributes and exhibit very large receptive fields with no obvious topographic organization. This is undoubtedly also a correlate of the relative directness or indirectness of their respective retinocortical afferent circuits (Kaas, 1989). At the sensory end of the spectrum of areas input from the periphery is highly variable and functional correlation is only exhibited over very short distances, whereas at the association end input is primarily limbic and likely highly intercorrelated over relatively larger distances. Since functional specialization of cortical areas can be significantly affected by sensory experience during early development it is almost certain that differences in the correlation of afferent signals among adjacent axons play a major role in determining which connections persist into adulthood. Finally, the issue of network allometry should be considered. Given the fact that break-up of previously integrated functional areas effectively distributes processing across a number of areas, functioning to some extent in parallel, cortico-cortical 690 TERRENCE W. DEACON Pattern of isocortical neurogenesis and probable programmed cell death x prior to the cortical plate early invasion by cortical plate cells addition of cortical plate cells to external layers VI probable elimination of cells outside the cortical plate and specialization of granular layer Hypothesis to account for development of laminar specificity of cortical afferents VI early non-specific thalamic (+?) projections displacement of non-specific projections from eliminated cells to cortical plate cells and invasion of specific thalamic projections FIG. 19. A theory of the displacement processes involved in laminar parcellation of cortical afferent connections and a possible explanation of their relationship to neocortical origins. The upper figure diagrams the events of corticogenesis assuming programmed cell death of cells that precede the formation of the cortical plate (it is also possible that some of these precursor neurons are converted into neurons with different morphologies in the mature cortex). Note that neurons forming the cortical plate migrate into position between the two groups of cortical precursor cells above and below it and deposit in the uppermost layers of the developing cortical plate. The lower figure depicts a displacement theory for the laminar segregation of cortical afferents during cortical development. It is hypothesized that axons from early maturing non-specific thalamic nuclei and possibly early maturing limbic areas establish synapses on the two populations of cortical precursor cells and maintain them as cortical plate neurons are added and the two cell groups are forced apart. After the formation of the cortical plate selective cell death of the precursor cells forces the axons attached to them to seek alternative synaptic contacts. They are displaced onto the dendrites of cortical plate neurons of the same lamina. These axons may have a competitive advantage because of their numbers and functional maturity compared to the later arriving principal thalamic afferents that target cortical plate cells in middle layers. The two independent populations of neurons, superimposed by the migration of the cortical plate cells, with different maturation schedules and distinct classes of afferents, suggest the possibility that they derive from independent phylogenetic origins—the precurser cells from the dorsal cortex and the cortical plate cells from the dorsal ventricular ridge of a reptilian ancestor. RETHINKING MAMMALIAN BRAIN EVOLUTION connections should be competitively selected during development that maximize intercorrelated function and most efficiently distribute processing demands throughout the network. Such an interpretation is suggested by the "counter-current" organization of these cortico-cortical connections (see Deacon, 1989). This analysis of laminar maturation and connectional differentiation suggests an alternative interpretation for the origins of mammalian isocortex that combines both the equivalent cell hypothesis and invasion by displacement. My suspicion (also suggested in Marin-Padilla, 1978) is that these transient cells are the homologues of the cells of the ancestral dorsal cortex of reptiles and that the cortical plate cells represent a phylogenetically later intrusion (following the equivalent cell hypothesis) perhaps from ancestral dorsal ventricular ridge positions. The death of the reptilian dorsal cortex homologue cells in mammalian ontogeny induces the displacement of their afferents onto the apical dendrites of these recently juxtaposed nonhomologous cells of the cortical plate. The cortical plate cells also maintain their original afferents and thereby establish a novel integration of these ancestrally separated and independent circuits. In this sense the laminar termination pattern of centrifugal pathways links them with the ancestral dorsal cortex system (which has always been linked with limbic cortex) and the laminar pattern of centripetal pathways links them with the ancestral dorsal striatal (dorsal ventricular ridge) system. Cetacean brain evolution In the preceding sections I have referred to a number of general trends in mammalian brain evolution that appear to be strongly correlated with brain size. These include both microscopic and macroscopic features of brains, all of which ultimately have to be understood in terms of biases and constraints that modify developmental processes, and most particularly, axonal competition/parcellation processes. It is therefore important to consider exceptional cases where these correlations do not seem to hold. Understanding what it is 691 about these brains that causes them to diverge from these otherwise ubiquitous trends will unquestionably provide important insights into the causes for the general trends. Some of the most striking exceptions can be found in the dolphin brain (and presumably in all cetacean brains). Many of the unique architectonic features of the dolphin brain have been meticulously documented in a series of recent papers (Jacobs et al., 1971, 1979, 1984; Morgane and Jacobs, 1972; Morgane et al., 1982, 1985, 1986a, b, 1990). These findings concerning the dolphin brain are paradoxical in the context of traditional theories of mammalian brain evolution because they suggest that dolphin brains combine features that are considerably highly advanced with features that are considered quite primitive and conservative (Glezer et al., 1988). The highly advanced features of the dolphin brain are largely macroscopic morphological features, including a large brain size, a high degree of encephalization, a highly convoluted cortex, a high ratio of neocortex to total cortex (and therefore a high ratio of neocortex to limbic cortex), and apparently (although this is difficult to assess accurately) a large percentage of association cortex. Dolphins also are considered to be among the most behaviorally advanced species by many behavioral researchers (Herman, 1980; Wiirsig, 1989; but see critique by Gaskin, 1982). In contrast, their conservative traits are largely microscopic features, including relatively thin and poorly laminated isocortex, essentially agranular (and some would argue, nonexistent) layer IV and therefore no typically definable koniocortical areas, apparent lack of a gigantopyramidal cortex {i.e., architectonically specialized primary motor cortex; although some evidence of this can be discerned in the form of larger layer V pyramidal cells), remarkably thick layer I with respect to the rest of the cortical layers, a well developed layer VI, densely packed layer II, large "extroverted" cells in layer II, poorly defined columnar organization, indistinct architectonic boundaries, and apparent adjacency of sensorymotor projection areas with respect to 692 TERRENCE W. DEACON dorsal view lateral view FIG. 20. Drawing of the dolphin brain shown in simplified form from above and from the side with an indication of the topographic position of the different sensory and motor projection fields. Although the areas depicted are presumed to be the primary projection areas for these modalities, I prefer to reserve judgement on this hornological relationship. Assuming that the indicated areas are representative of the proportion of neocortex occupied by primary areas it would appear that most of the dolphin isocortex is composed of association areas. Limbic cortex is not visible from the lateral view and is a relatively small proportion of the total cerebral cortex as is appropriate for a mammalian brain of this size. Information for this drawing is derived from Morgane et al. (1986a, by. association areas (see Fig. 20). Glezer et al. (1988) are compelled to designate a special "conservative-progressive mode" of cortical evolution to account for this anomalous combination of features. The dichotomy between macroscopic morphological features and microscopic cytoarchitectonic features is undoubtedly significant for understanding this apparent paradox. If gross morphological traits were the only available evidence then the dolphin brain would be ranked along with the human brain as a highly advanced brain. Such traits as the ratio of neocortex to limbic cortex follow expected allometric predictions of a brain the size of a dolphin brain. It is in fact even more convoluted than might be expected from a terrestrial brain of such size, but this can probably be explained on the basis of its relatively thinner cortex (compared to terrestrial mammal brains of similar proportions—e.g., primate and human brains). Thus, with respect to the production of initial cell populations the dolphin brain probably shares common mechanisms with all mammals. But the production of cell populations and the parcellation of those populations into distinct functional divisions and architectonic fields occur independently at separate developmental stages. The cortical architectonic parcellation process occurs subsequent to the production of the cortical mantle. The allometric proportions that will determine the proportion of isocortex to limbic cortex and projection cortex to association cortex are already established, but the competitive interactions which will subdivide and specialize these cortical targets have not begun at this stage. We must look to this latter process for a clue to the peculiarities of the dolphin brain. The hypothesis I will suggest to explain these architectonic peculiarities focuses on the agranularity of dolphin cortex. The lack of layer IV granule cells throughout the cortex of the dolphin brain is particularly remarkable because the origin of granule cells is far more ancient than the divergence of Cetacea from the rest of the eutherian stock. Despite the fact that small brains are in general less "granularized" RETHINKING MAMMALIAN BRAIN EVOLUTION (a feature that may in part be attributed to the fact that the size difference between granule cells and pyramidal cells in small brains is relatively slight), even the apparently primitive brain of the North American opossum Didelphis virginiana exhibits a clearly denned and even subdifferentiated layer IV that receives dense principal thalamic afferents (Johnson, 1988; Walsh and Ebner, 1970), so the common eutherian ancestor of cetaceans and other eutherian mammals doubtless also possessed layer IV granule cells. Complete loss of this cell type and cell layer must be considered a rare derived trait. The absence of granule cells is of primary significance with respect to the process of architectonic parcellation of cortical areas. During development of cerebral cortex, it is the competition between invading axons from the major thalamic nuclei that is largely responsible for the specification of topographic maps and establishment of functional and architectonic boundaries. As is well demonstrated by studies of the formation of somatosensory barrels in the rat and ocular dominance columns in the cat and monkey, the terminations of these critical projections and the principal layers in which this competition takes place are layers III and IV, corresponding to the distribution of granule cells in those layers (Jacobson, 1978). Experimental destruction of these thalamic afferents at an early stage (Rakic, 1988) or even elimination of peripheral sensory information to these afferents (Woolsey and Wann, 1976) is capable of profoundly altering the structure of the resultant cortical map and even causing functional-architectonic boundaries to be displaced (Rakic, 1988). Given the developmental importance of this thalamofugal-granule cell relationship for architectonic differentiation, it is clear that elimination of this cell type and displacement of many or all of the thalamofugal projections to other layers and other cellular targets in the developing cerebral cortex of the dolphin brain is bound to profoundly alter all features of its tangential and radial organization. The relatively thick layer I likely results from the dis- 693 placement of thalamic afferents that lack their "normal" primary affinity targets. The presence of unusual "extroverted" cells in layer II whose large dendrites reach up into layer I may also be explainable in this way. These cells are present in fetal mammals brains but are eliminated early in development and are the targets for transient synapses during the early stages of cortical differentiation (see discussion in the previous section). The displacement of axons lacking their principal target into this layer at an early stage of development, prior to the "normal" elimination of these cells, may allow them to persist by establishing permanent synapses with the orphaned principal thalamic afferents. Since columnar organization is established by competitive exclusion processes within layer IV and deep layer III, the lack of clearly delineated columnar organization undoubtedly results from the absence of axonal competition in this layer. The lack of clear architectonic boundaries and the apparently clustered projection areas all reflect this significant reduction of axonal competition processes. However, the presence of differences in relative cell sizes in different layers and differences in the density of pyramidal cells in different areas (e.g., between somatosensory and motor areas, Morgane et al., 1986a), that correspond to similar differences in terrestrial mammals, indicate that these features are probably controlled by factors other than thalamocortical connections, most probably their efferent terminations. These hypothetical effects of granule cell elimination are depicted in Figure 21. Neurological mutant mice that completely lack cerebral cortical granule cells should also exhibit many of these same characteristics. Study of these could serve as an informative test case, although because of the great size difference many of the most unusual features of the dolphin brain might not express themselves so obviously in a mouse. How and why cetacean brains lost their granule cells is a mystery. It is a trait that is probably shared by all cetaceans and so was inherited from their common ancestor subsequent to their divergence from terrestrial mammals. There is some trace of 694 TERRENCE W. DEACON Monkey thalamic afferents Dolphin thalamic afferents FIG. 21. Granule cell degeneration hypothesis of the dolphin isocortex is depicted in comparison with the pattern typical for terrestrial mammals represented by the monkey brain. In the diagram of monkey cortical architecture the droplet shaped cells represent pyramidal cells the small spherical cells in middle layers represent granule cells and other small interneurons and the ellipsoid cells represent small layer II cells. The same shapes are used to depict cells in the diagram of the dolphin cortex with the exceptions that there are no granule cells and some of the layer two cells are assumed to be embryologically retained "extraverted" cells. Principal thalamic afferents that normally would target the granule cell populations in the cortical plate are induced to establish alternative targets in the dolphin cortex in which granule cells are strangely absent. These afferents without normal targets may establish synapses with embryonic cells in layer II that would otherwise be eliminated after cortical plate formation in terrestrial mammals. These retained layer II extraverted cells and the displaced thalamocortical axons cause layer I to be disproportionately thick and layer II to be more densely packed than is observed in terrestrial mammal brains. Middle layers are also prevented from normal competitive parcellation into columnar units, that otherwise would distinguish specialized koniocortical areas. This suggests that dolphin cortical organization is neither conservative nor atavistic, but highly derived. this degenerative process left because immature dolphin brains do exhibit a transient but thin layer IV that disappears by maturity (Garey and Leuba, 1986). It seems unlikely that this loss can be rationalized as an adaptation. Given the total breakdown of cortical differentiation processes that resulted one would presume that this is a costly mutation, although even in the mutant Reeler mouse, with its totally disrupted cortical architecture (but not lacking granule cells), the cortex still functions and allows for adequate perceptual and motor functions. The fixation and survival of this trait in cetaceans as opposed to any other terrestrial lineage may be related to their unusual and relatively complete adaptation to the aquatic habitat. The lack of "granularized" competitors (e.g., pinnipeds) in this niche until much later in mammalian evolution may have been cru- cial to the persistence of this trait. Consider the significance of the regression of many specific sensory and motor systems in these species associated with their aquatic adaptation. They are anosmic, they have significantly reduced visual requirements (and in this regard are comparable to fossorial species and echolocating bats with secondarily reduced visual systems), and they exhibit significant reduction of the distal limbs, shoulder girdle and pelvis (which in terrestrial vertebrates comprise the predominant afferent and efferent representation of the primary somatosensory and motor fields). Although many species have highly developed echolocation systems, this appears far more substantially represented by collicular specialization (evidenced by the immensely expanded and highly differentiated inferior colliculus) than by cortical specialization. All these regressive fea- RETHINKING MAMMALIAN BRAIN EVOLUTION tures appear to coincidentally correlate with the inability of the dolphin cortex to architectonically differentiate. In summary, this exception appears to prove the rule in a rather striking and unambiguous way. The problems of determining whether the dolphin brain is conservative or advanced or conservativeadvanced are irrelevant. The dolphin brain is none of these. It is highly derived. These problems that arose in the analysis at the level of comparative morphology and comparative cytoarchitecture dissolve once we approach the question from the perspective of developmental homologies. Human brain evolution Assumptions about human brain evolution are the ultimate source for many of the misleading ideas that have haunted the study of brain evolution, so it is fitting that the exorcism of these ideas in this paper should conclude with a discussion of the uniqueness of human brain evolution. Two unique characteristics of the human brain stand out as central. The human brain is roughly three times larger than would be predicted for an anthropoid primate of human body size, and human brains are capable of acquiring an unprecedentedly complex and flexible communication system—language. These two facts are undoubtedly linked. Beginning with the issue of human brain size, it is important to find out if this increased cell production follows trends that are typical in other members of the primate order. This can be ascertained by comparing the relative sizes of the various major structural divisions of the human brain with predictions based on trends for primates in general. Initial evidence that there is a deviation from predicted allometries comes from an examination of studies that have used brain structure volumes to construct possible phylogenetic trees for primate ancestry. Two studies, using largely similar data but different methods that control for the effects of brain size (Douglas and Marcellus, 1975; Bauchot, 1982), have concluded that the human brain is more similar to either one of two New World monkeys' brains (woolly 695 monkeys in one case and capuchin monkeys in the other) and one Old World monkey's brain (Cercopithecus talapoin), than to the brains of any other Old World monkeys and apes. It is probably not coincidental that those primates most closely linked with Homo by these studies also represent relatively encephalized primates. When a structure by structure allometric analysis is performed it appears that the human brain diverges from primate trends in a number of striking ways. Based on predictions from primate trends, the cerebral cortex and cerebellar cortex of the human brain are disproportionately large relative to the diencephalon, corpus striatum, brain stem and spinal cord (Deacon, 1984, 1988ft). This is depicted in Figure 22. The production of neurons that constitute cortical structures takes place well before any axonal parcellation processes begin, and therefore, as noted earlier, the increase in cerebral cortex cannot be specific to any particular region of cortex. The increase in radial dimensions of the cortical germinal field and in the number of ontogenetic columns that will differentiate out of it must take place in the human brain prior to neural production within the cortex. The size disproportions between the expanded neocortical target field, the relatively unexpanded population of thalamofugal axons, and the relatively unexpanded efferent subcortical targets of cortical neurons must significantly bias parcellation processes in all these areas during subsequent stages of differentiation. One effect of this is apparent in deviations of relative cortical area dimensions with respect to predictions based on the allometry of these structures in other primates. Some cortical areas appear significantly smaller than expected for a primate brain this size and others significantly larger. For example, the visual cortex appears to scale appropriate to the size of its peripheral input (the retina) and its principal thalamic nucleus, but does not occupy the proportion of cortex predicted for a primate brain of this size. Its peripheral sources are constrained by the small human body size with respect to the large brain size. As a result they do not scale to 696 thaiamocortical parceilation process in a brain with typical primate cortical-nuclear proportions TERRENCE W. DEACON thaiamocortical parcellation process in the human brain with disproportionately enlarged cortex FIG. 22. Schematic diagram of large-scale human brain structure disproportions and their effects on axonal competition processes during human development as compared to development in the absence of these human disproportions. With respect to the predictions based on other primate brains, human cortical structures (including the entire cerebral and cerebellar cortices) are larger than expected with respect to brainstem, cerebellar, diencephalic and telencephalic nuclear structures. Since the cell production processes which determine the gross size of these major morphogenetic fields are completed prior to their parcellation into functional subdivisions it is predicted that these disproportions will result in biased displacement processes. The typical condition is depicted by the three brains on the left. Brains A and B represent the normal developmental stages of cortical axonal parcellation of visual (gray cortex with gray dashed arrows as afferents), somato-motor (black with black arrows) and prefrontal (gray with solid gray arrows) cortical fields in a large primate. The human deviation from this is depicted by the three brains on the right. Brains C and D represent the human developmental stages with constraint of visual and somatic fields by their unexpanded peripheral afferents and displacement by prefrontal afferents producing a much enlarged adult prefrontal area. the level that would otherwise be predicted on the basis of brain size (a brain this big would be expected only in a very very large ape—the "King Kong" null hypothesis of human brain evolution). The competitive limits for these afferent systems are constrained by the size of the peripheral input. Preliminary data suggest that this is probably also the case for auditory, somatic and FIG. 23. A diagram of some of the relative proportions of cortical fields in the human brain as compared to predictions based on typical anthropoid primate trends. The percentages represent absolute deviations from the predictions for a primate brain of human size. Temporal, parietal, and motor area predictions are based on too few data points to be significant, but demonstrate a pattern that is consistent with the findings for other areas and with the displacement hypothesis. The depiction of peripheral structures associated with different cortical areas is intended to indicate that cortical areas with relatively direct representation of peripheral sensory or motor systems are constrained by these afferents or efferents in their competition for cortical representation. Figure taken from Deacon (19906). motor areas as well as for visual areas (Deacon, 1984, 19886; see Fig. 23). The competitive limitation of these projection systems translates into a competitive advantage for other areas not constrained by peripheral afferents, which must inherit the cortical surface area that is left unrecruited as a result. The prefrontal zone appears to be one major beneficiary of this competitive imbalance. It is estimated to be approximately twice the size expected for a primate brain of human proportion (and this translates to six times the size predicted for a primate of human RETHINKING MAMMALIAN BRAIN EVOLUTION 617 FIG. 24. Some predicted connectional consequences of prefrontal enlargement are represented by brain A (typical primate brain structure allometry) as compared to B (human cortical-nuclear disproportion). Displacement theory suggests that the enlargement of the number of prefrontal efferents competing for midbrain targets as compared to diencephalic efferents will bias competition in favor of prefrontal projections which will displace both some diencephalic, limbic and intrinsic midbrain axons from their normal targets. This may lead to the relative dominance of prefrontal outputs over limbic and diencephalic outputs in control of midbrain and brainstem vocalization centers and motor circuits. This may be linked to adaptations associated with language skills and the loss of many stereotypic vocalizations in human evolution. body size; Deacon, 1984, 19886). Prefrontal cortex is not a recipient of peripheral inputs, but of inputs from other nonspecific and polymodal systems of the midbrain and cerebral cortex. It is thus buffered by being synaptically removed from the cascading effects of peripheral bias that affect other systems. It is probably not incidental that Broca's area for speech is contained within this enormously expanded field. This disproportionate prefrontal cortical surface area is a secondary consequence of the initial disproportion of the entire embryonic cortex with respect to its subcortical-peripheral connections. These initial disproportions biased axonal competition for cortical representation in favor of cortical areas whose afferents were not constrained by peripheral systems. But this secondary disproportion of prefrontal areas itself must have other tertiary biasing consequences. Deacon (1990c) notes that efferent projections of this system target limbic cortical structures and a range of midbrain structures. We can expect prefrontal projections to have a significant competitive advantage over other afferents to these areas during development (see Fig. 24). The midbrain targets of prefrontal projections also receive descending limbic cortical and hypothalamic projections and intrinsic projections from the central gray and reticular formation. Many of these prefrontal and limbic cortical targets turn out to play major roles in vocal call production in primates. The displacement of "normal" afferents of these areas and replacement by a larger fraction of prefrontal axons may have significantly altered their function. Deacon (1990c) argues that this may account for the significantly reduced repertoire of stereotypic call types in humans, as well as for the recruitment of some of these systems by cortical areas capable of supporting complex skilled motor programming. The disproportions among cortical areas and the relative reduction of thalamocortical as opposed to corticocortical axons undoubtedly also played a role in altering cortical functions, some of which are related to the human language capacHuman brain evolution cannot be conceived in the terms of a conservative-progressive scheme of mammalian brain evolution. Our brains are not at the pinnacle of any evolutionary trend. Rather the human brain is an unusual divergent case. The extreme disproportion of human brain size with respect to the human body size with respect to other primates and mammals is only a surface manifestation of a complex allometric reorganization within 698 TERRENCE W. DEACON the brain, and is unlikely itself to be the crucial trait under selection in human evolution. It is not just the increase in cortical complexity nor the increased relative size of the whole brain but the correlated reorganization of underlying neural circuitry that is probably most significant to human uniqueness (see also Holloway, 1979). Because the data that we possess concerning the human brain are still necessarily limited to morphological information and notably do not include detailed connectional data (due to the invasive nature of present tracer techniques) direct verification of these hypothetical reorganizations will have to wait. However, our understanding of the processes that must underly development of a brain with the allometric characteristics of the human brain can be further augmented by continuing investigations of the relationships between allometric and developmental processes shared by all mammals. The details of human brain evolution are still largely obscure. The hypotheses presented here are based on a massively incomplete set of data. And yet the basic underlying logic of allometric change and axonal displacement processes during development has provided an important new window through which to view these data and an indispensable guide to the gathering of subsequent information about human brain structures and human development. Conclusions Understanding the evolutionary ancestry of the brain's organization is not merely an academic exercise. It is crucial to the study of its basic functional processes as well. Few if any brain structures initially evolved their present form precisely for the purposes they now serve, and many current brain systems may be the result of lucky syntheses of previously separated circuits or else the result of fortuitous degenerative events. Because the brain was not predesigned for its current adaptations the strategies employed in its operation will not likely yield to a purely functional physiological analysis. More importantly, an understanding of the predispositions and constraints inherited from past adaptations and developmental strategies can lead us beyond a merely superficial understanding of function to appreciate some of the deeper fundamental organizing principles shared by all features of the brain. Neither the study of mammalian brain evolution nor even the study of human brain evolution is limited to merely theoretical exploration. We currently have access to experimental tools that are adequate to the task of analyzing the neural developmental processes that underly, canalize and constrain brain evolution, and are capable of gathering the sorts of comparative anatomical evidence that can elucidate the variety of ways these processes have been expressed in evolution. This is an invaluable complement to other areas of the neurosciences that are rapidly building a database of comparative physiological and behavioral information. Our failure to immediately grasp the significance of these data for brain evolution has largely been the fault of the unrecognized influence of some very old notions about evolution, the nature of mental processes, and the place of humans in some cognitive scala naturae. The displacement hypothesis has led me to propose four highly speculative explanations of some major problems in mammalian brain evolution. But displacement theory does not depend on the correctness of these particular interpretations. In fact, it provides means to falsify them if they are incorrect. The theory clearly requires that patterns of brain evolution be explained in terms of the biasing of competitive developmental mechanisms and suggests numerous possible candidates for biasing influences that are likely involved: including (in order of likely importance) allometric relationships, cell death, heterochronous changes in maturational events and changes in molecular affinities between cells and axons. The correlates of displacement processes that are postulated to account for an evolutionary change must be physically exhibited by the developmental processes that construct living brains. If they are not observed then a displacement explanation must be rejected or modified. The examples presented in this RETHINKING MAMMALIAN BRAIN EVOLUTION 699 the hominoid thalamus. I: Specific sensory relay nuclei. Am. J. Phys. Anthrop. 51:365-382. Armstrong, E. 1983. Relative brain size and metabolism in mammals. Science 220:1302-1304. Armstrong, E. 1985. 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