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OCR for page 24
Areas and Modules in Visual Cortex
JON H. KAAS
One of the disadvantages of speaking on the topic of visual cortex
organization is that there are already a lot of tracks in the snow. But there
is also the advantage that tracks have already been laid, and I can travel
along some of these trails. I would like to start by noting some of the
difficulties in determining brain organization.
PROBLEMS IN SUBDIVIDING CORTEX
In talking about the organization of the brain, there is an initial
problem: What kind of concepts do we use to describe the structure of the
brain? We commonly use the terms, area and nucleus. We have some idea
of what we mean by nucleus, although the term has been used somewhat
inconsistently for subdivisions of the brain. David Van Essen has already
talked about the difficulty of experimentally defining an area; the same
criteria do not apply to what constitutes an area as one moves from lower
to higher areas in a processing hierarchy. We also tale about smaller
subdivisions, bands, clusters, modules, columns, and so on. Delimiting areas
and nuclei can be difficult, and defining subdivisions of the brain becomes
even more difficult with these smaller units.
A second problem in dealing with studies of the mammalian brain is
that there are a lot of species, and, hence, a lot of different brains. Figure 1
illustrates the pathways by which mammals arose very early from therapsid
reptiles around 200 million years ago. Clearly there are a number of
major separate lines of mammalian evolution, and they have been separate
for a long time. Brains that we now can study evolved from the rather
simple brains of early mammals. Figure 2 provides an example of one of
the changes that has occurred in mammalian brains. Radinski (1975) has
24
OCR for page 25
AREAS AND MODUI FS IN VISUAL CORTEX
25
illustrated how much mammal brains can differ in size, even if the animals
are roughly the same size. He considered the brains of a hedgehog, a
galago, and a squirrel monkey. It is clear that neocortex has expanded in
primates, with major differences in the neocortex of prosimian primates and
New World monkeys. There must be major differences in brain structure
as well. So there is also the difficulty of comparing brains when major
differences undoubtedly exist.
A third problem is that current theories are often constrained by a his-
tory of conclusions based on rather ambiguous evidence. Early investigators
of brain organization were very limited by the available methods. Early ar-
chitectonists (e.g., Brodmann, 1909) considered brain structure and divided
the brain into histologically distinct subdivisions that were presumed to be
functional subdivisions. However, it is now clear that many errors occurred.
Figure 3 demonstrates some of the difficulties of interpreting differences
in histological structure. In the caudal part of a tree shrew brain, primary
visual cortex (area 17) can be clearly identified, and binocular and monocu-
lar parts can be distinguished by thickness. If we consider a similar section
through the brain of a hedgehog, area 17 is also apparent, with structurally
distinct binocular and monocular parts. However, what is most obvious is
that the appearance of area 17 differs in hedgehogs and tree shrews. One
of the real achievements of Brodmann was that he recognized area 17 as
homologous, that is, the same field across these and other mammals. We
now know these fields are the same from many types of evidence, although
some people deny that area 17 of hedgehogs is area 17 even to this day.
If there is disagreement over the identity of the primary field which is
the most easily identified field—you can imagine the problem of identifying
homologies for other fields across species. Investigators relying only on
architectonic evidence have had quite different opinions on how brains are
divided. This variability is due partly to the difficulty of making distinctions,
and partly because it is very hard to deduce homologies from histological
appearance.
The solution to the problem of identifying cortical areas is not simply
to say that species differences relate to the expansion of a lot of cortex
we know nothing about—what we will call association cortex. We would
then need only to identify a few primary areas across different species.
The methods are available to do much better than that. Figure 4 shows
an example of how the application of a new procedure can reveal the
organization of part of the brain with such certainty that there can be
no further doubt. A brain section through somatosensory cortex in a rat,
when reacted for cytochrome oxidase (CO), reveals a map of the rat body
surface, with the hindfoot, the trunk, the forefoot, and the head, including
the mouth parts and the lower lip, all represented by CO dense regions.
The use of such new stains and reactions, which reveal aspects of cortical
OCR for page 26
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OCR for page 27
AReAS AND MODULES IN VISUAL CORTEX
~~-
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FIGURE 2 An example of how brains vary in size relative to body weight. E = brain
weight; P = body weight. Source: Radinksy (1975).
organization with clarity, will allow the development of new hypotheses
about how cortex is organized in different mammals. Using many methods,
often in conjunction, much progress has been made. One conclusion of
Brodmann that seems to be absolutely supportable is that animals with very
small brains, like hedgehogs, have few cortical areas, and animals with big
brains have more areas. Some of these areas can be identified as the same
across different mammals, but others must have evolved independently.
With regard to smaller subdivisions of the brain, a popular idea intro-
duced by Mountcastle (1957) was that of '~columns" within somatosenso~y
cortex. Expanding this concept from the original description of two classes
OCR for page 28
28
JON H. ROTAS
Rae
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FIGURE 3 Frontal brain sections through caudal visual cortex of a hedgehog (A) and a
tree shrew (B). Nissl stain. Although areas 17 (V-I) and (18) (V-II) have been identified lay
multiple cntena, they are quite different in histological appearance (modified Tom Kaas,
1987a).
Of columns (one related to receptors in deep tissues and one related to
cutaneous receptors) allows the sort of scheme to emerge that has been
illustrated by Wally Welker (1973: Figure 103. Within a single cortical area,
all submodalities related to each body part (hair, touch, pressure, joint,
temperature) would have their own cortical columns of neurons extending
from surface to white matter in S-I. It turns out that there is no evidence
that any cortical area is organized in this complex manner. However, the
segregation of neurons into groups of similar response properties and ac-
tivity patterns seems to be a general feature of cortex. What is needed now
OCR for page 29
AREAS AND MODULES IN VISUAL CORTEX
29
B ,
/
trunk
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04
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FIGURE 4 The cytochrome oxidase reaction pattern of primary somatosensory ortex in a
rat showing the details of the body surface representation. Source: Li et al., (1990), based
on illustrations of Dawson and Killackey (1987~.
OCR for page 30
30
JON H. BAAS
is to expand the concept of the cortical column to include a lot of different
kinds of configurations of segregations of neurons. One example is in the
primary somatosensory cortex of monkeys, area 3b. Mriganka Sur and
coworkers (1984) have shown that the two classes of peripheral afferents
(slowly adapting and rapidly adapting cutaneous adherents) seem to activate
quite different band-shaped configurations of neurons. Similarly, a number
of people working in the auditory system have described neurons in pri-
mary auditory cortex that are either excited by inputs from the two ears, or
excited by one ear and inhibited by the other (Middlebrooks et al., 1980~.
The two types of bands alternate and cross A-I counter to the lines of the
isofrequency representations. Of course, there are the well-known ocular
dominance and orientation specific columns and cytochrome omdase blobs
in area 17 and the three types of bands in area 18 of primates (see Living-
stone and Hubel, 1988, for a review). These are not traditional columns
because they have different shapes, or they fail to include all cortical layers,
so the term module is more applicable.
ASSIGNING FUNCTIONS TO MODULES
Given that the concepts of areas and modules are useful, I would like
to talk a little bit about the functional significance of the subdivisions of
areas that are called modules. Brodmann (1909) called areas "organs of
the brain" and argued that areas have different functions. In principle,
areas can be related to functions. But when we are talking about modules,
we need to use a little caution. Modules may be collections of small groups
of neurons that interact in ways that are aided by proximity and form
small processing units. However, segregations of neurons may occur for
other than functional reasons. Selection in evolution may produce modular
segregations as a by-product of selection for other factors (Gould and
Lewontin, 1979~.
Although this is a little confusing, Figure 5 provides an example of one
kind of segregation in the nervous system, the ocular dominance columns
or bands that subdivide layer 4 of 17 of macaque monkey. Florence et
al. (1986) have summarized the distribution of ocular dominance bands in
different primates. The important finding is that many species of mon-
keys do not have ocular dominance bands in cortex, some primates have
weakly segregated bands, and some have highly segregated bands as in
Figure 5. However, most animals do not have ocular dominance bands,
and these mammals function quite well without them. Ocular dominance
columns obviously evolved independently a number of times, which raises
several questions. Are ocular dominance columns functionally significant,
in that some visual functions are improved by the monocular activation of
OCR for page 31
AREAS AND MODULES IN VISUAL CORTEX
Ace'
~ 1
~~.\'
31
'.
`~ _ . __~
FIGURE 5 Flattened cortex showing ocular dominance bands in area 17 of a macaque
monkey. Black bands and black monocular area (left) indicate parts of layer 4 activated
by the contralateral eye. The central white oval corresponds to the blind spot of the
contralateral eye. The lower white area indicates a region of mission data. Based on
unpublished experiments of S.L~ Florence and J.H. Kaas.
alternating bands? Or are the bands an epiphenomenon resulting from se-
lections for other factors? The fact that the capacity for ocular dominance
bands can be experimentally induced without the direct evolution of this
capacity is suggested strongly by the research of Martha Constantine-Paton
(1982~. In one study, by adding an extra eye she directed two eyes into the
same optic tectum of a developing frog and the two inputs segregated into
alternating bands. Clearly, the frog's tectum did not evolve to segregate
visual information from each eye, but the capacity for such segregation
is there. The capacity must be a result of factors that evolved for some
other purpose. My point is that we need to be very cautious when talking
about the functions of such segregation as ocular dominance bands because
they might be totally unrelated to such functions as. for example. binocular
vlslon.
---I--,
Another example of a type of modular organization with implications
for how modules form is the lamination patterns in the lateral geniculate
nucleus. One of the remarkable features of the lateral geniculate nucleus
is that the laminar pattern is so variable across different mammals and
even different primates. Figure 6 is a schematic of the laminar patterns
in different primates. Why all these patterns? It is quite puzzling. Such
OCR for page 32
32
JON H. KAAS
variation has fascinated investigators for a long time (e.g., Walls, 1953), and
a number of hypotheses have been born and died along the way. Although
some understanding of the nature of lamination in some species in coming
is about, an adequate explanation for the great variety still is not available
Looking at a relatively simple lateral geruculate nucleus of an owl monkey,
one can see two poorly separated paIvocellular layers of medium-sized
neurons, two obvious magnocellular layers of large neurons, and a large
number of small cells in the interlaminar zone between the magno- and
parvocellular layers (Figure 7~. Thus, there are three main populations of
cells in the LGN: medium, very small, and large. The reason I show the
LGN of a nocturnal monkey is that the small cell system is well developed
in nocturnal monkeys. Parvocellular neurons have small receptive fields and
have a sustained response to a maintained stimulus, while magnocellular
neurons have large receptive fields and tend to respond to the onset and
offset of a stimulus (Sherman et al., 1976~. These are just examples of
some of the many differences in-the response properties of these kinds of
neurons. Determining the response properties of the small cells turns out
to be difficult because there are few of them and it is more difficult to
record from small neurons. However? Ibm Norton and Vivien Casagrande
(1982) have studied the small (W) cells in the LGN of galagos, in which
they are more frequent, and they have properties that are distinctively
different than pa~vocellular and magnocellular neurons.
These three groups of cells seem to relate to different processing
streams in the visual system that originate in the retina. The W-cell or
small cell stream from the retina passes through the lateral geniculate
nucleus and up into cortex, where it terminates largely in pulp in layer
3 (Fitzpatrick et al., 1983~. We have heard some speculation that the
paIvocellular system relates more to form vision, and the magnocellular
system more to motion, attention, and detection. One wonders about the
small cell system that is not very well-developed in diurnal primates. That
system may be modulating both of the other systems, not having a major
activating influence on cortical neurons.
However, the major point about the laminar pattern of the LGN of
primates (and other mammals) is that the layers separate neurons that have
different properties. The apparent significance of the different properties
is that they result in discorrelations in activity in development. The types
of discorrelations that occur depend on transduction factors in the retina,
which can be species variable. Other factors, such as the developmental
stage at which discorrelations become effective, may also be important.
Layers are also formed because activities in the two eyes are discorrelated.
In addition, breaks or discontinuities in layers (see Kaas et al., 1972) occur
because the nettle head or optic disc of the retina results in adjacent
neurons in some layers having significant discorrelations. In a very similar
OCR for page 33
33
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OCR for page 34
34
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JON H. BAAS
.
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FIGURE 7 A Nissl stained, paragittal section through the lateral geniculate nucleus of an
owl monkey showing the parvocellular, magnocellular and interlaminar neurons. Compare
with Figure 6. Based on Kaas et al., (1987a).
manner, the clusters of neurons and the groupings of thalamic inputs to
neurons in S-I of rats and mice, reflected in the cytochrome oxidase pattern
(Figure 4), may be the outcome of correlations in activity of afferents from
a single whisker or body part and the discorrelations that occur for inputs
from adjacent body parts. The argument here is that modular and laminar
segregations separate neurons of differing properties, but that this is an
outcome of the interaction of a few basic developmental mechanisms, and
such segregations need not have patent functional correlates.
VISUAL CORTEX ORGANIZATION IN PRIMATES
There are many uncertainties in our understanding of how visual
cortex is organized in primates, and differences in proposals for how cortex
is subdivided vary for New World and Old World monkeys and even from
laboratory to laboratory for the same species. Undoubtedly there are
major differences across the major primate groups, since such features
as brain size relative to body weight vary considerably from prosimians
to humans. Some of the proposed differences undoubtedly reflect the
difficulties in determining valid subdivisions of the brain and the limitations
of past and current studies. Nevertheless, some features have been clearly
demonstrated in a range of primate species and appear to be parts of the
OCR for page 35
AREAS AND MODULES IN VISUAL CORTEX
35
basic primate plan. Figure 8 is a drawing of a brain section cut parallel
to the surface of manually flattened cortex from the brain of a prosimian
galago. The drawing is of the posterior part of the hemisphere, and it
shows the distribution of transported label after an injection of the tracer,
WGA-HRP, into area 17. The advantage of the flattened preparation is
that a favorable section can reveal most of the surface-view patterns of
connections without the distortions and errors introduced by the laborious
reconstruction process from serial sections in traditional plans. The first
notable feature of the projection pattern is that small patches or puffs
of label are distributed in a pinwheel fashion around the injection site in
area 17. Such widespread, systematic, and discontinuous distributions of
intrinsic connections were first adequately described by Rockland and Lund
(1982, 1983~. In area 17 of primates, the most widespread connections
are between neurons in the cytochrome oxidase blobs. Widespread and
discontinuously distributed intrinsic connections appear to exist in all visual
areas of primates, suggesting that all areas are modularly organized. A
second notable feature is that nearly all of the projections are to two
other visual areas, V-II and the middle temporal visual area, MT. Such
connections have been consistently demonstrated since the very first studies
of the projections of area 17 (Kuypers et al., 1965), and they are part of
the evidence that areas 17, V-II, and MT are basic to all primates and
even to other mammals. Other, less dense projections (not apparent in the
figure) are to cortex we call the dorsomedial visual area, DM (see Lin et al.,
1982), and (from the dorsolateral part of area 17) the dorsolateral visual
area, DL (commonly called V-4 in macaque monkeys). These connections
have been shown to be present in a number of primate species, and this
provides part of the evidence that areas DL and DM are present in all
primates as well. A third notable feature of the projection pattern is that
it is discontinuously distributed in all targets, further supporting arguments
for modular organization in these fields. Such patchy projections of area
17 are characteristic of all studied mammals.
CONCLUSIONS
Figure 8 demonstrates what one can learn about the visual system from
a few simple experiments. I started this talk by noting the magnitude of
the problem of determining the organization of visual cortex in mammals
when there are so many species, and when their brains appear to vary so
much. I argued that the concepts of cortical areas and modules within
areas are valid and useful, but cautioned that modules may occur in the
brain for developmental reasons that are not related to obvious function.
I also noted that there have been many proposals for subdividing cortex.
Early proposals based largely or only on evaluations of differences in
OCR for page 36
36
it_
JON H. BAAS
. ~
..
.% .
L
.;~ -
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/
. .
I\
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'..
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A.''" :,.'`.~
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GALAGO 84-34
1 mm
FIGURE 8 A drawing of a brain section from the flattened cortex of a galago showing
the injection site of WGA-HRP (dark circle), and projections within area 17 and in more
rostral cortex. From Cusick and Kaas (19~.
histological appearance produced many conclusions we now know to be
in error. Thus, we need to critically evaluate past and current proposals,
with the realization that establishing valid subdivisions is difficult, and often
will depend on evidence from a multitude of procedures. Nevertheless, as
Figured shows, when the outline of a conceptual framework have been
established, much can be deduced from rather simple and easy experiments,
and the problem of having so many brains to study and understand is greatly
diminished.
REFERENCES
Brodmann, K
1909 Hergleichende Lokalisationslehre der GrosshimrJnde, Barth: Leipzig.
OCR for page 37
ARE4S AND MODUl AS IN VISUAL CORTEX
Constantine-Paton, M.
37
1982 The retinotectal hookup: the process of neural mapping. In: Developmental
Order: Its Origin and Re~laiu~n, S. Subtelny (ed.), New York: Alan R. Liss,
pp. 317-349.
Cusick, C G. and J.H. Kaas
1988 Surface view patterns of intrinsic and extrinsic cortical connections of area
17 in a prosimain primate. Brain Research 458, 383-388.
Dawson, D.R. and H.P. Killackey
1987
The organization and mutability of the forepaw and hindpaw representations
in the somatosensory cortex of the neonatal rat. 1 Comp Neurol. 256, 246-
256.
Li, X-.G., S.L. Florence, and J.H. Kaas
Areal distribution of cortical neurons projecting to different levels of the
caudal brain stem and spinal cord in rats. Somato. & Motor Rats. Vol. 7.
K. Itoh, and I.T. Diamond
The laminar organization of the lateral geniculate body and striate cortex
in the squirrel monkey (Saimin sciureus). 1 Neuroscience 3, 673-702.
M. Conley, and V.A. Casagrande
Ocular dominance columns and retinal projections in New World spider
monkeys. (Ateles ater). 1 Comp. Neuro. 243, 23~248.
Fitzpatrick, D.,
1983
Florence, S.L^,
19&6
Gould, HJ., III, and R.C. Lewontin
1979 The spandrels of San Marco and the Panglossian paradigm: A critique of
adaptation programme. Prac. R. Soc. Lond. (viol.) 205:234-248.
Kaas, J.H., M.F. Juerta, J.T., and J.K Horting
1978 Patterns of retinal terminations and laminar organization of the lateral
geniculate nucleus of pnmates. "l Cornp. Newol. 182, 517-554.
Kaas, J.H.
1982 The segreation of function in the nervous system: Why do sensory systems
have so many subdivision? In: Contributors to Sensory Physiology, W.P. Neff
(eddy. Academic Press, New York, pp. 201-240.
Kaas, J.H.
1987a The organization of neooortex in mammals: Implications for theories of
brain function. And Rev. of Psych. 38, 124-151.
1987b The organization and evolution of neocortex. Pp. 347-378 in Higher Brain
Functions, S.P. Wise (ed.~. New York: John Wiley.
Kaas, J.H., R.W. Guillery, and J.M. Allman
1972 Some principles of organization in the dorsal lateral geniculate nucleus.
Bram, Behavior, Evol. 6, 253-299.
Kuypers, H.GJ.M, M.K Szwarcbart, M. Mishkin, and H.E. Rosvold
1965 Occipitotemporal cortico~ortical connections in the rhesus monkey. Exp.
neurol 11:245-262.
Lin, C-S., R.E. Weller, and J.H. Kaas
1982 Cortical connections of striate cortex in the owl monkey. ~ Comp. Neurol.
211, 165-176.
Livingstone, M.S. and D.H. Hubel
1988 Segreation of form, color, movement, and depth: Anatomy, physiology, and
perception. Science 240, 740-749.
Middlebrooks, J.C, R.W. Dykes and M.M. Merzenich
1980 Binaural response-specific bands in primary auditory cortex (A-I) of the cat:
Topographic organization orthogonal to isotrequency contours. Brain Res.
181, 3148.
OCR for page 38
38
JON H. BAAS
Mountcastle, V.B.
1957 Modality and topographic properties of single neurons of cats somatic
sensory cortex. 1 Neuro~zysiol, 20, 408434.
Norton, T.T. and VA. Casagrande
1982 Laminar organization of receptive-field properties in the lateral geniculate
nucleus of bushba~y (Galago crassicoudatus). I Neurophysiol. 47, 715-741.
Radink~y, L.
1975 Primate brain evolution. American Sci. 63, 656-663.
Rockland, K.S., and J.S. Lund
1982 Widespread periodic intrinsic connections in tree shrew visual cortex (area
17). Sconce 215:1532-1534.
1983 Intrinsic laminar lattice connections in primate visual cortex. 1 Comp.
Neurol. 216:303-318.
Sherman, S.M., J.R. Wilson, J.H. Kaas, and S.V Webb
1976 X and Y cells in the dorsal lateral geniculate nucleus of the owl monkey
(Aotus trivirgatus). Science 1g2, 475477.
Sur, M., J.T. Wall, and J.H. Kaas
1984 Modular distribution of neurons with slowly adapting and rapidly adapting
responses in area 3b of somatosensory cortex of monkeys. ~ Neuroph~io.
51, 724-744.
Walls, G.W.
1953 The lateral geniculate nucleus and visual histophysiology. Univ. of Calif.
Publ. Physio., pp. 911-1000.
Welker, W.I.
1973 Principles of organization of the ventrobasal complex in mamammals. Brad',
Behav.' Evol. 7, 253-336.
Representative terms from entire chapter:
ocular dominance
