Download A Brain Adaptation View of Plasticity: Is Synaptic Plasticity An Overly

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. Roles of Astros, ECM, TPA, etc. in synaptogenesis;
adhesions; rec aggregation DEFINITELY NOT ENOUGH TIME FOR THIS.
Grossman et al., 34
Incorporate Harris, Matus, Segal. Motility and shape issues. Put together
a model, slow accumulation of synapses via overproduction-selection as a basis
for the stable long-term substrate of memory; plus fast shape changes, PSD size,
perfs, interpret multiple synapses from local and wiring diagram view. DONE TO
AN EXTENT, NIX THIS SECTION?
Grossman et al., 35
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