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

A Brain Adaptation View of Plasticity:
Is Synaptic Plasticity An Overly Limited Concept?
There is a long tradition, traceable to the early musings of Ramon y Cajal,
of focusing upon the neuron as the only plastic cell type of any importance within
the brain, and upon the synapse as the only important plastic aspect regulating
the interactions among neurons. While neuronal plasticity is without question an
important aspect 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. Moreover, it is becoming clear in work of others that there are
multiple forms of synaptic plasticity: the synaptic number response to a complex
environment, for example, occurs in animals genetically rendered incapable of
the most common form of LTP. Our work and that of others indicates that
oligodendrocytes, astrocytes, vasculature, and perhaps other cellular elements
exhibit plasticity quantitatively equivalent to that of neurons in the developing and
mature brain and that modifications of these cellular elements may be brought
about by experience. It also suggests that multiple forms of plasticity may occur
at the synapse. In short, while memory researchers largely focus on naturallyand artificially-induced changes in synaptic connectivity, the brains of real
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. The intent of this review is to consider the data that point to this view and
consider how we might assess nonsynaptic effects of learning and/or determine
whether the effects of learning upon measures of brain functional organization
may already be affected by these nonsynaptic changes.
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 depended on
development of adequate tools, but, by the early 1970s, electrophysiological and
anatomical evidence for the ability of the nervous system to alter its functional
connectivity in accord with its experience was becoming reasonably well
established. Electrophysiologically, long-term potentiation had been described
(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 ); exposure to a complex environment from
weaning through adolescence increased dendritic field dimensions (Volkmar
and Greenough, 1972) and synaptic size (West and Greenough, 1972)
in the rat visual cortex; and parallel findings were described in invertebrates,
where there was more precise knowledge of the underlying structure-function
relationship (e.g., Bailey and Chen, 1988). This paper summarizes progress in
understanding the brain plasticities thought to be associated with learning and
memory, focusing heavily on our own work, since those relatively early
beginnings.
A relatively early specific demonstration that synapses formed in response
to experience was the report by Turner and Greenough (1985) that there were
more synapses per neuron in upper layers of the visual cortex in rats that had
been reared from weaning in a complex environment. This rearing and adult
housing paradigm, pioneered by Hebb (1949) and his students (e.g. Hymovitch,
19xx Jay has this one) using behavioral measures, and first used as a tool for
exploring brain plasticity by Krech, Rosenzweig, Bennett and colleagues (e.g.,
Bennett et al., 1964), has been used extensively to examine the range of
plasticity of various elements of brain other than neurons, but most of these
results have failed to become incorporated into the continuing literature on brain
plasticity. Early in this history, Diamond (Diamond et al., 1966) reported
that glial cells in visual cortex exhibited morphological changes in response to
experience in parallel with the changes reported in neuronal dendrites and
synapses. We have subsequently examined plasticity of non-neuronal elements
of the cerebral cortex in response to complex environment exposure in some
detail. These results are addressed at a later point in this chapter.
Following up on these initial demonstrations of dendritic and synaptic
responsiveness to rearing conditions, neuronal responsiveness to neuronal
activity and experience has subsequently been demonstrated in a wide variety
of brain structures: hippocampus ( Moser et al., 1994;), basal ganglia
(Comery et al., 1995; Comery et al., 1996), cerebellar cortex
(Greenough et al., 1986) —someone to fill this in with both our data
and others.** 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]) [Jeff can add more here or elsewhere]
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. Effects of adult exposure to a complex
environment have 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; Conner et al., 1981).
Synapse numbers per neuron are increased in adults placed in complex
environments (Briones et al., emailed as in prep). Likewise, cerebellar
plasticity, 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.,
(Floeter and Greenough, 1979); Pysh & Weiss, 1979; (Greenough et
al., 1986)).
It should be briefly noted that there is a reason that the oft-used term
“enriched” is avoided here. 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 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, just need one ref can’t find). 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 human development (e.g., Hart and Risley, 1995).
Non-neural forms of brain plasticity
This neuronal/synaptic plasticity is accompanied by plasticity of non-neural
elements such as glia and vasculature. Subsequent work by Juraska and Kopcik
(1988) indicated enhanced myelination of of splenial axons in EC rats. Further
work has suggested that all type of brain tissue may exhibit plasticity, at least in
regions such as the cerebral and cerebellar cortices where this has been
examined. Perhaps surprisingly, both in terms of earlier reports (e.g., Diamond
??64 (Rowan and Maxwell, 1981) See Black et al 1987 reprint) and in
terms of long held beliefs regarding vascular development ( e.g., 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 Fig. 1 (as we discussed). 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); As we noted, check % and left panel of graph).
While the greatest capillary plasticity is seen in weanlings, this plasticity
continues into adulthood, diminishing with age (Black et al., 1989).
Similarly, astrocytic plasticity evident in measures of nuclei (?(Diamond
et al., 1966); Sirevaag and Greenough, 1987) reflects plasticity of
astrocytic branching. The extent of GFAP-immunoreactive astrocytic processes
was greater in EC than in IC rats (Sirevaag and Greenough, 1991).
These effects of complexity that occur in brain areas that exhibit synaptic
plasticity are differentiable from effects of stress seen, e.g., in the hippocampal
formation (Sirevaag et al., 1991) and, at least in cerebellar cortex,
parallel plasticity of synapse number (Anderson et al., 1994, see
below); see also (Sirevaag and Greenough, 1988). 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.
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 (Szeligo and
Leblond, 1977). Subsequently Juraska and Kopcik 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.
Particularly interesting is that these effects of experience on myelination, as with
those for the addition of synapses (e.g., (Camel et al., 1986; see also
Kleim et al., 1997a below); Briones, synapse adult in preparation )
exhibit a “ratchet” effect: unlike experience induced changes in glia which appear
to fade rapidly once differential experience is discontinued (e.g., Kleim SFN
abstract—paper still to be written IN REVISION), changes in myleination appear
to be stable across a subsequent 30 day period of return to an individual cage
housing condition (Briones SFN abstract; Fig. X). 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 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.
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.
What drives the plasticity of brain tissues?
The existence of short-term and long-term processes of brain cellular
adaptation, and the fact that physical activity 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. 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 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).
To examine the roles of learning vs. other consequences of training 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. Both groups had been
previously subjected to transection of the corpus callosum, a “split-brain”
procedure that disrupts communication between the two hemispheres of the
brain, such that the unilaterally-trained rats should have most training input
restricted to the hemisphere opposite the open eye, whereas the bilaterallyalternating training should have allocated the learning input about equally to both
hemispheres. Controls that were surgically operated and subsequently handled
but not trained were divided into unilaterally and bilaterally occluded groups. The
result indicated increased dendritic branching, a correlate of increased synapse
number, in both hemispheres of the alternately trained group relative to the nontrained 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.
A similar study used unilateral vs. bilateral training to reach with the
forelimb into a chamber for food with similar untrained controls, but without
surgical intervention. The results in this somatosensory-somatomotor study were
similar: for deep pyramidal neurons of the type that control forelimb activity,
dendritic branching was greater opposite trained forelimbs (Greenough et
al., 1985); for more superficial pyramidal neurons, there were effects of
training, but these effects were not restricted to the “trained” side in unilaterallytrained animals (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 bein
An obvious issue remaining is that with which we opened this section:
whether activity or learning causes structural changes in the brain. 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 (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 or learning. Results were clear in initial studies focusing
on cerebellar cortex. As Fig. XA 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. By contrast, when the number of synapses per neuron was
measured, shown in Fig. XB, the learning group, AC, exceeded the other 3
groups, which did not differ, suggesting that when learning takes place, new
synapses are formed.
There is one other interesting thing about these synapses—many of them
involve additional postsynaptic spines contacting presynaptic varicosities on
which one or more spines already exist (Federmeier et al., submitted). A similar
result has recently been reported by [Geinisman/Disterhoft](Geinisman et
al., 2001) in the ? cerebellar lobule see email following associative eyeblink
conditioning. In general, according to (Kristen Harris), spines on a single
cerebellar parallel fiber varicosity tend to arise from the same postsynaptic cell,
such that they are separate parallel lines between a parallel fiber and a Purkinje
(or stellate) neuron NEW NATURE NS PAPER ENTITLED “DENDRITIC SPINES
DO NOT SPLIT DURING HIPPOCAMPAL LTP OR MATURATION” BY HARRIS
TO CONSIDER. 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
varicosities in the visual cortex of rats reared in complex environments in
comparison to caged rats (Jones et al., 1997).
[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).
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 morphological
change to occur is, on average, an increase in the size of PSDs, 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 Fig. X. 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.)
Need a paragraph on perforated synapses. Start with Greenough west devoogd,
Science 1978 EC IC and developmental age findings. Include Geinisman LTP
findings, other things included in Chapters that Chang & Greenough did for
Cotman and Jones/Peters volumes (Greenough, W. T., & Chang, F.-L. F. Plasticity
of synapse structure and pattern in the cerebral cortex. In E. G. Jones & A. Peters (Eds.),
Cerebral Cortex. Vol. 7. New York: Plenum, 1988, pp. 391-440.) Include Jeff's stuff
from Motor Cortex, any other recent work that supports this as a possible plasticity
mechanism. We could think about interpretations.
Are there other synapse modifications to be discussed. Given space constraints I
suggest we merely mention vesicle aggregate size and some other possible measures and
refer to review articles.
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. One is Brenda's 1994? Paper
showing the correlation between synapse number and astrocyte Vv. The other is Jeff's
astrocyte persistence paper. 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 goes on the end or might be placed elsewhere:
A note on Long-term potentiation
Tell me if this seems out of place. At least 3 studies 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.
However, Engert and Bonhoefferhave reported apparent synaptogenesis in vitro in
association with LTP induction. 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 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. If you want to take a
crack at this, feel free. Otherwise I will.
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 ibnclude 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). 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.
Summary and Concerns: Synaptic Plasticity and Beyond
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.
CHAPTER WORKING NOTES:
Tissue cultures lacking astrocytes—how good a model? 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,
perfs, interpret multiple synapses from local and wiring diagram view.
Also local regulation in dendrites; protein synthesis; dynamic view;
incorporate FMRP in this context
Do also a TINS—go head to head with Menahem
Turner, A. M. & Greenough, W. T. Synapses per neuron and synaptic
dimensions in occipital cortex of rats reared in complex, social, or isolation
housing. Acta Stereologica, 2 (Suppl. 1):239-244, 1983.
Turner, A. M. & Greenough, W. T. Differential rearing effects on rat visual cortex
synapses. I. Synaptic and neuronal density and synapses per neuron. Brain
Research, 329:195-203, 1985.
Anderson, B. J., Li, X., Alcantara, A. A., Isaacs, K. R., Black, J. E. and
Greenough, W. T. (1994) Glial hypertrophy is associated with synaptogenesis
following motor-skill learning, but not with angiogenesis following exercise.
Glia 11, 73-80.
Bennett, E. L., Diamond, M. C., Krech, D. and Rosenzweig, M. R. (1964)
Chemical and anatomical plasticity of brain. Science 146, 610-619.
Black, J. E., Polinsky, M. and Greenough, W. T. (1989) Progressive failure of
cerebral angiogenesis supporting neural plasticity in aging rats. Neurobiol
Aging 10, 353-8.
Black, J. E., Sirevaag, A. M. and Greenough, W. T. (1987) Complex experience
promotes capillary formation in young rat visual cortex. Neurosci Lett 83, 3515.
Bliss, T. V. and Lomo, T. (1973) Long-lasting potentiation of synaptic
transmission in the dentate area of the anaesthetized rabbit following
stimulation of the perforant path. J Physiol 232, 331-56.
Comery, T. A., Shah, R. and Greenough, W. T. (1995) Differential rearing alters
spine density on medium-sized spiny neurons in the rat corpus striatum:
evidence for association of morphological plasticity with early response gene
expression. Neurobiol Learn Mem 63, 217-9.
Comery, T. A., Stamoudis, C. X., Irwin, S. A. and Greenough, W. T. (1996)
Increased density of multiple-head dendritic spines on medium-sized spiny
neurons of the striatum in rats reared in a complex environment. Neurobiol
Learn Mem 66, 93-6.
Diamond, M. C., Law, F., Rhodes, H., Lindner, B., Rosenzweig, M. R., Krech, D.
and Bennett, E. L. (1966) Increases in cortical depth and glia numbers in rats
subjected to enriched environment. J Comp Neurol 128, 117-26.
Floeter, M. K. and Greenough, W. T. (1979) Cerebellar plasticity: modification of
Purkinje cell structure by differential rearing in monkeys. Science 206, 227-9.
Green, E. J., Greenough, W. T. and Schlumpf, B. E. (1983) Effects of complex or
isolated environments on cortical dendrites of middle-aged rats. Brain Res
264, 233-40.
Greenough, W. T., McDonald, J. W., Parnisari, R. M. and Camel, J. E. (1986)
Environmental conditions modulate degeneration and new dendrite growth in
cerebellum of senescent rats. Brain Res 380, 136-43.
Hart, B. and Risley, T. R. (1995) Meaningful Differences in the Everyday
Experience of Young American Children. Brookes Publishing: Baltimore.
Hebb, D. O. (1949) The organization of behavior. Wiley: New York.
Jones, T. A. and Greenough, W. T. (1996) Ultrastructural evidence for increased
contact between astrocytes and synapses in rats reared in a complex
environment. Neurobiol Learn Mem 65, 48-56.
Juraska, J. M., Greenough, W. T., Elliott, C., Mack, K. J. and Berkowitz, R.
(1980) Plasticity in adult rat visual cortex: an examination of several cell
populations after differential rearing. Behav Neural Biol 29, 157-67.
Juraska, J. M. and Kopcik, J. R. (1988) Sex and environmental influences on the
size and ultrastructure of the rat corpus callosum. Brain Res 450, 1-8.
Kleim, J. A., Pipitone, M. A., Czerlanis, C. and Greenough, W. T. (1998)
Structural stability within the lateral cerebellar nucleus of the rat following
complex motor learning. Neurobiol Learn Mem 69, 290-306.
Moser, M. B. (1999) Making more synapses: a way to store information? Cell Mol
Life Sci 55, 593-600.
Moser, M. B., Trommald, M. and Andersen, P. (1994) An increase in dendritic
spine density on hippocampal CA1 pyramidal cells following spatial learning
in adult rats suggests the formation of new synapses. Proc Natl Acad Sci U S
A 91, 12673-5.
Moser, M. B., Trommald, M., Egeland, T. and Andersen, P. (1997) Spatial
training in a complex environment and isolation alter the spine distribution
differently in rat CA1 pyramidal cells. J Comp Neurol 380, 373-81.
Ramon y Cajal, S. (1893) New findings about the histological structure of the
central nervous system. Archiv fur Anatomie und Physiologie (Anatomie),
319-428.
Sirevaag, A. M., Black, J. E. and Greenough, W. T. (1991) Astrocyte hypertrophy
in the dentate gyrus of young male rats reflects variation of individual stress
rather than group environmental complexity manipulations. Exp Neurol 111,
74-9.
Sirevaag, A. M., Black, J. E., Shafron, D. and Greenough, W. T. (1988) Direct
evidence that complex experience increases capillary branching and surface
area in visual cortex of young rats. Brain Res 471, 299-304.
Sirevaag, A. M. and Greenough, W. T. (1987) Differential rearing effects on rat
visual cortex synapses. III. Neuronal and glial nuclei, boutons, dendrites, and
capillaries. Brain Res 424, 320-32.
Sirevaag, A. M. and Greenough, W. T. (1988) A multivariate statistical summary
of synaptic plasticity measures in rats exposed to complex, social and
individual environments. Brain Res 441, 386-92.
Sirevaag, A. M. and Greenough, W. T. (1991) Plasticity of GFAP-immunoreactive
astrocyte size and number in visual cortex of rats reared in complex
environments. Brain Res 540, 273-8.
Tanzi, E. (1893) Facts and inductions in current histology of the nervous system.
Rivista sperimentale di freniatria e medicina legale delle mentali alienazioni
19, 419-472.
Turner, A. M. and Greenough, W. T. (1985) Differential rearing effects on rat
visual cortex synapses. I. Synaptic and neuronal density and synapses per
neuron. Brain Res 329, 195-203.
Volkmar, F. R. and Greenough, W. T. (1972) Rearing complexity affects
branching of dendrites in the visual cortex of the rat. Science 176, 1145-7.
West, R. W. and Greenough, W. T. (1972) Effect of environmental complexity on
cortical synapses of rats: preliminary results. Behav Biol 7, 279-84.