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A Brain Adaptation View of Plasticity:
Is Synaptic Plasticity An Overly Limited Concept?
AUTHOR LIST
1Beckman
Institute, 2Neuroscience Program, 3Medical Scholars Program,
4Departments
of Psychology, 5Psychiatry, and Cell and Structural Biology,
University of Illinois at Urbana-Champaign
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]
1
Abstract
There is a long tradition, traceable to the early musings of Ramon y Cajal,
of focusing on the neuron as the only plastic cell type of any importance within
the brain, and on the synapse as the only important plastic aspect regulating the
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 plastic 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. The different components of an experience appear
to influence each of these cellular elements differently. It is also becoming clear
that these various forms of brain plasticity likely have distinct functional purposes.
Exposure to a complex environment, for example, causes synaptogenesis in
animals genetically rendered incapable of the most common form of long-term
potentiation, suggesting that these forms of plasticity serve different purposes in
the brain. In short, while research focuses largely on naturally- and artificiallyinduced 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 that suggest the existence of multiple forms of plasticity,
and briefly discuss how these changes might differentially affect functional brain
organization.
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Roots of neuronal plasticity
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 the formation and
loss of connections. The ability to examine these possibilities was limited by the
availability of adequate 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 reasonably well
established. Electrophysiologically, activity-dependent modification of postsynaptic responses were described in invertebrates (Castellucci, 1978 #200),
and in the mammalian visual system (Hubel, 1977 #196), and long-term
potentiation was proposed as a vertebrate model of neural learning (Bliss and
Lomo, 1973). Anatomically, there was evidence that synapses in the late
developing and adult nervous system could both form and change in size in
response to experience; functional innervation of neurons by surviving axons
occurred spontaneously in response to denervation (e.g. Lynch et al., 1973;
Raisman and Field, 1973); CAN WE TALK HERE ABOUT NON-EC EVIDENCE
FOR ADULT PLASTICITY HERE? – possibly use anatomical observations from
associative learning paradigms I think this might better go in later below Here we
summarize progress in 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.
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Contributions of Complex Environment Research to Plasticity
The complex environment housing paradigm, pioneered by Hebb (1949)
and his students (e.g., Hymovitch, 1952 #178), was first used as a tool for
exploring brain plasticity by Krech, Rosenzweig, Bennett and colleagues (Bennett
et al., 1964), who reported some of the earliest evidence for morphological brain
plasticity in response to experience. It was subsequently reported that dendritic
field dimensions (Volkmar and Greenough, 1972) and synaptic size (West and
Greenough, 1972) were increased in the visual cortex of rats exposed to a
complex environment (EC) from weaning through adolescence. A later report by
Turner and Greenough (1985) demonstrated specifically that there were more
synapses per neuron in upper layers of the visual cortex in rats reared in a
complex environment. These observations demonstrated 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.” These
laboratory environments are simply not enriched relative to the norm of wild or
feral rats. We would argue that no one has published studies of the brains of rats
that have been provided levels of environmental complexity and challenge
beyond the level provided by the natural environment, and studies of wild
animals have for years confirmed that feral animal brains are larger than those of
domestically reared comparable animals (old german and other literature).
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Nonetheless, 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).
Complex environment effects on neurons are widespread in brain
In addition to the effects of complex environment on the visual cortex,
neuronal responsiveness to activity and experience has been demonstrated in a
wide variety of brain structures including hippocampus (Moser et al., 1994), basal
ganglia (Comery et al., 1995; Comery et al., 1996), cerebellar cortex (Greenough
et al., 1986), auditory cortex (Greenough [volkmar & Juraska, 1973]) and
somatosensory cortex (Coq, 1998 #53) .**.{I think this is sufficient}
At this point, neuronal plasticity seems to be the rule rather than the
exception in CNS structures, although there are some striking failures to
demonstrate it in some cases (Kleim et al., 1998; but see Jeff's new paper) – this
is the LCN no-change paper) Kleim now has data showing
dentate/interpositus?? plasticity in eyeblink conditioning. this should go in
a separate section that Jeff will write. (I’m keeping these notes here until
we’ve got this written….) Jeff can add more here or elsewhere see changed
version, not done, on bill’s revision given to aaron
Note that these lists are exemplary and not all-inclusive.
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brief review of Neuronal forms of brain plasticity (basically set up all the
measures of plasticity that we’ll be referring to later in the chapter)
each topic should be described in light of all non-dichotomised forms of plasticity
in which it has been observed (development, EC, lesion, monocular deprivation)
Plasticity of synapse number
Dendritic arborization has long been used as an indirect indicator of
synapse number (or of available postsynaptic space). The magnitude of
experience effects upon dendritic branching of representative visual cortex
neuron types and upon the number of synapses per neuron was in the same
range, a 20-25% increase in EC vs IC rats (Greenough and Volkmar, 1973 vs
Turner and Greenough, 1985). Spine density was somewhat elevated on some
EC visual cortex neurons as well (Globus et al., 1973?), and in caudate nucleus
(Comery et al., 1995). Robust dendritic branching increases also occurred in rats
placed in EC (vs IC) housing as adults (Greenough, Juraska and Volkmar, 1979)
and in middle age (Green et al., 1983). The effects of a complex environment on
dendritic branching appear largely to remain present through a subsequent
period of exposure to IC housing (Camel et al., 1986).
As noted above, exposure of rats to a complex environment increases the
number of synapses per neuron in visual cortex. This is also seen in adults and,
like dendritic effects, the synaptic effects largely survive a subsequent period of
IC housing (Briones). Synaptic effects of EC exposure also occur in
hippocampus (Tsien paper cited elsewhere).
I don’t know what else should go in the preceding paragraph but it seems rather
short
might measures of the effects of learning on functional brain organization already
be affected by these nonsynaptic changes?? What is this?
A number of morphological effects have typically been viewed as
odifications of pre:existing synapses, although they could reflect formation and/or
loss of synapses with different properties. The simplest of these are measures of
synaptic size (or the size of components such as the postsynaptic density (PSD)
length, vesicle aggregates and the pre and postsynaptic processes themselves
(e.g. West and Greenough, 1972; Sirevaag and Greenough, 1985). ***Not sure if
anything else is needed here***
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Perforations : The curious discontinuities in the postsynaptic density
characteristic of “perforated” synapses have been regularly associated with
plastic synaptic change since they were first reported to increase with age and
with complexity of experience in rat visual cortex (Greenough et al., 1978; see
also Jones and Calverley, 1991). Geinisman and others reported that some
types of these synapses increased in number following induction of LTP or
kindling (e.g., Geinisman et al., 1991; Geinisman et al., 1990) and suggested that
they might represent stages in a single synapse “splitting” into dual separate,
innervated spines, the presence of multiple headed spines and their increases in
number in both electrical and behavioral plasticity paradigms (Geinisman, 1989
#138; (Comery et al., 1996). Perforated synapses also increase after motor skill
training (e.g., Kleim et al., in preparation; Jones et al., 1999). Fiala et al. (Fiala et
al., 2002) argue that this is highly unlikely as mature axons pass through the gap
between two spines connected to the same presynaptic process. However, this
conclusion would appear to require a specific type of splitting in which the spine
continuously maintained contact with both spines throughout the splitting
process. If splitting involved transient retraction followed by re-extension to
contact the presynaptic process (excluded from the definition of splitting by Fiala
et al., 2002), it would be possible, even likely, that other processes would be
contained between the daughter spines. Other forms of synaptic remodeling
have also been proposed with regard to perforated synapse structures (e.g., Toni
et al., 1999), and it is clear that the final words have yet to be written regarding
this phenomenon.
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MSBs
Geinisman et al., (2001) reported MSBs in hippocampal subfield CA1
following associative eyeblink conditioning using the trace paradigm which
requires an intact hippocampus in order to be learned. In general, according to
(Kristen Harris), spines on a single hippocampal axonal varicosity sometimes
arise from the same postsynaptic cell, but may also arise from distant processes
or separate neurons. (**need to check this**) 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 such synapses is increased in behavioral and electrical
plasticity paradigms. The implications of this finding for wiring diagram level
models of the learning process remain to be determined. It should be noted that
this phenomenon is not unique to the hippocampus—we have seen multiple
postsynaptic contacts increased on excitatory morphology presynaptic
varicosities in the visual cortex of rats reared in complex environments in
comparison to caged rats (Jones et al., 1997). Moreover, Federmeier {in the
earlier today version I cited Harris / Stevens cerebellum paper. That one had
something like 70% of PC spines onto Pf varicosities coming from the same
Purkinje cell. That still is relevant to the Federmeier et al paper (submitted) which
found a dramatic increase in cerebellar msbs with acrobatic training.
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TA Jones et al have also described increases in these multiple synapses
in the intact cortex in the course of compensatory changes following unilateral
cortical lesions, and a relatively early report indicated increased multiple
postsynaptic innervation of presynaptic terminals associated with the open eye in
monocularly-deprived (kittens?) (M. Friedlander).
Persistence of neuronal changes
TESS’s EC REVERSAL EFFECT ON SYNAPSE/NEURON, among others
And jeff’s stuff
NON-NEURONAL CHANGES
The complex environment paradigm has been used extensively to
examine the range of plasticity of non-neural elements, as well as neurons and
synapses, but these data have largely failed to become incorporated into
theoretical development regarding brain plasticity. Early in this history, for
example, 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, these changes parallel those observed in neuronal dendrites and
synapses. These observations suggest that all types of brain tissue may exhibit
plasticity, at least in regions such as the cerebral and cerebellar cortices where
this has been examined.
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Astrocytes and the Complex Environment
Following early reports on experience-induced plasticity of astrocytes
(Diamond et al. 1966; Szeligo, 1977 #117), Sirevaag and Greenough (1991)
found that the extent of GFAP-immunoreactive astrocytic processes was greater
in EC than in IC rats This effect
These effects of complexity that occur in brain areas that exhibit synaptic
plasticity are differentiable from increases in astrocytic processes that result from
stress seen, e.g., in the hippocampal formation (Sirevaag et al., 1991) and, at
least in cerebellar cortex, the volume fraction of astrocytes parallels plasticity of
synapse number on an individual animal basis (Anderson et al., 1994; see also
Sirevaag and Greenough, 1988). THIS SECTION SHOULD GO IN THE
DISSOCIATION OF PLASTICITY EFFECTS ON GLIA.
The source of such inhibition likely stems from extragranular layers of
cortex as rapid (physiologically-defined) changes in both visual (Trachtenberg,
2000 #188; Trachtenberg, 2001 #189) and somatosensory cortices (Diamond,
1994 #190) have been reported to occur prior to the expression of modifications
in layer IV. Finally, observations by Gilbert and colleagues (Gilbert, 1979 #191;
Gilbert, 1992 #192) further suggest that horizontal connections may be the
source of such changes in visuo- and topographic-maps.
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At a higher level of resolution, astrocytic process proliferation is more
specifically associated with synapses, which are more completely ensheathed by
astrocytic processes in EC rats (Jones and Greenough, 1996), presumably
reflecting the function of optimizing the synaptic microenvironment. There are
substantial reasons to expect astrtocytic plasticity to have functional
consequences. Glia influence synaptic function in ways that range from efficacy
modulation (e.g., Araque et al., 1998; Smit et al., 2001) to the apparent
dissection of presynaptic from postsynaptic processes in synaptic remodeling
(e.g., Hatton, 1997; Meshul et al., 1987; Salm, 2000). Astrocytes can conduct
excitation via propagated Ca2+ waves (e.g., Araque et al., 1999; Dani et al., 1992)
which interact with neuronal activity (e.g., Rouach et al., 2000). Glial cells take
up and metabolize glutamate and GABA (e.g., Bezzi et al., 1999; Schousboe et
al., 1992) and have receptors for many neurotransmitters such as norepinephrine
(Shao and Sutin, 1992) and glutamatergic NMDA (Muller et al., 1993),
metabotropic (Shelton and McCarthy, 1999) and AMPA receptors (Muller, 1992),
all systems implicated in multiple aspects of learning and memory processes.
Considering these observations of the interaction between the astrocytes and
what would appear to be “synaptic” transmission, it seems likely that for an
animal to learn and to remember, there must be plastic changes in glia as well as
neurons.
Myelination and the Complex Environment
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Myelination continues well into human adulthood (Yakovlev and Lecours,
1967; Benes et al., 1994), and the first evidence for experience effects on
oligodendrocyte development came from Szeligo and Leblond (1977).
Subsequently Juraska and Kopcik (1988) found that EC rats had more
myelinated axons than IC rats in the splenial corpus callosum. A recent finding
(Briones et al., 1999) indicates that experience continues to have dramatic
effects on the adult myelination process in the splenial corpus callosum.
Subsequent work by Juraska and Kopcik (1988) indicated enhanced
myelination of splenial axons in EC rats.
Plasticity of Vasculature
Perhaps surprisingly, both in terms of earlier reports (e.g., Diamond et al.,
1964; Rowan and Maxwell, 1981) and in terms of long held beliefs regarding
vascular development (Bar, 1980), the capillary system exhibits two forms of
plasticity: Capillaries are both larger, on average, and more frequent, it terms of
measures such as the fractional volume of capillaries per neuron, as shown in
Figure 1. In fact, on a percentage change per neuron basis, capillaries exhibit far
more plasticity than synapses, with an increase of over 80% in volume fraction
per neuron in rats placed in complex environments at weaning (Black et al.,
1987; Sirevaag et al., 1988). While the greatest capillary plasticity is seen in
weanlings, this plasticity continues into adulthood, diminishing with age (Black et
al., 1989).
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Persistence of non-neuronal changes
As with those for the addition of synapses (e.g., Camel et al., 1986 ;
(Kleim et al., 1997; Briones et al., in preparation), these effects of experience on
myelination exhibit a step-wise effect: unlike experience induced changes in glia
which appear to fade rapidly once differential experience is discontinued (e.g.,
Kleim et al., in revision), changes in myelination appear to be stable across a
subsequent 30 day period of return to an individual cage housing condition
(Briones et al., 1999; Figure 2). It is interesting to speculate that added synapses
and myelin are stable because they represent permanent, survival-important
additions to the “wiring diagram” of the brain whereas astrocytic and possibly
vascular (yet to be tested) changes are adjustments to immediate demands of
experience that can be reversed, saving metabolic investment, in the absence of
continued environmental pressure.
Summary: EC-based description of neuronal & non-neuronal
plasticity
whether measures of the effects of learning on functional (synaptic) brain
organization may already be affected by these nonsynaptic changes
The overriding message, in any case, is that the brain is the organ of
adaptation—the interface between the individual and its environment. As such it
dynamically adjusts to the past demands placed upon it by experience, as if
assuming that the experiences of the past must be good predictors of the future.
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Abraham and Tate (Abraham and Tate, 1997) termed this concept of past
experiences predicting, or predisposing the system to subsequent activity as
“metaplasticity.” This idea is consistent with a wide-body of literature where
seemingly “sub-optimal” patterns of activity have pronounced effects on
subsequent responses (e.g., Frey and Morris, 1997). Moreover, technological
advances are now revealing that what may once have been thought of as a nonexistent synaptic response was actually the inability to observe the response –
just because we didn’t see it doesn’t mean it’s not there. For example,
modulation of intracellular pathways (REF) or the insertion/activation of “silent”
synapses (Atwood and Wojtowicz, 1999) are mechanisms whereby similar
patterns of synaptic activation could produce distinctly different subsequent
responses.
plasticity not as age-restricted as people believe (vis ctx &
cerebellum) I’m not sure where to put this!!!
Similarly, these forms of neuronal plasticity occur relatively consistently
across the age spectrum and do not appear to be restricted to particular critical
or sensitive periods of development. Exposure to a complex environment has
been shown to affect dendritic field dimensions in the visual cortex of adult,
middle-aged and even elderly rats (e.g., Juraska et al., 1980; Green et al., 1983;
Black et al., 1986; Connor et al., 1981). Synapse numbers per neuron are
increased in adults placed in complex environments (Briones et al., in
preparation). Likewise, cerebellar plasticity (Floeter and Greenough, 1979;
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Pysh and Weiss, 1979), while not thoroughly studied, seems to occur throughout
the lifespan, although this may occur in the context of a decline in elderly animals
(e.g., Greenough et al., 1986).
What drives the plasticity of brain tissues?
here we start to parcel out the dichotomies of trainings that induce plasticity
(activity vs. metabolic; learning vs activity; skill vs reach; LTP vs EC). in this
section, I see a parallel-structured discussion of whether each specific form of
plasticity (within the broader neuronal and non-neuronal categories) was involved
in that type of training
The existence of short-term and long-term processes of brain cellular
adaptation, and the fact that physical activity HAS THE IDEA OF PHYSICAL
ACTIVITY INDUCING CHANGES BEEN BROUGHT UP YET? and learning are
both involved in the behavioral events that appear to drive these processes,
leads to the next natural question: what causes changes in neurons, glia and
vasculature.
neuronal activity (learning or motor/sensory activity) or more nonspecific (metabolic/endocrine) changes
At the topmost level (as opposed, say, to the level of trophic factors or
receptor subunits), we can propose two general causal forces at the behavioral
level: the general categories of activity and learning. With regard to activity, 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, via
intermediary cellular events, might similarly trigger neuronal, glial or vascular
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hypertrophy of the sort seen in rats after complex environment housing. With
regard to learning, it seems plausible that certain changes in neurons, astrocytes
or oligodendrocytes might be learning-specific, although it is harder to imagine
that changes in capillaries would play very specific roles in learning.
At a broader level, it is also possible to imagine that responses to training
such as stress, or related metabolic consequences of behavioral manipulations
could lead to changes in brain tissue. Certainly stress can have negative
consequences for at least some brain regions (e.g., Sapolsky, 1996), although
the adrenal hypertrophy-correlated astroglial changes in the hippocampal
formation appear to be dissociated from the experience-correlated visual cortex
changes in complex environment research (Sirevaag et al., 1991).
early models to tease apart these two
chang and greenough ’82
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 consequences of learning would be focused in particular regions in 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) such that the unilaterally-trained rats should have most
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training input restricted to the hemisphere opposite the open eye, whereas the
bilaterally-alternating training should have allocated the learning input 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. Controls were
surgically operated and subsequently handled but not trained were divided into
unilaterally and bilaterally occluded groups.
plasticity (dend. branching) limited to where neural activity occurred
The result indicated increased dendritic branching, a correlate of
increased synapse number, in both hemispheres of the alternately trained group
relative to the non-trained group and in the non-occluded hemisphere of the
unilaterally trained group, compared to the occluded hemisphere. This result
indicates that either training or training-related activity drives dendritic plasticity.
reach training -- again, plasticity (dend. branching) limited to where
neural activity occurred
Greenough et al. (1985) used unilateral and bilateral training to study the
effects of forelimb reaching on plasticity in rat somatosensory-somatomotor
cortex of the trained vs. untrained (or activated vs. unactivated) hemisphere.
These animals were compared to untrained controls. The results in this study
were similar to those of Chang and Greenough (1982): for deep pyramidal
neurons of the type that control forelimb activity, dendritic branching was greater
in the hemisphere opposite trained forelimbs. For more superficial pyramidal
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neurons, there were effects of training, but these effects were not restricted to the
“trained” side in unilaterally-trained animals do you not want to mention the
forked-apicals? (Withers and Greenough, 1989). Taken with the study above,
the results indicate that learning or some other aspect of training-related activity
drives morphological change in neurons. Both experiments make clear that a
generally-acting hormonal or metabolic effect would be expected to alter
comparable regions of the brain whether or not they were beinXXXXXXXx Bill,
not really sure what you meant to say here….
how to distinguish between learning- and motor/sensory activitybased neural activity??
An obvious issue remaining is that with which we opened this section:
whether activity or learning causes structural changes in the brain.
AC/VX/IC I’m trying to combine work in cerebellum & motor cortex
To address this directly, Black et al. (1990) created adult female rat
groups that had 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 learning. Voluntary eXercise
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(VX) rats had access to running wheels attached to their cages and were the only
group to exhibit 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. Results were clear
in initial studies focusing on cerebellar cortex.
the rest of this segment goes like this…. learning was associated with
neuronal changes; activity was associated with non-neuronal changes.
(observed in both cerebellar and motor cortex)
When the number of synapses per neuron was measured, shown in
Figure 3A, the learning group, AC, exceeded the other 3 groups, which did not
differ, suggesting that when learning takes place (and not just as a result of
neural/motor activity), new synapses are formed. By contrast, as Figure 3B
shows, when blood vessel density was measured, the FX and VX groups both
had more than the AC or IC groups, which did not differ; this suggests that the
formation of new capillaries was driven by neural activity, and not by learning.
(the role of this and other non-neuronal changes will be discussed further below)
It should be noted that these effects are not limited to cerebellar cortex.
Kleim et al. (papers and absts) have described synaptogenesis and changes in
synapse morphology in association with the same AC motor learning procedure
in the somatosensory-somatomotor forelimb cortex of rats. The first
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morphological change to occur is, on average, an increase in the size of PSDs,
PSD BEEN DEFINED YET? which occurs within one to two days after training
begins. Subsequently, at the next day examined, day 5, an increase in the
number of synapses per neuron was detected, and the average size of synapses
decreased, possibly because the new synapses were, on average, smaller than
the pre-existing synapse population. The increase in synapse number was
maintained, drifting slowly, but not statistically, upward across the remainder of
training. As training progressed, the average size of synapses again increased,
possibly suggesting that the new synapses were growing larger or that the
population of synapses overall was doing so. A schematic interpretation of these
findings appears in Figure 4. There is a long history of evidence for involvement
of synapse size changes in plasticity that cannot be reviewed here due to space
limitations (is there a Harris or other review to which we could refer?) (Ask
Jeff for input on this paragraph.)
There is one other interesting thing about these (cerebellar) synapses—
many of them involve additional postsynaptic spines contacting presynaptic
varicosities on which one or more spines already exist (Federmeier et al.,
submitted). The implications of this finding for wiring diagram level models of the
learning process remain to be determined. It should be noted that this
phenomenon is not unique to the cerebellum—we have seen multiple
postsynaptic contacts increased on excitatory morphology presynaptic
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varicosities in the visual cortex of rats reared in complex environments in
comparison to individually-caged control rats (Jones et al., 1997).
Role of non-neuronal changes in learning / activity based plasticity. –
MAINTENANCE!!!!
One is Brenda's 1994? Paper showing the correlation between synapse number
and astrocyte Vv. The other is Jeff's astrocyte persistence paper.
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.
BILL, IT SEEMS THAT MOST OF THIS PARAGRAPH PROVIDES FURTHER
SUPPORT FOR THE NON-GLOBAL (METABOLIC) EFFECT OF ACTIVITY ON
PLASTICITY, BUT DOESN’T SAY MUCH ABOUT “DIFFERENT TYPES OF
PLASTICITY. MAYBE YOU CAN INTEGRATE IT INTO OTHER PARTS OF
THE TEXT, OR MOVE THE WHOLE THING…..
Although differential experience can induce widespread plastic changes
within the brain, the concept that different kinds of plasticity occur in different
situations, and suggests that the type and location of the plasticity is dependent
upon the nature of the experience (Morris et al., 1989; Klintsova & Greenough,
21
1999). As discussed above, motor training experiences that involve the
development of motor skill induce changes in synapse number within the
cerebellar and motor cortices while extensive repetition of unskilled movements
causes non-neuronal changes, but no change in synapse number (Black et al.,
1990; Kleim et al., 1998c; Kleim et al., 1996; Kleim et al., 2002b). Similarly, the
acquisition of skilled forelimb movements causes a 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., 2002a) and forelimb strength training (Remple et al., 2001) do not.
However, strength training does increase synapse number within the ventral
spinal cord but motor skill training does not (Kleim et al., 2001). Differential
patterns of plasticity can also be observed across different forms of learning. For
example, complex motor skill training does not alter synapse number within the
deep cerebellar nuclei (Kleim et al., 1998b) whereas eye blink conditioning does
(Bruneau et al., 2001). BUT WHO IS SAYING THAT THESE DIFFERENT
FORMS OF ACTIVITY ARE UTILIZING THE SAME AREAS??? CAN WE BE
MORE SPECIFIC ABOUT “DEEP CEREBELLAR NUCLEUS”?? Even within a
specific learning experience plasticity can be found within some brain regions but
not others. DOESN’T THIS SIMPLY SAY THAT NOT ALL BRAIN AREAS ARE
INVOLVED IN ALL BEHAVIORS? Complex housing causes dendritic
hypertrophy in visual and sensory cortices but not in prefrontal or temporal cortex
(Kolb ref). SAME ISSUE AS ABOVE. Skilled forelimb reach training causes a
reorganization of movement representations and an increase in synapse number
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within the caudal forelimb area but not within the neighboring rostral forelimb
area (Kleim et al., 2002b). THIS SEEMS OUT OF PLACE, IMPORTANT, BUT
NOT IN THE RIGHT SPOT . Interestingly, reach training induced increase of
field potential in forelimb contralateral to preferred limb (vs. ipsilateral) in layer
II/III (Rioult-Pedotti, 1998 #179), suggesting a selective strengthening of
horizontal cortical connections associated with learning new motor skill.
Complex motor training is associated with an increase in synapse number within
the cerebellar cortex (Kleim et al., 1998c) but not within the deep cerebellar
nuclei (Kleim et al., 1998b). REDUNDANT FROM ABOVE. The specificity of the
plasticity can even be reduced to subpopulations of neurons within the same
brain region. For example, complex housing causes dendritic hypertrophy within
cerebellar Purkinje cells but not granule cells (Floeter and Greenough, 1979).
Reach training causes dendritic hypertrophy within layer II/III of the motor cortex
that is restricted to a specific class of pyramidal cells (Withers and Greenough,
1989). Finally, plasticity can even be observed to be restricted to specific
afferents onto individual neurons. Complex motor skill training causes an
increase in parallel fiber synapses onto Purkinje cells but not climbing fibers
(Kleim et al., 1998c). Eyeblink conditioning causes an increase in the number of
excitatory synapses within the anterior interpositus without alter inhibitory
synapse number (Bruneau et al., 2001). Similarly strength training causes an
increase in excitatory but not inhibitory axosomatic synapses within the ventral
spinal cord (Kleim et al., 2001).
THESE CONCEPTS SEEMS TO GO WITH THIS SECTION:
23
Synaptic specificity supported by “synaptic tag” that is localized and
protein-synthesis independent (Frey, 1997 #56). 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
plasticity” (as defined anatomically), with physiological correlates (Yun, 2002
#106; Trepel, 1998 #99).
these next two sections seem out of place now…..
This goes on the end or might be placed elsewhere:
A note on Long-term potentiation
Engert and Bonhoeffer have reported apparent synaptogenesis in vitro in
association with LTP induction. High-frequency stimulation produced enhanced
growth of filopodia-like protrusions in CA1 slices (viewed with 2-photon), an effect
that was blocked by NMDAR antagonism (Maletic-Savatic, 1999 #180).
ALSO WORK OF ANDERSEN AND SOLENG (Andersen, 1998 #181)
WHO SHOWED SYNAPTOGENESIS ASSOCIATED WITH LTP AND SPATIAL
24
LEARNING (THEY SUGGESTED BIFURCATION/BRANCHING OF EXISTING
SPINES)
At least 3 studies, HOWEVER, dissociate LTP from spatial behavior and
morphological change. The primary point I want to make is the apparent
dissociation of LTP from EC effects on synapses published by J. Tsien in Nature
Neuroscience. This suggests that LTP and synaptogenesis are independent
phenomena. I am not sure what the range of the evidence is or the weight of it
(e.g., other more recent work that bears on this issue, most of which are likely to
have cited both of the above studies (Tsien and E-B) and hence should be
locatable via the science citation index, which I have not used for the last million
years), but the dissociation to me seems most powerful-synapse addition may
mediate LTP, but synapse addition need not involve an LTP-like process for its
induction.
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 literature
investigating this phenomenon. Synaptic and dendritic protein synthesis have
been shown to be activated by metabotropic glutamate receptors 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
25
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). Plasticity-inducing
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 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.
The principal thing I want to add at this point is a summary that comes
back to the main point of the chapter--that we are only looking at a small portion
of what the brain does when it accomplishes plastic change. I really would like
your feedback on the earlier stuff. I have attached a copy of the chapter file as it
exists on my computer. There is a demarcated line below which all of my
additions occur, so it should be possible to just paste what you have and the part
that I added together.
ANOTHER “TYPE” OF PLASTICITY TO CONSIDER: NEUROGENESIS.
26
Housing in an complex environment resulted in enhanced survival of “new
neurons” (aka, neurogenesis) (tested 4 weeks later) but no effect on number
generated (tested 1 day after BrdU injection) (Nilsson, 1999 #83).
Neurogenesis rate is doubled in dentate following training on an
associative learning task requiring hippocampus (Gould, 1999 #183).
DO WE WANT TO DISCUSS ANY SORT OF TEMPORAL COMPONENT
THAT COULD DIFFERENTIALLY INFLUENCE THE “TYPE” OF PLASTICITY
GOING ON? FOR EXAMPLE:
Consider temporal component of morphological changes. For example,
following one-trial learning, the density of axospinous synapses was increased
77% in IMHV of chicks and PSD (measured by “height” of synapse) was
decreased at 1 HR post training. Yet 24-hours later there were no differences
(Doubell, 1993 #184). Of course this is consistent with Kleim work, possibly
integrate this study with that section???
STRUCTURE-FUNCTION RELATIONSHIP, NEEDS TO BE
INCORPORATED SOMEWHERE:
Housing in complex environment resulted in 50% increase in somatotopic
representation of forepaw, most of which came from glaborous surface and more
specifically, from digit tips (Coq, 1998 #53).
Primary neurons and the cortex: Is the current approach overly-restrictive?
27
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. (Seress, 2001 #92) have reported that 95% of terminals
forming asymmetric synapses with parvalbumin-positive dendrites in the dentate
and strata pyramidale and lucidum of CA3 originated from granule cells. SO
WHY DO WE ALWAYS LOOK @ PYRAMIDAL CELLS? Modification of
“modulatory” neurons can have dramatic effects on the function of primary
neurons. For example, receptive field size in sensory cortex has been shown to
be sensitive to pharmacological disinhibition (Tremere, 2001 #185; Tremere,
2001 #186; Jacobs, 1991 #187). The source of such inhibition likely stems from
extragranular layers of cortex as rapid (physiologically-defined) changes in both
visual (Trachtenberg, 2000 #188; Trachtenberg, 2001 #189) and somatosensory
cortices (Diamond, 1994 #190) have been reported to occur prior to the
expression of modifications in layer IV. Finally, observations by Gilbert and
colleagues (Gilbert, 1979 #191; Gilbert, 1992 #192) further suggest that
horizontal connections may be the source of such changes in visuo- and
topographic-maps.
The cerebral cortex is often-times considered “where” changes occur,
ignoring subcortical contributions and importance. Numerous changes outside
cortex such as striatum (Comery, 1995 #21; Comery, 1996 #22), spinal cord
28
(Devor, 1978 #193), temporal dynamics of change are distributed across the
neuroaxis (Faggin, 1997 #194),
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. Blah, blah, blah.
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, 2001 #100). Moreover, even
when synapses do form, they are functionally immature. Obvious implications on
studies of “synaptic plasticity” in vitro.
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
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,
29
perfs, interpret multiple synapses from local and wiring diagram view. NOT
SURE WE HAVE TIME FOR THIS NOR IS THIS NECESSARILY CONSISTENT
WITH UNDERLYING THEME OF THE PAPER.
30
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