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Master's Theses
City College of New York
2015
Refinement of feedforward projections, neuronal
density, and characterization of synapsesin layer 4 of
ferret primary visual cortex
Violeta Contreras Ramirez
CUNY City College
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Refinement of feedforward projections, neuronal density, and characterization of synapses
in layer 4 of ferret primary visual cortex
Violeta Contreras Ramirez
Mentor: Dr. Jonathan B. Levitt
A thesis submitted in partial fulfillment of the requirements for the BS/MS dual degree
program in the Biology Department of the City College of New York of the City University
of New York
2015
Introduction
We are interested in studying the organization and refinement of brain circuits in the
visual cortex. To accomplish this, we used a combination of light and electron microscope (EM)
techniques to quantify how various connectional characteristics in visual areas change
throughout development in the ferret. Having a richer understanding of the maturation of
different visual areas is relevant to understanding the neural correlates of the maturation of
different visual functions, since these do not all develop at the same age. For example, in
humans, cells show responses that are orientation-selective at around 6 weeks of age, but
responses to direction of motion do not occur until 10-12 weeks of age (Braddick et al., 1986;
Wattam-Bell, 1991).
The developing visual cortex is a very interesting model to study due to its ability to be
greatly affected by experience. The importance of experience in the refinement of visual
connections is illustrated by the finding that rats placed in a complex environment display 27%
more synapses per neuron than rats in a simple environment (Briones et al., 2004). Furthermore,
kittens with normal vision have a large portion of their visual cortex that is binocular, and thus,
does not show bias in eye specificity. Causing strabismus in kittens for 2 days leads to a smaller
binocular zone, while strabismus lasting for 7-14 days completely eliminates it, resulting in a
cortex that is biased for same-eye domains (Trachtenberg and Stryker, 2001). Finally, it has been
found that preventing the contralateral eye from opening at P9 in mice retards maturation of
ipsilateral retinotopy. In contrast, enucleating the contralateral eye leads to acceleration in
retinotopic map maturation by P15. Vision is necessary for this acceleration, as enucleating the
contralateral eye and preventing the ipsilateral eye from opening until P15 results in a retinotopic
1 map that is less organized than that found when the contralateral eye is enucleated but the
ipsilateral eye is allowed to open naturally between P10 to P12 (Smith and Trachtenberg, 2007).
While there are features whose development is clearly dependent on visual experience,
other features are partially intrinsic; but even in these cases, visual experience might still play a
role. Ko et al. (2014) found that mice that are deprived of vision from before eye opening still
develop orientation-selective cells in V1, just as controls do. In addition, there is an increased
probability for cells in layers 2 and 3 to form connections with other cells with similar
orientations, compared to with cells responding to orthogonal orientations, and this is seen in
dark-reared mice as well as controls. Nevertheless, visually unresponsive cells lose connections
made to visually responding cells in controls, but this loss is not present in dark reared mice (Ko
et al., 2014). Furthermore, the orientation selectivity present in visually deprived mice could
have developed as a result of visual experience before the mice opened their eyes. Krug et al.
(2001) provide evidence for orientation-selectivity in LGN and V1 neurons in ferrets in the two
weeks before eye opening. Their data suggest that visual stimuli could be involved in the
development of orientation selectivity even through closed eyelids. Thus, visual experience
could still be shaping the development of features that initially appear to be intrinsic.
A possible mechanism for the role of visual experience in refining connections in the
visual cortex may involve the action of astrocytes. Astrocytes are known to be involved in
guiding axons to their destinations, as well as in synapse formation and elimination. Since
neurogenesis occurs earlier than gliogenesis in the developing cortex, it is believed that neurons
may provide extrinsic cues that allow neural stem cells to differentiate into glial cells. In
particular, the IL-6 family of cytokines may promote the formation of astrocytes (Sloan and
Barres, 2014). Thus, it is reasonable to expect that cortical neurons receiving visual stimuli
2 would be activated more than non-visually activated neurons, and the former, more active
neurons will produce more cytokines, leading to the proliferation of more astrocytes. Astrocytes,
in turn, will be important for the elimination of synapses. Thus, this mechanism could explain
how visual experience leads to refinement of connections in the visual cortex.
A fundamental question in neuroscience is whether different visual areas mature with
similar or different timecourses. In order to shed light on this question, we quantified changes in
the density and distribution of boutons in feedforward projections, which are those that originate
in area 17 and extend to extrastriate areas (Callaway, 2004). By studying the distribution of
synaptic zinc in the ferret visual cortex, Khalil and Levitt (2013) found evidence for a similar
developmental timecourse of different visual areas. Synaptic zinc is present in a subset of
glutamatergic processes from various sources, and the intensity of the label declines with age.
The major decline occurred from 5 to 6 weeks, which is around the time of eye opening in the
ferret (P32). However, we are now interested in studying a specific type of glutamatergic
processes: feedforward projections. To do this, we used a tracer to label axons that project from
area 17 to extrastriate areas. We assessed the developmental timecourse of extrastriate areas by
quantifying bouton density, interbouton intervals, and axon density. Boutons are the anatomical
specializations by which neurons make synaptic contacts, and interbouton intervals refer to the
distance between adjacent boutons along individual axons. Thus, by quantifying boutons, we can
obtain an estimate of how connectivity changes during development. In addition, the interbouton
intervals and axon density allow us to determine if changes in bouton density are due to the
elimination of individual boutons, the pruning of axonal branches, or both of these processes.
Given that connections can be eliminated while the neuron survives, we decided to
compare the developmental changes in bouton density to those of neuronal density in different
3 visual areas. It is well established that the brain contains exuberant connections early in
development, and thus, we expected to find a decrease in feedforward projections with age.
Exuberance refers to the overproduction of axons and synapses and their selection for persistence
during development. Elimination of projections may result from neuronal death or the
elimination of synapses, and selection is determined by input from the periphery, competition
among axons, and hormones. It is believed that exuberance is important for modulating the
strength of connections and providing circuits with flexibility. Exuberance occurs in both
cortico-cortical and cortico-subcortical projections (Innocenti and Price, 2005). To compare
changes in bouton density with those in neuronal density, we obtained the latter measurement at
4 and 6 weeks of age, and adulthood. While the pruning of exuberant projections leads to a
decrease in connections during development, we expected to also find a decrease in neuronal
density due to the enlargement of the brain with age (and not due to cell death). The weight of
the ferret brain, which is indicative of its size, increases at a very fast rate from P0 to P30.
However, even after this age, it continues to increase in weight. From 4 weeks to 6 weeks, the
weight increases from approximately 5 to 7 grams, an increase of 40% (Barnette et al., 2009).
This increase in brain weight, and size, is coupled with a lack of neurogenesis. It has been
reported that in ferrets, neurogenesis in the visual cortex ends at approximately 2 weeks after
birth (Jackson et al., 1989). Thus, if the brain is getting bigger, and no more neurons are being
produced, then neuronal density should decline.
Having quantified a proxy for synapses, we then asked what particular aspects of
synapses are changing with age; thus, a final analysis we undertook was to use electron
microscopy to characterize different types of synapses in layer 4 of area 17 in the ferret. While
we hope to quantify synaptic changes during development at the EM level, we began by
4 describing several synaptic features in the adult, in order to understand what the organization of
the developing cortex will ultimately be. In addition to synapse density, we identified the postsynaptic targets as dendritic shafts or spines. While synapses can occur on both of these
structures, spines are the most common targets (DeFelipe et al., 1999). We also differentiated
between asymmetric and symmetric synapses. This categorization is important, as asymmetric
synapses are thought to be excitatory, and symmetric are thought to be inhibitory, with the
former being more common (DeFelipe et al., 1999). Thus, by quantifying these anatomical
characteristics, we can gain insight into the functional circuits in primary visual cortex.
After quantifying the above-mentioned characteristics in all types of synapses of layer 4
in V1, we differentiated between the measurements of cortico-cortical and thalamo-cortical
synapses. Given that the synapses we are interested in classifying are glutamatergic, they use a
glutamate transporter to recycle the neurotransmitter, and different populations of cells use
different glutamate transporters. The vglut1 transporter is believed to be used in cortico-cortical
synapses, while vglut2 is used in thalamocortical synapses. Indeed, Nahmani and Erisir (2005)
found that vglut2 immunoreactivity (ir) can effectively identify thalamo-cortical projections to
layer 4 of V1. Furthermore, they found that vglut2 is not used by cortico-cortical synapses, and
that vglut2 is present in boutons that form asymmetric synapses, indicating that these are
glutamatergic. Finally, these researchers identified that vglut2+ boutons have an even greater
tendency than cortico-cortical boutons to synapse onto spines compared to dendritic shafts (77%
vs. 59%) (Nahmani and Erisir, 2005). While vglut2+ puncta can be found in any layer of V1 of
monkey to different degrees, they are much more heavily concentrated in layer 4Cβ (GarciaMarin et al., 2012). Therefore, we were able to use vglut2 immunoreactivity to identify thalamo-
5 cortical synapses in layer 4 of V1 and compare the characteristics of these synapses with those of
cortico-cortical synapses in the same layer.
The reason for differentiating between particular types of synapses in specific layers is
that the visual cortex is very highly organized anatomically, and as such, a “canonical circuit”
has been proposed to describe the functional properties of the cortex. A canonical circuit is a
microcircuit repeated in all areas of the cortex, where it performs similar functional computations
(Douglas et al., 1989). To understand the significance of the canonical circuit, it is useful to
review the basic organization of the visual cortex, as this was the first area used to describe the
circuit. Very broadly, neurons in the cortex can be divided into excitatory and inhibitory classes,
with the former representing the majority of neurons in V1 (Binzegger et al., 2004). While the
majority of excitatory neurons are pyramidal and glutamatergic, inhibitory neurons have smooth
dendrites (do not have spines) and are GABAergic. Even though the much larger proportion of
excitatory to inhibitory cells holds across several species (cat, monkey, and rodents), in rodents,
pyramidal cells in layers 2 and 3 target more inhibitory neurons than we would expect by chance
(Bopp et al., 2014). Nevertheless, it is generally the case that most connections in V1 are from
layer 2/3 pyramidal cells onto other pyramidal cells in the same layers (Binzegger et al., 2009).
Layer 1 consists mainly of axons, dendrites, and glial processes, while layers 2, 3, 5, and 6 are
predominantly made up of pyramidal neurons. Layer 4, on the other hand, has spiny stellate
neurons in the largest proportion (DeFelipe et al., 1999). Nevertheless, this layer also contains
GABAergic neurons, of which 78% are basket cells (Binzegger et al., 2004). Layer 4 is unique in
that it is the primary input layer for thalamo-cortical synapses from the lateral geniculate nucleus
(LGN). Despite this, layer 4 neurons receive many more cortico-cortical synapses than thalamocortical synapses. Indeed, layer 6 pyramidal neurons and layer 4 spiny stellate neurons provide
6 approximately 8 and 5 times as many synapses onto layer 4 neurons as LGN neurons do (Ahmed
et al., 1994). Despite its proportionately small input, it is thought that LGN input to V1 accounts
for most of the orientation tuning of V1 cells (Lamme et al., 1998). Indeed, the model of the
canonical circuit suggests that thalamocortical inputs into layer 4 neurons drive these cells,
whose responses then get amplified by cortico-cortical excitation. GABAergic inhibition from
cortical cells onto excitatory cells in all layers of the cortex is provided to prevent overexcitation
(Douglas et al., 1989). Even though they were first described in the visual cortex, canonical
circuits perform similar functional computations throughout the cortex, as the specific wiring of
the canonical circuit is flexible and can be universally applied (Costa and Martin, 2010). Indeed,
differences in the laminar organization of different areas require a labile definition of a
functional unit, as Beul and Hilgetag (2015) argue in trying to build a canonical circuit that is
consistent with less organized regions than V1, such as the prefrontal cortex, which contains
agranular regions.
While the canonical circuit model emphasizes the importance of thalamo-cortical and
intra-areal connections, it is important to keep in mind that another set of important inputs to V1
is that from extrastriate areas: feedback projections. It has been shown that both feedforward and
feedback projections are mostly excitatory (Lamme et al., 1998). While thalamo-cortical
projections to V1 drive these neurons, feedback projections have been suggested to modulate V1
neurons’ responses (Angelucci et al., 2002; Budd, 1998). For example, Hupe et al. (1998) found
that inactivating area V5 by cooling leads to a decrease in the response of V1, V2, and V3
neurons. Their data also suggest that V5 is involved in enhancing responses to the figure relative
to the background in lower visual areas. V2, or area 18, is the largest extrastriate area and
provides more input to V1 than any other extrastriate area. Nevertheless, this feedback input
7 accounts for only approximately 0.9-6.1% of excitatory inputs onto V1 neurons in layers 2 and 3
(Budd, 1998).
Our study focused on the refinement and organization of the visual cortex of the ferret.
We calculated neuronal density, bouton density, interbouton intervals, and axon density of
feedforward terminal clusters at different developmental stages, and we characterized synapses
in layer 4 of area 17 in adults. We chose the ferret (Mustela putorius furo) for this study because
its brain is very underdeveloped at birth (Sur and Leamy, 2001). In fact, the emergence of layers
2/3 and 4, as well as connections from the LGN to the cortex occur postnatally in ferrets. The
ages at which we studied the refinement of the visual cortex in ferrets were chosen to span the
period around eye opening, which is approximately P30 to P32 (Durack and Katz, 1996). Thus,
we could observe if visual experience could be driving the development of connections among
visual areas, and whether different areas refine synchronously or asynchronously. We
hypothesized that different visual areas would develop with different timecourses, as this could
indicate the neural correlates of the different maturation rates of distinct visual functions.
Interestingly, it is during and shortly after the period of eye opening during which we found the
greatest refinement. However, contrary to our hypothesis, we found that many aspects of
refinement that we observed occurred at similar rates in different visual areas.
Methods
General methods
We studied the visual cortex of ferrets at different developmental stages. For neuronal
density, peak bouton density, and interbouton intervals, we used a sample size of two ferrets per
area at most ages. We used a sample size of one for peak bouton density and intervals at 5
8 weeks; so far, we have studied only one ferret at each age for the calculations of overall bouton
density and axon density. The ferrets were obtained from Marshal Farms (North Rose, NY) and
maintained under a 12 hour light/dark cycle.
Anatomical tracer injections
We injected the tracer cholera toxin B (CTb) into area 17 (following the protocol
described in Khalil and Levitt, 2014), and after a week of survival, the animals were euthanized
with an intraperitoneal administration of pentobarbital (60-100 mg/kg). They were then perfused
with saline and their brains were fixed with a 4% paraformaldehyde solution. The ferrets’ brains
were removed and frozen sections were tangentially cut into 40 µm thick sections using a sliding
microtome. The sections were mounted on slides, dehydrated, cleared in xylene, and
coverslipped. While alternate sections were reacted to reveal the CTb label, other sections were
processed for Nissl substance, synaptic zinc, or cytochrome oxidase (Cantone et al., 2005).
Bouton and axon density measurements
We used a light microscope and the program Neurolucida (MBF Bioscience: Williston
VT) to mark boutons and axons in the ferret extrastriate cortex and calculate peak bouton
density, overall bouton density, interbouton intervals, and axon density. We found peak and
overall bouton density, interbouton intervals, and axon density in areas 18, 19, 21, and Ssy at 4,
5, 6, 8, and 10 weeks of age (we performed measurements in a 7 week old case as well, but we
excluded the data due to sparse labeling in the animal). To calculate peak bouton density, we
counted the number of boutons in 5 μm by 5 μm boxes using a 100x oil immersion lens to view
the region of interest. We placed a total of 10 of these sample boxes in the core, mid-periphery,
and periphery of each CTb labeled cluster in each area at each age in each animal. We counted
9 the boutons along the depth of each section using a 1μm exclusion zone at the top and bottom of
the Z plane. In addition, we used the bottom and left edges of each sample box as exclusion
zones to prevent overcounting. We followed a similar procedure for estimating overall bouton
density, with the exception that we specifically looked for label when placing our sample boxes
in the peak bouton density measurements, but not when estimating overall bouton density.
Instead, for overall bouton density, we laid a grid of 5 μm by 5 μm boxes over our area of
interest, and we used random.org to generate a random number from 1-14, which indicated the
first box from which we took measurements (we numbered boxes from left to right).
Subsequently, we sampled from every 14th box and counted from as many boxes as needed in
order to obtain at least 100 boutons per region, per area, in each case. To calculate axon density,
we measured the length of all axon processes present in 10 μm by 10 μm boxes at 40x. As in the
peak bouton density counts, we placed 10 boxes in each region (core, mid-periphery, and
periphery), in each area, at each age. When calculating densities, we corrected for shrinkage, as
the tissue shrank due to processing by an average of 62%. We also used a 100x oil immersion
lens to view the area of interest when we calculated interbouton intervals in the core and
periphery of each CTb labeled cluster. We marked the boutons along individual axons, excluding
terminal boutons to prevent overcounting, and used Neurolucida to find the distance between
adjacent boutons in a given axon. Several axons were studied in each cluster, until we had
accumulated at least 100 interbouton intervals in the core and 100 in the periphery in each area,
at each age, in each case.
Neuron counting
10 We calculated neuronal density in areas 17, 18, 19, 21 and Ssy using a light microscope
at 100x oil immersion lens to view the tissue. We used Neurolucida to draw columns
perpendicular to the cortical surface. These columns spanned layers 2 to 6, since layer 1 was
excluded due to its low number of neurons. Each column contained a 45 µm by 45 µm square in
each of the five layers studied. Neurons were identified by their distinct nucleoli, and counted
along the X,Y, and Z planes of each box. In the Z plane, we used an exclusion zone of 2 µm at
the top and the bottom to prevent over counting. In the X and Y planes of each box, the bottom
and left lines were used as exclusion zones. The average tissue shrinkage in the Z plane was
around 50%. Once the number of neurons in a box was determined, we calculated the density by
dividing this number by the volume of the box in mm3 and correcting for shrinkage.
Electron microscopy
To have a richer understanding of the particular types of synapses present in the visual
cortex, we characterized cortico-cortical and thalamo-cortical synapses in layer 4 of primary
visual cortex. We used a total of 4 adult ferrets to categorize synapses at the EM level. The
brains of 2 adult ferrets were processed for EM following the protocol in Garcia-Marin et al.
(2009). The tissue from the other 2 brains was reacted with an antibody against the vesicular
glutamate transporter (vglut2), following the protocol in Garcia-Marin et al. (2012), in order to
allow us to distinguish cortico-cortical synapses from thalamo-cortical synapses, as the latter use
the vglut2 transporter. The processing of these 4 adult brains was done by our collaborator
Virginia Garcia-Marin at New York University. Using the program Fiji, we counted the number
of synapses present in layer 4 of area 17 in randomly chosen EM images from all 4 animals, and
characterized the synapses as asymmetric when there was a prominent PSD, as symmetric when
the PSD was very thin, or as uncharacterized when it was unclear whether the synapse was
11 symmetric or asymmetric, when there was no clear synaptic cleft, or when the synapse was en
face. We also measured the length of the PSD and identified the post-synaptic target as either a
spine or a dendritic shaft.
Results
Developmental changes in bouton density
We measured the peak bouton density in different visual areas at different ages to
understand how the strength of connections in each area changes with age, and to elucidate if
different areas might develop with similar or different timecourses. We obtained peak bouton
densities in different regions of terminal clusters, namely: the core, mid-periphery, and
periphery, as the boutons may not be distributed equally throughout the cluster. Figures 1a to 1c
display the average bouton density ± S.E.M. in these different regions of the cluster in areas 18,
19, 21, and Ssy at 4, 5, 6, 8, and 10 weeks of age. In general, the core of each cluster contains the
highest bouton density, particularly in the younger animals. In all parts of the cluster, there is
more variability in the densities among areas early in development, while the densities at 8 and
10 weeks, with the exception of area 19 at the latter age, are nearly indistinguishable. In general,
the bouton density in the different areas decreases with age, although the data also show
increases from 5 to 6 weeks of age. Comparing the densities at 4 weeks with those at 10 weeks
shows that there is a decline in most of the areas by at most 50%. We performed a permutation
test and found significant differences at the P<0.05 level in bouton density with age in all areas
except for area 19. Interestingly, all of the areas seem to follow the same trend, with peak
densities at 4 and 6 weeks of age, and lower densities at all other ages.
12 Bouton Density (boutons x10^6/mm^3)
Peak Bouton Density in the Core
25
20
15
18
19
10
21
Ssy
5
0
2
4
6
8
Age (weeks)
10
12
Figure 1a. Average peak bouton density in the core of the cluster in areas 18, 19, 21, and Ssy at different ages. Error
bars represent S.E.M.
Bouton Density (boutons x10^6/mm^3)
Peak Bouton Density in the Mid-Periphery
25
20
15
18
19
10
21
5
Ssy
0
2
4
6
8
Age (weeks)
10
12
Figure 1b. Average peak bouton density in the mid-periphery of the cluster in areas 18, 19, 21, and Ssy at different
ages. Error bars represent S.E.M.
13 Bouton Density (boutons x10^6/mm^3)
Peak Bouton Density in the Periphery
25
20
15
18
19
10
21
Ssy
5
0
2
4
6
8
Age (weeks)
10
12
Figure 1c. Average peak bouton density in the periphery of the cluster in areas 18, 19, 21, and Ssy at different ages.
Error bars represent S.E.M.
Figures 2a to 2d display the same peak bouton density data as that plotted above, but
these graphs allow us to more clearly observe the developmental changes occurring in each area.
Figure 2a shows that in area 18, the core contains the highest density at 4, 6, and 8 weeks of age,
while the densities are more similar in different parts of the cluster in the rest of the ages. In
contrast, in area 19, the core contains the highest density and the mid-periphery the second
highest at all ages. Area 21 shows a similar trend to that of area 19, with the exception that the 10
week olds have nearly indistinguishable densities in different regions of the cluster. As shown in
2d, the core and mid-periphery of the Ssy cluster contain very similar bouton densities from 4 to
6 weeks of age, while that of the periphery is substantially lower. Nevertheless, starting at 8
weeks of age, there is more overlap between the densities in all three regions of the cluster. All
the areas show a similar trend of changes in density with age, with a relatively low density at 5
weeks, and a decline after 6 weeks of age.
14 Bouton Density (boutonsx10^6/mm^3)
Peak Bouton Density in Area 18
25
20
15
core
10
mid-periphery
periphery
5
0
2
4
6
8
Age (weeks)
10
12
Figure 2a. Average peak bouton density in the core, mid-periphery, and periphery of the cluster in area 18 as a
function of age. Error bars represent S.E.M.
Bouton Density (boutonsx10^6/mm^3)
Peak Bouton Density in Area 19
25
20
15
core
10
mid-periphery
periphery
5
0
2
4
6
8
Age (weeks)
10
12
Figure 2b. Average peak bouton density in the core, mid-periphery, and periphery of the cluster in area 19 as a
function of age. Error bars represent S.E.M.
15 Bouton Density (bouonx10^6/mm^3)
Peak Bouton Density in Area 21
25
20
15
core
10
mid-periphery
periphery
5
0
0
2
4
6
8
Age (weeks)
10
12
Figure 2c. Average peak bouton density in the core, mid-periphery, and periphery of the cluster in area 21 as a
function of age. Error bars represent S.E.M.
Bouton Density (boutonsx10^6/mm^3)
Peak Bouton Density in Ssy
25
20
15
core
10
mid-periphery
periphery
5
0
2
4
6
8
Age (weeks)
10
12
Figure 2d. Average peak bouton density in the core, mid-periphery, and periphery of the cluster in Ssy as a function
of age. Error bars represent S.E.M.
Given that we obtained peak bouton density by sampling specifically from locations in
the cluster that contained labeled boutons, we wondered if perhaps our methodology could have
16 masked certain developmental changes in bouton density occurring with age. Thus, we decided
to take a completely random measurement of bouton density from the same three regions of each
cluster of areas 18, 19, 21, and Ssy at 4, 5, 6, 8, and 10 weeks of age. Figures 3a to 3c show these
measurements by region of the cluster as a function of age. While the values for overall bouton
density are lower than those of peak bouton density, the trends are the same. A permutation test
revealed changes in average bouton density as a function of age to be highly significant in all
areas (P≤0.0006). Early in development, there is more variability among densities in different
areas in all parts of the cluster. However, by 10 weeks of age, with the exception of the density in
the mid-periphery of Ssy, the densities of all areas are essentially the same within a given region
of the cluster. While the core and mid-periphery have very similar densities in all areas at all
ages, the densities in the periphery are somewhat lower. However, this lower density in the
periphery is present at 4 and 6 weeks of age, but not in the older animals.
Bouton Density (10^6/mm^3)
Overall Bouton Density in the Core
16
12
18
8
19
21
4
Ssy
0
4
6
8
10
Age (weeks)
Figure 3a. Overall bouton density in the core of the cluster in areas 18, 19, 21, and Ssy at different ages. Error bars
represent S.E.M.
17 Bouton Density (10^6/mm^3)
Overall Bouton Density in the Mid-periphery
16
12
18
8
19
21
4
Ssy
0
4
6
8
10
Age (weeks)
Figure 3b. Overall bouton density in the mid-periphery of the cluster in areas 18, 19, 21, and Ssy at different ages.
Error bars represent S.E.M.
Overall Bouton Density in the Periphery
Bouton Density (10^6/mm^3)
16
12
18
8
19
21
4
Ssy
0
4
6
8
10
Age (weeks)
Figure 3c. Overall bouton density in the periphery of the cluster in areas 18, 19, 21, and Ssy at different ages. Error
bars represent S.E.M.
Figures 4a through 4d display the same overall bouton density described above, but listed
by area. All of the areas show similar trends in that the major bouton density declines occurs
18 after 6 weeks of age. In areas 18 and 21, however, there is a substantial decline in overall bouton
density from 4 to 6 weeks of age in the core and mid-periphery of the cluster, but not in the
periphery. In general, most regions of the cluster show a higher density at 10 weeks compared to
8 weeks in all of the areas. The only steady decline of bouton density from 4 to 10 weeks occurs
in the periphery of Ssy, as all other regions in all areas show either an increase or little change
from 8 to 10 weeks. Despite the small variation among regions of different areas at different
ages, all of the areas show a bouton density decline of approximately 50% at 10 weeks compared
to 4 weeks, and this is true in the core, mid-periphery, and periphery of each cluster.
Bouton Density (10^6/mm^3)
Overall Bouton Density in Area 18
16
12
core
8
mid-periphery
4
periphery
0
2
4
6
8
Age (weeks)
10
12
Figure 4a. Overall bouton density in the core, mid-periphery, and periphery of the cluster in area 18 as a function of
age. Error bars represent S.E.M.
19 Bouton Density (10^6/mm^3)
Overall Bouton Density in Area 19
16
12
core
8
mid-periphery
4
periphery
0
2
4
6
8
Age (weeks)
10
12
Figure 4b. Overall bouton density in the core, mid-periphery, and periphery of the cluster in area 19 as a function of
age. Error bars represent S.E.M.
Bouton Density (10^6/mm^3)
Overall Bouton Density in Area 21
16
12
core
8
mid-periphery
4
periphery
0
2
4
6
8
Age (weeks)
10
12
Figure 4c. Overall bouton density in the core, mid-periphery, and periphery of the cluster in area 21 as a function of
age. Error bars represent S.E.M.
20 Bouton Density (10^6/mm^3)
Overall Bouton Density in Ssy
16
12
core
8
mid-periphery
4
periphery
0
2
4
6
8
Age (weeks)
10
12
Figure 4d. Overall bouton density in the core, mid-periphery, and periphery of the cluster in Ssy as a function of
age. Error bars represent S.E.M.
Developmental changes in interbouton intervals
After observing developmental changes in bouton density, we decided to measure
changes in interbouton intervals to find out if elimination of individual boutons along axons
could account for the decline in bouton density with age. Figures 5a and 5b show the median
interbouton intervals ± S.E.M. in the core and periphery of the clusters in areas 18, 19, 21, and
Ssy in 4, 5, 6, 8, and 10 week olds. In both the core and the periphery, from 4 to 6 weeks, all of
the interbouton intervals range from 0.785 to 1.14 µm. However, from 8 to 10 weeks, the median
interbouton intervals are considerably larger, ranging from 1.1 to 1.53 µm. The major increase in
interbouton intervals occurs from 6 to 8 weeks, with little change thereafter. We performed a
Kruskal-Wallis test and found significant differences in interbouton intervals as a function of age
in all areas, at the P=0.01 level.
21 Interbouton Intervals in the Core
Interbouton Intervals (µm)
2
1.5
18
1
19
21
0.5
Ssy
0
2
4
6
8
Age (weeks)
10
12
Figure 5a. Median interbouton intervals in the core of the clusters in areas 18, 19, 21, and Ssy as a function of age.
Error bars represent S.E.M.
Interbouton Intervals in the Periphery
Interbouton Intervals (µm)
2
1.5
18
1
19
21
0.5
Ssy
0
2
4
6
8
Age (weeks)
10
12
Figure 5b. Median interbouton intervals in the periphery of the clusters in areas 18, 19, 21, and Ssy as a function of
age. Error bars represent S.E.M.
Developmental changes in axon density
22 While the elimination of individual boutons could explain the developmental decline in
bouton density, it is also possible that the elimination of axonal branches plays a role. Thus, we
calculated axon densities at different ages to reveal the contribution that this pruning has on
bouton density changes. Figures 6a through 6c display the axon density in the core, midperiphery, and periphery of each cluster in areas 18, 19, 21, and Ssy at 4, 5, 6, 8 and 10 weeks of
age. The axon density in the core and mid-periphery are very similar, but that of the periphery is
lower, particularly at the earlier ages. There is a lot of variability in the changes that occur with
age among areas. For example, in the core of the cluster, the axon density of area 18 increases
from 4 to 5 weeks, decreases from 5 to 8, but then increases slightly from 8 to 10. In contrast, the
core density in area 19 decreases steadily from 4 to 8 weeks, but then increase from 8 to 10. The
density in the core of both 21 and Ssy increases from 4 to 5 weeks, but shows a steady decline
afterwards. Even within an area, there is variability in the trends of axon density with age in
different parts of the cluster. As mentioned earlier, the density in the core of the Ssy cluster
increases from 4 to 5 weeks and decreases thereafter. However, in the mid-periphery, the axon
density in Ssy shows a steady decline from 4 to 8 weeks, but an increase from 8 to 10. In the
periphery, however, Ssy displays very little change in axon density from 4 to 8 weeks, but a large
decline from 8 to 10. Indeed, using a permutation test, we found that the only areas showing a
significant change with age in all regions of the cluster were areas 21 and Ssy. It is difficult to
make generalizations about the trends occurring in axon density with age, due to the high
variability among and within areas.
23 Axon Density in the Core
Axon Density (µm x10^6/mm^3)
14
12
10
8
18
6
19
21
4
Ssy
2
0
2
4
6
8
Age (weeks)
10
12
Figure 6a. Average axon density in the core of the clusters in areas 18, 19, 21, and Ssy as a function of age. Error
bars represent S.E.M.
Axon Density (µm x10^6/mm^3)
Axon Density in the Mid-Periphery
14
12
10
8
18
6
19
4
21
Ssy
2
0
2
4
6
8
Age (weeks)
10
12
Figure 6b. Average axon density in the mid-periphery of the clusters in areas 18, 19, 21, and Ssy as a function of
age. Error bars represent S.E.M.
24 Axon Density in the Periphery
Axon Density(µm x10^6/mm^3)
14
12
10
8
18
19
6
21
4
Ssy
2
0
2
4
6
8
10
12
Age (weeks)
Figure 6c. Average axon density in the periphery of the clusters in areas 18, 19, 21, and Ssy as a function of age.
Error bars represent S.E.M.
Developmental changes in neuronal density
To correlate changes in bouton density with age to changes in neuronal density, we
obtained this latter measurement in layers 2 to 6 in areas 17, 18, 19, 21, and Ssy at 4 and 6 weeks
of age, as well as in adults. Figures 7a to 7e show the average neuronal density ± S.E.M. at 4
weeks, 6 weeks, and adulthood in each layer. Neuronal density decreased between the ages of 4
and 6 weeks in all layers in areas 17 and 18, though layer 5 of area 18 shows a more modest
decline. From 6 weeks to adulthood, however, neuronal density did not change much in the
majority of layers in these two areas. In Ssy, only layers 2 and 3 show a big decline in neuronal
density in 6 week olds compared to 4 week olds. However, the density in all the layers in Ssy
decreased by a much larger magnitude from 4 weeks of age to adulthood. Much of this decrease
is accounted for by a decline in density between 6 weeks of age and adulthood. Surprisingly,
neuronal density in all the layers of areas 19 and 21 remained essentially the same throughout
25 development. Despite the different magnitudes of changes in neuronal density occurring in each
area and layer, a permutation test revealed that the only significant changes with age at the
P<0.05 level occurred in layers 3-6 of area 17, layer 4 of area 18, and all layers in Ssy with the
exception of layer 3.
Neuroal Density (neurons x10^4/
mm^3)
Neuronal Density in Layer 2
35
30
25
17
20
18
15
19
10
21
5
Ssy
0
4 weeks
6 weeks
Age
adults
Figure 7a. Average neuronal density in layer 2 of areas 17, 18, 19, 21 and Ssy among the different age groups. Error
bars represent S.E.M.
26 Neuroal Density (neurons x10^4/mm^3)
Neuronal Density in Layer 3
35
30
25
17
20
18
15
19
10
21
5
Ssy
0
4 weeks
6 weeks
Age
adults
Figure 7b. Average neuronal density in layer 3 of areas 17, 18, 19, 21 and Ssy among the different age groups.
Error bars represent S.E.M.
Neuroal Density (neurons x10^4/mm^3)
Neuronal Density in Layer 4
35
30
25
17
20
18
15
19
10
21
5
Ssy
0
4 weeks
6 weeks
Age
adults
Figure 7c. Average neuronal density in layer 4 of areas 17, 18, 19, 21 and Ssy among the different age groups. Error
bars represent S.E.M.
27 Neuroal Density (neurons x10^4/mm^3)
Neuronal Density in Layer 5
30
25
20
17
18
15
19
10
21
5
Ssy
0
4 weeks
6 weeks
Age
adults
Figure 7d. Average neuronal density in layer 5 of areas 17, 18, 19, 21 and Ssy among the different age groups.
Error bars represent S.E.M.
Neuroal Density (neurons x10^4/mm^3)
Neuronal Density in Layer 6
35
30
25
17
20
18
15
19
10
21
5
Ssy
0
4 weeks
6 weeks
Age
adults
Figure 7e. Average neuronal density in layer 6 of areas 17, 18, 19, 21 and Ssy among the different age groups. Error
bars represent S.E.M.
28 Figure 8a shows the average neuronal density ± S.E.M. in all the layers in area17 at
different ages. Neuronal density decreased from 4 to 6 weeks in all layers, but did not change
much from 6 weeks to adulthood. In the 4 week olds, layer 2 had the highest neuronal density
(31.6 x 105 neurons/mm3) followed closely by layer 6 (30.4 x 104 neurons/mm3). At 21.6 x 104
neurons/mm3, the density of layer 5 was much lower than that of all the other layers at 4 weeks.
At 6 weeks, there were no large differences between the densities of layers 2, 3, 4, or 5.
However, layer 6 had a larger density than all the other layers. In adults, as in the 4 week olds,
layer 2 contained the highest neuronal density (21.6 x 104 neurons/mm3), and layer 6 contained
the second highest density (18.6 x 104 neurons/mm3).
Neuronal Density (neurons x10^4/mm^3)
Neuronal Density in Area 17
35
30
25
20
4 weeks
15
6 weeks
10
adult
5
0
2
3
4
Cortical Layer
5
6
Figure 8a. Average neuronal density in layers 2 to 6 in area 17 in the different age groups. Error bars represent
S.E.M.
Figure 8b shows the developmental changes in neuronal density in the 5 layers in area 18.
Similar to the changes in area 17, a decrease in neuronal density from 4 to 6 week olds was
observed in the layers of area 18. At 26.9 x 104 neurons/mm3, layer 2 contained the highest
29 density in the 4 week olds. From 6 weeks to adulthood, there was actually a large increase in
neuronal density in layers 2 and 3. In adults, as in the 4 week olds, layer 2 contained the highest
neuronal density (19.4 x 104 neurons/mm3). Layer 5 was the least dense layer at all ages: 12.5,
9.1, and 9.7 x 104 neurons/mm3 in the 4 week olds, 6 week olds, and adults, respectively.
Neuronal Density (neurons x10^4/mm^3)
Neuronal Density in Area 18
35
30
25
20
4 weeks
15
6 weeks
10
adults
5
0
2
3
4
Cortical Layer
5
6
Figure 8b. Average neuronal density in layers 2 to 6 in area 18 in the different age groups. Error bars represent the
S.E.M.
Figure 8c shows the average neuronal densities in layers 2 to 6 of area 19. All of the
layers show very little change in density from 4 to 6 weeks, and from 6 weeks to adulthood.
However, we do observe different densities among the layers within a given age. At all ages,
layer 2 contains the highest density and layer 5 the lowest. At 12.5 and 15.6 x 104 neurons/mm3,
layer 6 has the second highest density in the 4 and 6 week olds, respectively. However, in the
30 adults, layer 4 has the second highest density, though only slightly higher than that of layers 3
and 6.
Neuronal Density (neurons x10^4/mm^3)
Neuronal Density in Area 19
35
30
25
20
4 weeks
15
6 weeks
10
Adult
5
0
2
3
4
Cortical Layer
5
6
Figure 8c. Average neuronal density in layers 2 to 6 in area 19 in the different age groups. Error bars represent the
S.E.M.
Figure 8d shows that like area 19, area 21 does not show many changes in neuronal
density with age. Layers 2 and 5 contain the highest and lowest densities at each age,
respectively. Layers 3 and 4 contain very similar densities in all the age groups, ranging from
11.5 to 14.1 x 104 neurons/mm3. In layer 6, there is an increase in density from 4 to 6 weeks of
age, from 10.3 to 16.0 x 104 neurons/mm3. However, in the adults, the density is between that of
the 4 and 6 week olds, at 12.1 x 104 neurons/mm3.
31 Neuronal Density (neurons x10^4/mm^3)
Neuronal Density in Area 21
35
30
25
20
4 weeks
15
6 weeks
10
Adult
5
0
2
3
4
Cortical Layer
5
6
Figure 8d. Average neuronal density in layers 2 to 6 in area 21 in the different age groups. Error bars represent the
S.E.M.
Figure 8e shows that in Suprasylvian, the major declines in density were observed when
4 week olds are compared to adults. This is in part due to the decline in neuronal density from 6
weeks of age to adulthood, during which layers 2, 4, and 5 show the biggest decline. At 4 weeks,
the density in layer 2 was substantially higher than that in layers 3-6. In contrast, the density in
layer 5 was the lowest at 15.1 x 104 neurons/mm3. The density in Layer 2 remained the highest in
6 week olds, while the density in layer 5 was only slightly lower than that of layers 2, 3, and 6 at
this same age. In adults, layer 2 was still the highest, but the difference between this density and
that of other layers was substantial only for layers 4 and 5. Layer 5, however, contained a much
lower density than that of any other layer in adulthood.
32 Neuronal Density (neurons x10^4/mm^3)
Neuronal Density in Ssy
35
30
25
20
4 weeks
15
6 weeks
10
adult
5
0
2
3
4
Cortical Layer
5
6
Figure 8e. Average neuronal density in layers 2 to 6 in Ssy in the different age groups. Error bars represent the
S.E.M.
Thalamo-cortical and cortico-cortical synapses
We measured several characteristics of synapses in layer 4 of area 17, and we were able
to use vglut2 to differentiate thalamo-cortical from cortico-cortical synapses. In vglut2+ profiles,
we found that 13% of synapses were vglut2+. With the exception of one UC vglut2+ synapse, all
of these thalamo-cortical synapses were asymmetric. Despite the different proportions and
characterizations of thalamo-cortical versus cortico-cortical synapses, we did not find any
differences in PSD across any of the comparisons we performed. Figure 9a shows that the PSD
lengths of area 17 layer 4 synapses did not differ between vglut2+ and vglut2- profiles.
Furthermore, there is no significant difference between the PSD lengths of asymmetric,
symmetric, or UC synapses. We further compared the PSD lengths of all vglut2+ to that of all
vglut2- synapses, as well as the PSD lengths of asymmetric vglut2+ to that of asymmetric
vglut2- synapses, and as figure 9b shows, there were no differences. In all of our PSD length
33 comparisons we performed non-parametric Kruskal-Wallis tests (for 3-way comparisons) or
Mann-Whitney tests (for pairwise comparisons) and found no significant differences.
PSD lengths
0.4
Length (µm)
0.3
asym
0.2
sym
UC
0.1
0
vglut2-
vglut2+
Figure 9a. Average PSD lengths of asymmetric, symmetric, and uncharacterized synapses in layer 4 of area 17 in
vglut2+ and vglut2- profiles. Error bars represent the S.E.M. asym=asymmetric, sym=symmetric,
UC=uncharacterized.
PSD lengths
Length (µm)
0.40
0.30
0.20
vglut2+
vglut2-
0.10
0.00
all synapses
asym synapses
Figure 9b. Average PSD lengths of vglut2+ and vglut2- synapses in layer 4 of area 17. Error bars represent the
S.E.M.
34 In addition to PSD lengths, we also calculated synaptic density in layer 4 of V1 to
compare the strengths of synaptic input from different types of synapses. Figure 10 shows that
the total synaptic density was 6.37 x 108 synapses per mm3. Of this density, approximately two
thirds is due to asymmetric synapses. On the contrary, the density of UC synapses is less than
one third of the total density, while the density of symmetric synapses is even smaller.
Density (synapses x 10^8/mm^3 )
Synaptic Density in Layer 4 of V1
8
6
4
2
0
total
asym
UC
sym
Figure 10. Average synaptic density in layer 4 of V1. Error bars represent the S.E.M. Asym=asymmetric,
sym=symmetric, and UC=uncharacterized.
One last measure we calculated was the identity of the post-synaptic target in layer 4 of
V1. We found that, on average, spines made up 69% of the targets, while dendrites made up
31%.
Discussion
Decline in feedforward projections
One of the main findings of our study was the developmental decline of feedforward
projections to extrastriate areas. We found that bouton density, a proxy for synapses, declines in
the core, mid-periphery, and periphery of areas 18, 19, 21, and Ssy from 4 to 10 weeks of age.
35 As expected, when we calculated overall bouton density, we found lower densities than when we
calculated peak bouton density. However, the developmental changes in bouton density, namely,
a decline after 6 weeks of age, are similar in both types of measurements. Thus, our initial
methodology for counting boutons does not appear to have masked any changes occurring with
age. The observed declines in bouton density are in agreement with the phenomenon of
exuberance, which posits that more axons and connections are produced than will persist in
adulthood (Innocenti and Price, 2005). However, unlike the synaptic zinc decline that occurs
right after eye opening (from 5 to 6 weeks), the major decline in bouton density in all areas
occurs from 6 to 8 weeks of age. Nevertheless, just as Khalil and Levitt (2013) found that all
areas reach their lowest intensity of synaptic zinc by the same age but decline by different
amounts, we find that bouton density in all areas follows the same trends with age, implying a
similar developmental timecourse for these different areas. At this moment, we cannot explain
why is a decline in peak bouton density from 4 to 5 weeks of age, but an increase from 5 to 6.
One reason that could explain these findings is the sample size, as we only had one 5 week old
sample, while we had two samples at each of the other ages. In addition, the 5 week old case was
one of the last cases from which we sampled. Thus, it is possible that our criteria for marking
boutons changed. It is also probable that the particular 5 week old animal we used had a lower
density than the average 5 week old and was not representative of the population.
We calculated interbouton intervals to elucidate the mechanism of changes in bouton
density, as an increase in interbouton intervals would imply that individual boutons are being
eliminated. In addition, the interbouton interval data suggest a high degree of homogeneity in
extrastriate areas and provide some insight on the arrangement of axons from the core to the
periphery of each cluster. Though bouton density might vary initially in different areas, the
36 interbouton intervals in all areas are very similar from 4 to 6 weeks of age, during which they are
approximately 1µm. After 6 weeks, the intervals increase by approximately 50%. Although the
bouton density data are quite noisy, we do observe a decline by half in some areas from 4 to 10
weeks of age. However, since the interbouton intervals are not increasing by a factor of 2, we
know that the increase in intervals only partially accounts for the decline in bouton density.
Furthermore, we observed that bouton density is often, but not always, higher in the core than in
the periphery, but the interbouton intervals are nearly identical. This finding suggests that there
are more axons in the core than in the periphery, but the axons do not differ.
In addition to the elimination of individual boutons, it is also possible that axonal
branches are being pruned with age; to find out if this is the case, we measured axon densities.
Unlike changes in bouton density and interbouton intervals, the trends we observe in axon
density with age are much more variable between areas, and even between different parts of the
cluster within a given area. All of the areas show a decline from 4 to 8 weeks in at least one part
of the cluster. However, at 10 weeks, the axon density is higher than at 8 weeks in several areas
in different parts of the cluster. This increased axon density at 10 weeks could be due to the case
having a larger than average CTb injection core. This could have led to the labeling of more
axons, and the resulting high density. A large variability we observe in axon density could also
result from our using only one sample for this measure at each age. Furthermore, while we
calculated bouton density and interbouton intervals by observing the brain section under 100x
magnification, the axon density counts were done at 40x. As a result, the axon density counts are
less accurate than the other two measures. Despite the noisiness of the data, we do see some
modest declines in density with age, particularly in areas 21 and Ssy, which implies that axonal
37 branches are being eliminated. Thus, the decline in bouton density seems to be due to the
elimination of individual boutons as well as pruning of axons.
Developmental changes in neuronal density
To further understand the anatomical changes occurring in the developing visual cortex,
we decided to also measure neuronal density and observe if these changes mirror changes in
bouton density. Indeed, we found that areas 17 and 18 show a decrease of about 50%, similar to
the changes observed in bouton density. However, while the major decline in bouton density
occurs after 6 weeks, neuronal density in areas 17 and 18 decreases between 4 and 6 weeks of
age. Furthermore, while we find similar changes in bouton density in all areas, this is not the case
when we consider neuronal density. Suprasylvian develops at a slower rate than areas 17 and 18,
as the greatest decline in neuronal density in Ssy occurs between 6 weeks of age and adulthood.
Interestingly, areas 19 and 21 do not show any major changes in density during development.
We presume that most of the decline in neuronal density is likely due to the expansion of the
brain with age, which might explain why areas 19 and 21 do not show a decline in neuronal
density with age. Since these two areas are situated near the lateral sulcus, the brain might
expand less in this area, resulting in fewer developmental changes in the density of these areas.
Interestingly, if we focus on neuronal density in layer 4 of extrastriate areas, where we made the
quantifications of feedforward bouton densities, we observe that the decline in neuronal density
in this layer after 6 weeks does not fully account for the decline in bouton density. Thus, the
decline in bouton density does not simply reflect the expansion of the brain that could be spacing
the target neurons of feedforward projections farther apart. Instead, as stated earlier, changes in
bouton density seem to reflect elimination of boutons and pruning of axons.
38 Thalamo-cortical versus cortico-cortical synapses
While we can gain insight into the relative strengths of feedforward projections to
different extrastriate cortex at different ages by calculating bouton densities, we need to study
synapse at the EM level to understand the distribution and characteristics of different types of
synapses. We began by investigating thalamo-cortical and cortico-cortical inputs in layer 4 of
primary visual cortex in the adult. We found that only a small proportion of synapses in this
layer, namely, 13%, comes from the LGN. This is consistent with what other researchers have
found, as they identify that the majority of synapses in layer 4 of area 17 are cortico-cortical in
nature (Ahmed et al., 1994). In addition, all vglut2+ synapses were characterized as asymmetric,
except for one synapse which was en face, and thus, was listed as UC. Again, this is consistent
with previous findings, as Nahmani and Erisir (2005) identified that vglut2 was localized in
asymmetric, and thus, excitatory, synapses. However, while these researchers found that vglut2+
synapses have a larger PSD, we did not find any differences in PSD length when we compared
vglut2+ to vglut2- synapses. Nor did we find any significant differences in the PSD length of
asymmetric, symmetric, or UC synapses. A possible explanation for our lack of differences in
PSD lengths could be the angle at which the synapses were cut. Since we are working with twodimensional images, we are not always measuring the largest extent of the PSD in the synapse,
but rather, the extent that is visible in the particular image we obtain. Thus, measurements using
three-dimensional reconstruction would be useful to validate or reject our findings. Despite our
null findings of PSD length differences, we did find that each type of synapse makes differential
contributions to the synaptic density in layer 4. In particular, the density of asymmetric synapses
was substantially larger than that of symmetric or UC synapses. Furthermore, our data indicate
39 that synapses in layer 4 target spines more than dendrites, which is also commensurate with the
results of Nahmani and Erisir (2005).
Conclusions
We are interested in understanding the development of the visual cortex due to the
refinement it undergoes post-natally and its susceptibility to be influenced by visual experience.
We have begun to elucidate some of the mechanisms that are at work in the developing visual
cortex of the ferret. We found that the there is a decrease in the density of boutons that area 17
projects to areas 18, 19, 21, and Ssy between 4 and 10 weeks of age, and the changes are similar
in all areas, suggesting a similar timecourse of refinement. This finding is the opposite of what
we hypothesized, as we expected different areas to mature with different timecourses, likely
elucidating a mechanism for the development of different visual functions. At these same ages,
interbouton intervals increase with age, partly accounting for the decline in density, though
elimination of axonal branches also seems to play a role. Our data suggest that the major changes
in connectivity of feedforward projections occur after 6 weeks of age. In contrast, changes in
neuronal density vary more by area, with areas 17 and 18 showing a decline before Ssy. On the
other hand, areas 19 and 21 do not show any changes in neuronal density with age. The changes
that we do observe in both bouton and neuronal density, however, occur during or shortly after
eye opening in the ferret. This could indicate that visual experience plays a role in driving the
refinement of these developmental changes. We are now beginning to quantify how
characteristics and distributions of particular types of synapses in the visual cortex change with
age. Our next step will be to use EM techniques and construct three-dimensional reconstructions
40 of synapses in different visual areas at different ages to have a much richer understanding of the
refinement that the visual cortex undergoes.
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