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Grossman et al., 1 A Brain Adaptation View of Plasticity: Is Synaptic Plasticity An Overly Limited Concept? Grossman?, Churchill, Kleim, Bates, Greenough? AUTHOR LIST 1Beckman Institute, 2Neuroscience Program, 3Medical Scholars Program, 4Departments of Psychology, 5Psychiatry, and 6Cell and Structural Biology, University of Illinois at Urbana-Champaign, Illinois, USA. 7Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Alberta, Canada. Address correspondence and reprint requests to: William T. Greenough, Ph.D. Beckman Institute 405 N. Mathews University of Illinois at Urbana-Champaign Urbana, IL 61801 Phone: 217-333-4472 Fax: 217-244-5180 Email: [email protected] Grossman et al., 2 Abstract There is a long tradition, traceable to the early speculations of Ramon y Cajal, of focusing on the neuron as the only plastic cell type of any importance within the brain, and synaptic plasticity as the only important process for modulating interactions between neurons. While neuronal and synaptic plasticity are without question important aspects of brain function, it has become increasingly clear that other cellular elements of brain are malleable and that their plasticity can contribute to brain function. For example, oligodendrocytes, astrocytes, vasculature, and perhaps other neuropil components also exhibit plasticity in the developing and mature brain, and each of these cellular elements appears to be differentially influenced by distinct components of an experience. It is also becoming clear that various forms of brain plasticity likely have different functional purposes. Exposure to a complex environment, for example, causes synaptogenesis in animals genetically rendered incapable of potentiation of some post-synaptic responses, suggesting that these forms of plasticity serve different purposes in the brain. In short, while research has focused largely on naturallyand artificially-induced changes in synaptic connectivity, the brains of animals (and presumably people) in real-world situations are in a dynamic state in which synaptic adjustment may in some cases be a relatively small part of the mix. Here we review some of the data regarding multiple forms of plasticity, and briefly discuss how such changes might differentially affect functional brain organization. Grossman et al., 3 1 Introduction Since the early speculations of Tanzi and Ramon y Cajal, the synapse has been the principal proposed site of plasticity underlying learning and memory in the brain. Tanzi (1893) initially emphasized the possibility of strength changes in pre-existing connections while Ramon y Cajal (1893) stressed both the formation and loss of connections. The ability to appropriately investigate these possibilities was limited by the availability of suitable tools, but, by the early 1970s, electrophysiological and anatomical evidence of the nervous system’s ability to alter its functional connectivity in accord with its experience was becoming well established. Physiologically, activity-dependent modification of post-synaptic responses were described in invertebrates (Castellucci, et al., 1978), and in the mammalian visual system (Hubel, et al., 1977), and long-term potentiation was proposed as a model of vertebrate neural learning (Bliss and Lomo, 1973). Anatomically, there was evidence that dendrites and synapses could both form and change in size in response to altered patterns of synaptic activity, not only in the developing nervous system, but also in adult animals. Functional reinnervation of neurons by surviving axons, for example, was shown to occur spontaneously subsequent to denervation (Lynch et al., 1973; Raisman and Field, 1973). Likewise, morphological changes have been described in wellcharacterized models of invertebrate learning (e.g., Bailey and Chen, 1988), enabling researchers to better understand the relationship between structure and function that underlies plastic neural change. Here we summarize progress in Grossman et al., 4 understanding the various types of brain plasticity thought to be associated with learning and memory, focusing heavily on our own work, since those relatively early beginnings. The complex environment housing paradigm, pioneered by Hebb (1949) and his students (e.g., Hymovitch, 1952; [Forgays, 1952 #207] was first used as a tool for exploring brain plasticity by Bennett, Diamond, Krech, Rosenzweig, and colleagues (Bennett, et al., 1964), who reported some of the earliest evidence for morphological brain plasticity in response to experience. Subsequently, dendritic field dimensions (Volkmar and Greenough, 1972) and synaptic size (West and Greenough, 1972) were reported to increase in the visual cortex of rats exposed to a complex environment (EC) from weaning through adolescence. A later report by Turner and Greenough (1985) specifically demonstrated that there were more synapses per neuron in upper layers of the visual cortex in rats reared in a complex environment than in control animals reared in standard laboratory housing conditions. Taken together, these observations demonstrate that behavioral experiences could be used as a tool to study very specific, measurable aspects of neuronal plasticity. It should be noted that there is a reason that we use the term “complex environment” instead of the oft-used term “enriched environment.” We contend that laboratory environments simply are not enriched relative to the norm of wild or feral animals. We would argue that no one has published studies of the brains of rats that have been exposed to levels of environmental complexity and challenges beyond the level provided by the natural environment, and studies of Grossman et al., 5 wild animals have for years confirmed that feral animal brains are larger than those of domestically reared animals (old german and other literature). Nevertheless, studying different degrees of environmental complexity can provide information about brain responses that are likely to generalize to higher levels of stimulation, as suggested also by studies of the differential effects of environment on human development (e.g., Hart and Risley, 1995). In addition to the effects of complex environment on the visual cortex, multiple brain areas suspected to be involved in the processing and/or responding to environmental stimuli have also been shown to exhibit plasticity. For example, morphological changes have been reported in the auditory cortex (Greenough, et al., 1973) and physiological modifications of forepaw representation in primary somatosensory cortex (Coq and Xerri, 1998) in response to exposure to a complex environment. Beyond cortical sites, areas such as hippocampus [Moser, 1997 #80;Rampon, 2000 #87] basal ganglia (Comery et al., 1995; 1996), and cerebellar cortex (Greenough, et al., 1986) are also responsive to experience. These observations should not be interpreted to suggest that exposure to a complex environment induces plasticity in an ubiquitous manner throughout the brain. Indeed, certain forms of activity-induced plasticity have been shown not to occur in specific brain regions (e.g. Kleim, et al., 1998; see also below). Instead, these data suggest that different brain systems are involved in the processing of specific components of an animal’s experience. Following a description of the multiple types of brain plasticity, Grossman et al., 6 experimental methods for dissociating the specific components of experience will be discussed. 1 Neuronal and Synaptic Plasticity Among the most exciting recent developments in the field of neuronal plasticity are the data suggesting that the brain responds to experience by adding new neurons. Using a thymidine analog (BrdU) that incorporates into replicating DNA, it has been demonstrated that neurogenesis occurs in the hippocampal formation in response to complex environment housing (Kempermann, et al., 1998; Kempermann, et al., 1998; Nilsson, et al., 1999). More specifically, using a learning paradigm in which the underlying neural pathways necessary to perform the task have been very well-characterized, it was reported that neurogenesis is dramatically increased in the hippocampus when this structure is critically involved in learning the task, yet when the contingency does not demand involvement of this structure, the rate of neurogenesis is unaffected (Gould, et al., 1999). Although these increases in neuron number are small relative to the contingent of neurons already present in the brain, theories of brain plasticity that have largely focused on changes in the number and strength of synapses in neural networks must now consider the profound effects that integration of new neurons could have on both the composition and function of neural networks. While neurogenesis represents an exciting areas, it is a principal focus of other chapters in this volume. Thus, we highlight here specific aspects of plasticity of Grossman et al., 7 existing neurons as they relate to elements of non-neuronal plasticity and to the functional implications of the existence of multiple forms of brain plasticity. 2 Plasticity of synapse number On an anatomical level, the malleability of neuronal systems and individual neurons can be quantified using a number of parameters. Dendritic arborization is an indirect measure of available postsynaptic space (suggestive of synapse number), and can be quantified according to the methods described by Sholl ([Sholl, 1956 #227]). A more specific measure of synapse number is the ratio of synapses per neuron [Cragg, 1975 #226], which should be estimated using stereologically unbiased techniques (West, 1999). Exposing weanling rats to a complex environment leads to increases in dendritic branching and in synapses per neuron in the visual cortex (Greenough, et al., 1973; Turner and Greenough, 1985). The magnitude of these two experience effects was in the same range, a 20-25% increase, suggesting that the synaptogenesis associated with visual experience may reflect an increase in dendritic length, upon which new synapses form, more than it reflects an increase in the density of synapses per unit length of dendrite. Experience-induced increases in dendritic spine density, however, have been described (Globus, et al., 1973; Comery, et al., 1995)[Rampon, 2000 #87]. Although the effects of experience on measures of synapse number are most profound in young animals, synaptogenesis appears to occur across the age spectrum and is not restricted to particular critical or sensitive periods of Grossman et al., 8 development. Exposure to a complex environment has been shown to affect dendritic field dimensions in the visual cortex of young adult, middle-aged and even elderly rats (e.g., Greenough, et al., 1979; Juraska et al., 1980; Green et al., 1983; Black et al., 1986; Connor et al., 1981). Synapse number per neuron is also increased in rats placed in complex environments as adults (Briones, et al., In preparation). Behavioral experience appears to induce anatomical change throughout the lifespan in the cerebellum as well as the cerebral cortex (Floeter and Greenough, 1979; Pysh and Weiss, 1979). 2 Plasticity of synapse morphology In addition to changes in neuronal morphology, the morphology of individual synapses is affected by experience, as well. A number of these morphological effects are typically viewed as modifications of preexisting synapses, although they could reflect intermediate stages of synapse remodeling or the formation and/or loss of synapses with different properties. The size of both pre- and post-synaptic components, the shape of dendritic spines, and the , or the size of components such as the postsynaptic density (PSD) length, vesicle aggregates and the pre and postsynaptic processes themselves (Sirevaag and Greenough, 1985; West and Greenough, 1972). In response to physiological activity ( While changes in spine shape appear to be sensitive to experience, Harris and Stevens (Harris, 1988 #110) have reported that the diameter of the Grossman et al., 9 spine neck is stable. These observations have been interpreted to suggest that the spine neck serves to isolate intracellular events to activated synapses without altering the transfer of synaptic charge to the dendrite. THE WESA STUFF SEEMS OUT OF PLACE, CERTAINLY OUT OF FLOW As yet another demonstration of plasticity at the synaptic complex, Wesa et al. (1982) reported an increase in concavity of the presynaptic element in visual cortex associated with rearing rats in a complex environment. Taken together, these findings support the contention that neuronal plasticity can be expressed in a number of manners….. It has been theorized (by harris KM) that as the synapse grows, and the PSD gets larger, it finally develops a peforation. (or some such transition) ********* 2 PSD Perforations and Multiple Synaptic Boutons Although it is widely appreciated that the changes in synapse size and number have functional relevance, other types of synaptic plasticity exist whose functional relevance is less well understood. The curious discontinuities in the postsynaptic density characteristic of “perforated” synapses have been regularly associated with plastic synaptic changes since they were first reported to increase during development and with complexity of experience in rat visual cortex (Greenough et al., 1978; see also Jones and Calverley, 1991). The frequency of perforated synapses also increases after motor skill training (Jones et al., 1999; Kleim et al., in preparation). Similarly, Geinisman and others Grossman et al., 10 reported that some types of these synapses increased in number following kindling or induction of LTP (Geinisman et al., 1990; 1991). Another form of synaptic morphology with a proposed relevance to plasticity is the multiple synaptic bouton (MSB). Multiple post-synaptic spine and dendritic shaft synapes occur on vesicle-filled presynaptic processes in a number of brain regions including cerebellar cortex, cerebral cortex and hippocampal formation. While there is debate about the processes whereby terminal boutons or en passant varicosities become contacted by more than one postsynaptic process, there are consistent reports that the frequency of these MSBs is increased in behavioral and electrical plasticity paradigms. Early evidence that their occurrence could be influenced by experience was the Friedlander et al. (1991) report of a greater number of postsynaptic contacts on presynaptic terminals associated with the open eye in monocularlydeprived kittens. Subsequently Jones et al. (1997) reported a higher number of MSBs in visual cortex of EC (vs. SC or IC) rats. Jones et al. (1999) have also described increases in these multiple synapses in the intact cortex in the course of compensatory changes following unilateral cortical lesions. Geinisman et al., (2001) reported an elevated number of MSBs in hippocampal subfield CA1 following associative eyeblink conditioning using the trace paradigm, which requires an intact hippocampus in order to be learned. Similarly, Federmeier et al (submitted) found a dramatic increase in cerebellar parallel fiber varicosity MSBs with motor skill training. Thus the formation of new postsynaptic contacts on Grossman et al., 11 previously-innervated varicosities appears to be a common form of plastic synaptic change. Several theories have proposed that perforated synapses and multiple synaptic boutons, in addition to branched dendritic spines, might represent intermediate stages of a single synapse “splitting” into separate dendritic spines (e.g., Carlin and Siekevitz, 1983). In addition to the work described above, studies on synapse morphology following the induction of synaptic activity (Geinisman et al., 1989; Comery et al., 1996; Toni et al., 1999) have suggested the likely involvement of these intermediates in the establishment and/or refinement of synaptic contacts. Alternatively, evidence suggests that the phenomenon of spine splitting is not supported by the existence of perforations, branched dendritic spines and multiple synaptic boutons (Sorra, et al., 1998; Fiala, et al., 2002). Transmission electron microscopy precludes investigation of morphological dynamics, but recently developed techniques hold promise for real-time visualization of dendritic spine motility and will surely shed more light on this subject (e.g., Engert and Bonhoeffer, 1999; Maletic-Savatic, et al., 1999). It is clear that the final words have yet to be written regarding modification of synaptic morphology in response to altered patterns of activation. 1 Persistence of neuronal changes For the most part, neuronal changes induced by differential experience (e.g., Volkmar and Greenough, 1972; Turner and Greenough, 1985) appear to persist after experience has been discontinued. In rats that had been exposed to Grossman et al., 12 30 days of complex environment (EC) and then returned to standard housing conditions (IC), the dendritic arborization (Camel et al., 1986) and number of synapses per neuron (Figure X; Briones, et al., in preparation) were not different from animals that were exposed to EC for 60 days. Both groups, however, differed significantly from animals that were individually caged for 60 days. The increases in number of synapses per Purkinje cell in response to motor skill training (Black, et al., 1990) appear to persist for a minimum of four weeks in the absence of continued training (Kleim, et al., 1997). 1 Non-neuronal Plasticity The complex environment paradigm has been used extensively to examine plasticity of neurons and synapses. This paradigm has also been used to study a range of plasticity of non-neural elements, yet these data have largely failed to become incorporated into theories of brain plasticity. Early in the history of this field, Diamond et al. (1966) reported that exposure to a complex environment induced changes in the number of glial cells in cerebral cortex. We have subsequently examined plasticity in glia and other non-neuronal elements of the cerebral cortex in response to complex environment exposure and find that, in general, the changes in these elements parallel those observed in neuronal dendrites and synapses. These observations suggest that most, if not all, types of brain tissue exhibit plasticity, at least in regions such as the cerebral and cerebellar cortices where this phenomenon has been systematically examined. Grossman et al., 13 . ALTHOUGH GLIAL-GENESIS COULD BE A VERY INTERESTING ASPECT OF PLASTICITY AS WELL, YET WE DON’T HAVE TIME TO DISCUSS SUCH. 2 Plasticity in astrocytes Although early reports on experience-induced plasticity of astrocytes (Diamond et al. 1966; Szeligo and Leblond, 1977) were suggestive of this phenomenon, the techniques for quantifying morphology that were available at that time limited the conclusions that could be drawn from the data. Subsequent reports used unbiased stereological techniques to quantify morphological changes and have described increases in both the surface density of glial fibrillary acidic protein-immunoreactive processes and the percent of tissue that is taken up by astrocyte cell nuclei (volume fraction) in the visual cortex in response to EC exposure (Sirevaag and Greenough, 1987; Jones et al., 1996; Sirevaag and Greenough, 1991). Additional complex experience appears to cause hypertrophy of astrocytic processes and cell number, although the effects of environment on astrocytes depend both on the duration of EC exposure and on the cortical layer examined (reviewed in Jones and Greenough, in press). Astrocytic changes in response to EC seem, for the most part, to follow a similar time course, paralleling measures of neural plasticity (e.g. Wallace et al., 1992; Jones et al., 1996). Moreover, at least in cerebellar cortex, the volume fraction of astrocytes parallels plasticity of synapse number on an individual animal basis [Sirevaag, 1988 #40; Anderson, 1994 #11]. Ultrastructurally, it appears that proliferation of astrocytic processes is closely associated with Grossman et al., 14 experience-induced synapse addition. Jones and Greenough (1996) found that in rats exposed to a complex environment synapses are more completely ensheathed by astrocytic processes than synapses in control animals, possibly reflecting a role for astrocytes in optimizing the synaptic microenvironment in response to increased neural activity. There are numerous reasons to expect astrocytic plasticity to have functional consequences. Glia influence synaptic function in ways that range from modulating synaptic efficacy (Araque et al., 1998; Smit et al., 2001) to dissociating presynaptic from postsynaptic processes during synaptic remodeling (Hatton, 1997; Meshul et al., 1987; Salm, 2000). Astrocytes can conduct excitation via propagated Ca2+ waves (Araque et al., 1999; Dani et al., 1992) which can directly influence neuronal activity (Rouach et al., 2000). These data suggest that astrocytes may constitute a separate, relatively local (and slower) system for activating brain regions. Glial cells are involved in the re-uptake and metabolism of GABA and glutamate (Schousboe et al., 1992; Bezzi et al., 1999), suggesting that they could play a role in synaptic modulation. Glial cells also have receptors for many neurotransmitters including norepinephrine (Shao and Sutin, 1992) and glutamate (Muller et al., 1993; Shelton and McCarthy, 1999), two neurotransmitter systems that have been implicated in the formation and modulation of memory processes. Considering the apparent close interaction between astrocytes and events typically thought to be “synaptic,” it is likely that Grossman et al., 15 plasticity of astrocytes as well as neurons is critical to the process of learning and memory. 2 Plasticity of myelination Although myelination is considered to be primarily a developmental phenomenon, studies of human autopsy tissue suggest that this process continues well into adulthood (Yakovlev and Lecours, 1967; Benes et al., 1994). In developing and adult animals, the process of myelination appears responsive to behavioral experience. The first evidence for experience effects on oligodendrocyte development came from Szeligo and Leblond (1977), who reported increased subcortical white matter in recently weaned rats following exposure to complex environment. It was subsequently observed that weanling (Juraska and Kopcik, 1988) and adult rats (Briones, et al., 1999) had more myelinated axons in the splenial corpus callosum after EC exposure than did IC rats. The effects of experience on oligodendrocytes are not restricted to white matter though as Sirevaag and Greenough (1987) found that the volume fraction of oligodendrocyte nuclei in the visual cortex was significantly greater for EC rats than their IC littermates. In addition to the role oligodendroctyes play in responding to the increased demands placed on the brain by experience, these cells are critical for repair of neural tissue following damage. The glial environment surrounding regenerating axons is important for their successful growth into target tissues (Aguayo, et al., 1981). Much of the currently research effort on spinal cord injury repair is Grossman et al., 16 focused on the functional development of oligodendrocytes at the lesion site. Stem-cells that are implanted into the spinal cord, for example, can differentiate into oligodendrocytes and myelinate passing axons (Liu, et al., 2000) Although some Schwann cells in the peripheral nervous system exhibit plasticity by converting from non-myelinating to myelinating cells in response to local tissue damage (Kioussi and Gruss, 1996, [Akassoglou, 2002 #221], it is debated whether mature oligodendrocytes in the central nervous system exhibit such plasticity (Bruck, et al., 1994; Blakemore and Keirstead, 1999). 2 Plasticity of Vasculature Despite long held beliefs that the brain’s capillary system is not plastic (Bar, 1980), and in contrast to earlier reports (e.g., Diamond et al., 1964; Rowan and Maxwell, 1981), the brain’s capillary system appears to increase in capacity in response to experience. Capillaries are both larger, on average, and more elaborately branched in rats placed in complex environments at weaning than in individually cages animals. As shown in Figure X, the volume fraction of capillaries per neuron, which combines diameter and density effects, increases by about 80% following exposure to EC, suggesting that capillaries exhibit seemingly far more plasticity than synapses in response to behavioral demands (Black et al., 1987; Sirevaag et al., 1988). Studies using functional magnetic resonance imaging indicate that the capacity of experientially enhanced vasculature to supply blood is increased in response to increased demand (reduced oxygen in anesthetized rats) (Swain et al, in press GET REF). While the Grossman et al., 17 greatest capillary response to EC housing is seen in weanlings, this experienceinduced plasticity also continues into adulthood, although diminishing with age (Black, et al., 1989). 2 Persistence of non-neuronal changes In general, the effects of plasticity on myelination appear to be relatively stable, while the effects on astrocytes seem to be more transient. The increased myelination observed in adults following 30 days of EC housing persists across a subsequent 30-day period of relative inactivity in an individual cage housing condition (Briones et al., 1999; Figure X). This stability parallels the synaptic effects discussed above (Camel et al., 1986; Kleim et al., 1997; Briones, et al., in preparation). Astrocytic changes, however, appear to fade rapidly once a specific behavioral experience is discontinued. As shown in Fig. X (Kleim et al., in revision), when rats were trained for 10 days and left idle for a subsequent 4 weeks, the astrocytic effects of training (compared to a group that simply traversed an alleyway) were no longer statistically evident, whereas the effects of training on synapses remained apparently as strong in the idle group as in animals examined at the end of 10 days of training or animals trained for all 38 (10 + 28) days of the experiment. One might speculate that added synapses and myelin are stable because they represent permanent additions to the “wiring diagram” of the brain that are important for survival, whereas astrocytic and possibly vascular (yet to be tested) changes are responses to immediate Grossman et al., 18 demands of experience that can be reset, conserving valuable metabolic resources in the absence of continued environmental pressure. The overriding message from studies of both neuronal and non-neuronal plasticity is that the brain is an organ of adaptation — the interface between an individual and its environment. As such, the brain dynamically adjusts to the demands placed upon it. It does so not just by forming, strengthening, losing and weakening synapses but by altering non-neuronal elements such that neuron-glia relationships are altered, in some cases on a long-term basis. The persistence of many of the resulting morphological changes suggests that the brain assumes that the experiences of the past are good predictors of the future. It is of interest to speculate upon the functional consequences of selective myelination of corpus callosal axons in response to complex environment exposure. Presumably those axons would more rapidly conduct action potentials. Assuming this is associated with functionally positive effects, the implication is that oligodendrocytes must follow some form of instruction in selecting particular axons for conduction enhancement (or, perhaps, retaining them from a larger set of initially myelinated axons). The clear implication is that there are means of neuronal-oligodendrocyte communication of which we are not now aware. Moreover, these communication mechanisms must be able to be activated by behavior. 1 What drives the plasticity of brain tissues? Grossman et al., 19 The behavioral sensitivity of transient and persistent processes of cellular adaptation, and the fact that both brain activity and learning are involved in the behavioral events that appear to drive this adaptation naturally led to the following questions: What causes changes in neurons, glia and vasculature? Can we rule out causes such as hormonal or metabolic responses to behavioral manipulations? Can we then attribute experience-induced neuronal and nonneuronal plasticity to learning or to brain activity? It is possible to imagine that cellular responses to experience that could appear to be due to learning could actually be responses to stress or related metabolic consequences. Certainly stress can affect the morphology of neurons in some brain regions (e.g., Sapolsky, 1996), although the astroglial changes in the hippocampal formation that are correlated with adrenal hypertrophy appear to be dissociated from visual cortex changes in complex environment research that are correlated with experience (Sirevaag, et al., 1991). To examine the roles of learning vs. other consequences of training (e.g., activity, stress, etc) on neuronal changes, we have utilized paradigms in which the effects of learning would be focused in particular regions of the brain for which other regions could serve as control or comparison samples. In one early study, Chang and Greenough (1982) compared rats trained in a complex series of changing maze patterns that learned with either the same eye always occluded or with occlusion of alternate eyes on successive days. In rodents, approximately 90-95% of retinal ganglion cell axons project to the opposite side of the brain (Lund, 1965). The unilaterally-occluded rats, therefore, Grossman et al., 20 should have most training-related activity restricted to the hemisphere opposite the open eye, whereas the bilaterally-alternating occlusion should allocate the learning-related activity about equally to both hemispheres. Both groups had been previously subjected to transection of the corpus callosum, a “split-brain” procedure that eliminates communication between the two hemispheres of the brain. Control animals were surgically operated, divided into unilaterally- and bilaterally- occluded groups, and subsequently handled but not trained. The results of this study indicated that dendritic branching, a correlate of synapse number, was increased in the non-occluded hemisphere of the unilaterally trained group, compared to the occluded hemisphere, and in both hemispheres of the alternately-trained group relative to the non-trained group. These results indicate that non-specific effects such as stress, which would have been distributed equally between hemispheres in the unilaterally-trained group, can not account for experience-induced plasticity. Rather, either training or training-related activity drives dendritic plasticity. As another example of the dissociation between non-specific and specific effects of experience on plasticity, Greenough et al. (1985) examined activated vs. non-activated hemispheres of rats trained to reach for a food reward with alternating forepaws or with only one forepaw. Dendritic branching in somatosensory-somatomotor cortex in these animals was compared to untrained controls. For deep pyramidal neurons of the type that control forelimb activity, apical dendritic branching was greater in the hemisphere opposite trained forelimbs than in ipsilateral homologous areas. Dendritic arborization in the Grossman et al., 21 group trained with alternating forepaws was greater than untrained controls. These findings parallel those described for visual cortex by Chang and Greenough (1982). In contrast, while there were effects of training on more superficial pyramidal neurons, these effects were not restricted to the activated side in unilaterally-trained animals (Withers and Greenough, 1989). These observations of morphological changes expressed in extragranular layers of the untrained cortex (as well as the trained side) are consistent with the idea that plasticity of intercortical connections plays a key role, possibly acting as the locus of plasticity initiation within the cortex [Gilbert, 1979 #191;Gilbert, 1992 #192;Hess, 1994 #224]. The work of Chang and Greenough (1982).and Greenough et al. (1985) suggests that studies indicate that learning or some other aspect of trainingrelated activity drives morphological change in neurons. Non-specific effects such as globally-acting hormonal or metabolic differences were not the causal force behind these morphological changes, as such non-specific effects would be expected to alter comparable regions of the brain whether or not they were selectively activated by unilateral training. Although these studies suggest that the artifact of nonspecific stress or metabolic effects on training-related changes may be small, we should continue to consider this possibility and to run appropriate controls. However, even if this artifact is ruled out, the issue remains that any learning involves at least some brain activity, and if learning is localized in the brain (e.g., to one hemisphere), Grossman et al., 22 the activity is likely to be localized as well. Is brain adaptation learning-related, or can activity alone drive plastic neural change? With regard to this question, we have muscle as a model: with sufficient activity, muscle will hypertrophy, the particular details varying with the extent and pattern of activation. One can suppose that activation of brain tissues in association with peripheral activity might similarly trigger neuronal, glial or vascular hypertrophy of the sort seen in rats after complex environment housing. Learning, in contrast, likely causes changes in neurons and glia, but perhaps not capillaries as is harder to imagine that changes in vasculature would play very specific roles in learning. 2 Activity vs. Learning To more directly address the issue of whether activity or learning causes structural changes in the brain Black et al. (1990) designed a paradigm in which adult rats were given the opportunity for either 1) a substantial amount of learning with relatively little physical activity (AC below), 2) a substantial amount of physical activity with relatively little learning (FX and VX below) or 3) minimal opportunity for physical activity or learning (IC below). ACrobatic rats (AC) completed a multi-element elevated obstacle course that required learning significant motor skill while providing only limited exercise. Forced eXercise (FX) rats ran on a treadmill, reaching durations of 60 minutes a day, exercising but with very little opportunity for learning. Voluntary eXercise (VX) rats had access to running wheels attached to their cages and were the only group to exhibit Grossman et al., 23 increased heart weight, a sign of aerobic exercise. Inactive Condition (IC) rats were merely removed from their cages for brief daily experimenter handling, providing neither activity nor learning. JEFF, you stopped here!!! In initial studies focusing on cerebellar cortex, Black et al. (1990) both the density of blood vessels and the number of synapses per neuron (Purkinje cells were used as a basis for this calculation). For the number of synapses per neuron, as depicted in Figure 3A, the learning group, AC, exceeded the other 3 groups, which did not differ, suggesting that when learning takes place, new synapses are formed. By contrast, as Figure 3B shows, the FX and VX groups both had a greater density of blood vessels than the AC or IC groups, which did not differ; this suggests that the formation of new blood vessels was driven by neural activity, and not by learning. FIGURE OUT KLEIM”S REFERENCES. Subsequent work indicated that these effects were not limited to cerebellar cortex. Kleim et al. (1996; ANYOTHER REF?) described synaptogenesis and changes in synapse morphology in association with the same AC motor learning procedure in forelimb area of primary sensory-motor cortex of rats. One of the Grossman et al., 24 first morphological changes to occuris an increase in the average size of PSDs, which occurs within two days after beginning training. After five days of training, an increase in the number of synapses per neuron was detected, and the average size of synapses had decreased, possibly because the new synapses were, on average, smaller than the pre-existing synapse population. This increased synapse number was maintained across the remaining days of training. As training progressed, the average synapse size increased again, suggesting that the new synapses were growing larger or that the population of synapses overall was increasing in size. A schematic interpretation of this process is depicted in Figure X. JEFF, we’ve revamped your figure a bit, and prefer to use this rather than the two graphs – 1 published, 1 unpublished. These findings suggest that at least two independent mechanisms of synaptic plasticity, the formation of new synapses and the enlargement of existing synapses, are activated in parallel during motor learning Jeff’s comment from above goes best here somehow: It is likely that angiogenesis is driven by increased activity within a specific brain region as the repeated performance of unskilled movements such as those produced during exercise causes increases in capillary density (Black et al., 1990; Isaacs et al., 1992 #230; Kleim et al., 2002). Further, changes in blood vessel density can occur independent of changes in synapse number (Black et al., 1990) and, as noted above, these changes are Grossman et al., 25 reflected in functional measures of blood flow and its responsiveness to oxygen demand (Swain et al., in press). It might be argued that running on a treadmill or in a wheel is sufficiently different from skilled movements involved in the AC task that this difference, rather than the learning difference, XXXXXXXX. However, even in paradigms in which the movements were very similar, the acquisition of skill has been selectively associated with the addition of synapses (Kleim et al., 1996; Kleim et al., 1998c; Kleim et al., 2002 this last reference may be the only one to support that statement, or another may be needed JEFF, can you address this??). Similarly, the acquisition of skilled forelimb movements resulted in reorganization of forelimb movement representations within motor cortex (Nudo et al., 1996; Kleim et al., 1998a) while extensive repetition of unskilled movements (Plautz et al., 2000; Kleim et al., In press) and forelimb strength training (Remple, et al., 2001) were without effect. In contrast, strength training increased synapse number in the ventral spinal cord but motor skill training was without effect (Kleim, et al., 2001). Differential patterns of plasticity can also be observed across types of learning that seemingly use similar neuronal pathways. For example, motor skill training that altered cerebellar cortex (in work noted above) did not detectably alter synapse number within the deep cerebellar nuclei (Kleim, et al., 1998) whereas eye blink conditioning that did not detectably affect cerebellar cortex (by one measure: Anderson et al. XXXX) did affect the deep nucleus (Bruneau, et Grossman et al., 26 al., 2001). It is perhaps not surprising that different types of learning would be differently represented in the same structures, given the lateralized effects of training described above (e.g., Chang & Greenough 1982), but there are remarkably few demonstrations of this. Similar specificity of plasticity is seen in subpopulations of neurons within the same region: specificity of the plasticity can even be reduced to subpopulations of neurons within the same brain region. For example, complex housing caused dendritic hypertrophy within cerebellar Purkinje cells but not granule cells (Floeter and Greenough, 1979). Reach training caused dendritic hypertrophy within layer II/III of the motor cortex that was restricted to a specific class of pyramidal cells (Withers and Greenough, 1989). Finally, plasticity can even be restricted to specific afferents onto individual neurons. Complex motor skill training increased parallel fiber synapses onto Purkinje cells but not climbing fibers (Kleim, et al., 1998). Eyeblink conditioning increased the number of excitatory synapses within the anterior interpositus without altering inhibitory synapse numbers (Bruneau, et al., 2001). This result is particularly interesting because the same system, with two excitatory afferents to the cortex and a single inhibitory output to the deep nuclei, is showing different patterns/locations of plasticity under different training conditions; perhaps when learning involves the integration of an array of body positioning movements and counter-forces the detailed representation of the body evident in the afferent parallel fiber system handles the task, whereas when the focal disinhibition of a response to a sensory stimulus is involved, this takes place at the output level of the cerebellar cortex. Grossman et al., 27 Similarly strength training increased excitatory but not inhibitory axosomatic synapses within the ventral spinal cord (Kleim, et al., 2001). This degree of specificity is consonant with the concept of local “synaptic tags” that designate locations where synapses are to be modified, or perhaps generated (e.g., Frey and Morris, 1997). A further dissociation of synaptic plasticities deserves mention. While some studies have suggested that synaptogenesis may be associated with longterm potentiation (e.g., [Lee et al., 1980 #233;Chang & Greenough, 1984 #231]; Engert and Bonhoeffer, 1999; Maletic-Savatic, et al., 1999; Andersen and Soleng, 1998), others have shown explicit dissociations between electricallyinduced LTP and behaviorally-induced synaptogenesis in receptor mutants [Rampon et al. 2000 #87]. The dissociation seems the more powerful result: synapse addition may mediate or be involved in some aspects of LTP, but synapse addition need not involve an LTP-like process for its induction. Primary neurons and the cortex: Is the current approach overly-restrictive? Historically, investigations into neuronal plasticity have focused, almost exclusively, on modifications in the structure and function of primary neurons and circuits. While the importance of these systems can not be overlooked, it belies the fact that neurons not directly involved in such pathways far out number those that do. Seress et al. (2001) have reported that 95% of terminals forming asymmetric synapses with parvalbumin-positive (assumed to be GABAergic) Grossman et al., 28 dendrites in the dentate and strata pyramidale and lucidum of CA3 are from granule cells. Such observations beg the question as to why large projection neurons (e.g., pyramidal cells) are the primary focus of so many studies while historically “modulatory” cells are largely ignored. It has been shown that modulatory systems can have dramatic effects on the function of primary neurons. For example, receptive field size in primary sensory cortex has been shown to be sensitive to pharmacological disinhibition [Jacobs, 1991 #187;Tremere, 2001 #185]. The source of such inhibition likely stems from extragranular layers of cortex as rapid (physiologically-defined) changes in both visual (Trachtenberg, et al., 2000; Trachtenberg and Stryker, 2001) and somatosensory cortices (Diamond, et al., 1994) have been reported to occur prior to the expression of modifications in layer IV. Finally, observations by Gilbert and colleagues further suggest that horizontal connections may be the impetus?? to reorganization of visuo- and topo-graphic maps (Gilbert and Wiesel, 1979; Gilbert, 1992). Much like the argument that additional scrutiny of “modulatory” neurons appears warranted, we also believe that it is both naïve and foolish to emphasize the role of cerebral cortex so heavily. That is, the cerebral cortex is often-times considered “where” changes occur, ignoring the fact that in a diverging pathway (e.g., somatosensory projections to S1), minor modifications in subcortical connectivity would have much larger effects at the level of the cortex. The idea that modifications of subcortical areas of the brain express plasticity is not a novel one and has been shown to occur in areas from the spinal cord (Devor and Grossman et al., 29 Wall, 1978) to striatum (Comery, et al., 1995; 1996). In general, plasticity appears to be a fundamental property of the nervous system and as such, the contribution of areas beyond those most readily accessible need to be fully considered. Finally we return to a point made at the outset and the subsequent presentation of supporting data: in both cortical and subcortical areas there is ample and growing evidence for substantial plasticity of non-neural cells such as oligodendrocytes, astrocytes and vasculature. In general we do not know what intermediary events relate behavioral or physiological activity to changes in glial or vascular structure and function. The morphological and functional changes may be transient or lasting and may be associated with learning-driven processes such as synaptogenesis or with vascular responses to activity. Astrocytic changes that appear to selectively accompany synaptogenesis (e.g., Anderson 94) may require activity for their maintenance (Kleim in revision). We are still working to understand the behavioral forces that regulate these nonneuronal plasticities as well as the cellular and molecular events that mediate them. Perhaps most important, we must continue to work on their functional roles. Summary and Conclusion A view that is emerging is that the brain has multiple forms of plasticity that must be governed, at least in part, by independent mechanisms. This view is illustrated by 1) the apparent separate governance of some non-neural changes by activity, in contrast to synaptic changes driven by learning, 2) the apparent Grossman et al., 30 independence of different kinds of synaptic changes that occur in response to the learning aspects of training, 3) the occurrence of separate patterns of synaptic plasticity in the same system in response to different task demands, 4) apparent dissociations between behaviorally-induced synaptogenesis and LTP and 5) reports of an increasing number of nonsynaptic forms of plasticity in neurons. The historical focus of research and theory in areas ranging from learning and memory to experiential modulation of brain development has been heavily upon synaptic plasticity since shortly after the discovery of the synapse. Based upon available data, it could be argued that 1) synaptic, and even neuronal, plasticity is but a small fraction of the overall range of changes that occur in response to experience and that 2) we are just beginning to understand the importance of these other forms of brain plasticity. Appreciation of this aspect of the brain adaptive process may allow us to better understand the capacity of the brain to tailor a particular set of changes to the demands of the particular experiences that generated them. ******* THIS IS THE FORMAL END OF THE CHAPTER ******** -----------------------------------------------------------------------------------------------------------Bill’s still working on the rest. I checked ref’s up to here. Role of non-neuronal changes in learning / activity based plasticity. – MAINTENANCE!!!! Grossman et al., 31 One is Brenda's 1994? Paper showing the correlation between synapse number and astrocyte Vv. The other is Jeff's astrocyte persistence paper. Discussed already?????Now covered in pre-conclusion paragr Regulation of Astrocyte Plasticity Is this redundant? This was mentioned in the section on non-neuronal plasticity and perhaps should merely be elaborated more in that section there are two studies that might be discussed in more detail either in that section or here. Tj's ensheathment paper fits in this discussion. The point of putting it here is by way of a segue into a discussion of the tendency to ignore non neuronal (or even nonsynaptic) changes. THIS HAS BEEN DONE THESE CONCEPTS SEEMS TO GO WITH THIS SECTION: Synaptic specificity supported by “synaptic tag” that is localized and protein-synthesis independent (Frey and Morris, 1997). Fits concept of metaplasticity in that history of synapse (even sub optimal stimulation patterns) pre-disposes synapse to subsequent modification. Could consider integrating notion of differential parameters necessary/sufficient to induce LTP (emphasize model of learning, not that it is equivalent or necessary for) in multiple areas of the brain. That one type of stimulus does not result in the same effect in numerous areas of the brain suggests (obviously) differential make-up of that area and surely different mechanisms. This notion would simply parallel our argument of different “types of Grossman et al., 32 plasticity” (as defined anatomically), with physiological correlates (Yun, et al., 2002); (Trepel and Racine, 1998). I AM NOT SURE MORE IS NEEDED IS THIS THE VENUE TO EVEN BRING UP FRAGILE X/FMRP???? I AGREE WITH YOU THAT THE ANSWER IS NO On the Horizon: A Role for Protein Synthesis at the synapse Since the first report of morphological evidence for protein synthesis at the synapse (Steward & Levy, 1992) there has been a growing body of literature investigating this phenomenon. Some forms of synaptic and dendritic protein synthesis have been shown to be activated by metabotropic glutamate receptors (mGluR) in some cases (e.g., Weiler & Greenough, 1993; Weiler et al., 1994, 1997; Eberwine PNAS-still in press?) and by NMDA receptors as well (Sheetz et al., 2000). Proteins synthesized at synapses include the fragile X protein FMRP and calcium/calmodulin-dependent protein kinase II (CAMKII). FMRP has also been shown to be necessary for the mGluR-dependent synthesis, which is not observed in FMR1 knockout mice (cite Spangler abstract). Plasticityinducing forms of electrical stimulation have been shown to trigger the transcription and transport of mRNA for the protein ARC to dendritic sites of stimulation, where it is translated (Steward and Worley references). mGluR1 activation, ARC synthesis and CAMKII activity have been proposed to be involved in various forms of plasticity (Huber/Bear work; Steward; Mary Kennedy), although details of the specific functions of synaptic or dendritic protein synthesis are still under investigation. Do you think we need to say Grossman et al., 33 anything more here? The chapter is really not "about" this, and I am not sure (but open to suggestions) what additional data makes sense to include. In summary, brain plasticity appears to be a phenomenon that is not restricted to elements that are neuron-specific. In fact, it could be argued that neuronal plasticity is but a small fraction of the overall changes that occur in response to experience and that we are just beginning to understand the importance of these other forms of brain plasticity. A big waving of our hands to and draw grand conclusions, followed by speculations on direction….. CHAPTER WORKING NOTES: MSVs—Kara; TJ; specialized synaptic morph changes. Tissue cultures lacking astrocytes—how good a model? Lack of synapse formation in cultures without astrocytes (Ullian, et al., 2001). Moreover, even when synapses do form, they are functionally immature. Obvious implications on studies of “synaptic plasticity” in vitro. NOT SURE WE HAVE TIME FOR THIS NOR IS THIS NECESSARILY CONSISTENT WITH UNDERLYING THEME OF THE PAPER. Lack of astro part of ECM. Lack of basis for TPA, other actions probably involved in synaptogenesis. MMP3, MMP6, MMP9 (Metalomatrix proteins), stromolysin, gelatinase. 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