The Connectional Organization of the
Cortico-thalamic System of the Cat
J.W. Scannell, G.A.P.C. Burns, C.C. Hilgetag, M.A. O’Neil1 and
M.P. Young
Neural Systems Group, Psychology Department, Ridley
Building, University of Newcastle, Newcastle upon Tyne NE1
7RU and 1The Bee Systematics and Biology Unit, Hope
Entomological Collections, University Museum, Oxford
University, Parks Road, Oxford OX1, UK
Data on connections between the areas of the cerebral cortex and
nuclei of the thalamus are too complicated to analyse with naked
intuition. Indeed, the complexity of connection data is one of the
major challenges facing neuroanatomy. Recently, systematic
methods have been developed and applied to the analysis of the
connectivity in the cerebral cortex. These approaches have shed
light on the gross organization of the cortical network, have made it
possible to test systematically theories of cortical organization, and
have guided new electrophysiological studies. This paper extends
the approach to investigate the organization of the entire corticothalamic network. An extensive collation of connection tracing
studies revealed ~1500 extrinsic connections between the cortical
areas and thalamic nuclei of the cat cerebral hemisphere. Around
850 connections linked 53 cortical areas with each other, and
around 650 connections linked the cortical areas with 42 thalamic
nuclei. Non-metric multidimensional scaling, optimal set analysis
and non-parametric cluster analysis were used to study global
connectivity and the ‘place’ of individual structures within the
overall scheme. Thalamic nuclei and cortical areas were in intimate
connectional association. Connectivity defined four major thalamocortical systems. These included three broadly hierarchical sensory
or sensory/motor systems (visual and auditory systems and a single
system containing both somatosensory and motor structures). The
highest stations of these sensory/motor systems were associated
with a fourth processing system composed of prefrontal, cingulate,
insular and parahippocampal cortex and associated thalamic nuclei
(the ‘fronto-limbic system’). The association between fronto-limbic
and somato-motor systems was particularly close.
Introduction
Empirical neuroanatomy has had tremendous success in tracing
the connections that link different regions of the brain.
However, individual connection tracing studies focus on the
connections of a few brain structures in a few individuals.
Therefore, each study describes only a small piece of the
connectional architecture of the whole brain. Trying to piece
together such data for an accurate overview of brain organization is like trying to solve an intricate jigsaw puzzle. There are
many complimentary approaches to help put the jigsaw
together. High-quality anatomical studies give us the right pieces.
Physiological and behavioural data help show how the pieces fit
together. We also think that systematic collation and analysis of
connection data are valuable tools to help assemble the puzzle
and describe the picture (Young, 1992; Hilgetag et al., 1996;
Scannell, 1997).
Preliminary attempts to collate neuronal connection data have
already been published for the extrinsic connections linking
areas in the cerebral cortices of the cat (Scannell and Young,
1993; Scannell et al., 1995; Scannell, 1997) and the macaque
(Pandya and Yeterian, 1985; Felleman and Van Essen, 1991;
Young, 1992, 1993). Systematic analyses of the data show that
the cortical networks of cats and macaques contain three
sensory systems and a fourth system, termed the ‘fronto-limbic’
system, composed of prefrontal, insular, cingulate, perirhinal,
retrosplenial and entorhinal cortex and the hippocampal
formation (Scannell and Young, 1993; Young, 1993; Scannell et
al., 1995; Young et al., 1995a,b). The sensory systems all exhibit
a degree of hierarchy, both in terms of laminar origin and
termination patterns (Felleman and Van Essen, 1991; Young,
1992; Scannell et al., 1995) and because there is a topological
progression with some areas being connectionally close to
peripheral input and others being distant from peripheral input.
Systematic collation and data analysis have also shown that the
simple local wiring rules can account for much of the pattern of
cortico-cortical connections in the cat and macaque (Young,
1992; Cherniak, 1994; Scannell et al., 1995; Scannell, 1997).
They have shown that sulci and gyri are arranged in a way that
efficiently reduces the volume of the connections in the extrinsic cortico-cortical network (Scannell, 1996, 1997; Van Essen,
1997). In addition, systematic analysis of cortical connectivity
has had success in guiding electrophysiological investigations of
the cat visual system (Scannell et al., 1996), and in providing
simple network models that account for classical str ychnine
neuronographic data (Hilgetag et al., 1997a).
It is important to extend the systematic approach to include
the thalamus. First, almost all neural signals from the sensory and
motor periphery reach the cerebral cortex via the thalamus (e.g.
Le Gros Clark, 1932; Jones, 1985). Second, cortical and thalamic
processing are closely co-ordinated (e.g. Le Gros Clark, 1942;
Kato, 1990; Guillery, 1995; Contreras et al., 1996; Crick and
Koch, 1998). Consequently, an undue emphasis on the cortical
networks ignores much of the machinery that may be
responsible for sensory and motor processing (Guillery, 1995).
This paper considers the extrinsic connections of both the
thalamus and cortex of the cat.
Researchers seeking large-scale principles of neuronal
organization face, in addition to the ‘jigsaw puzzle’ problem,
sampling bias and random sampling error in the primary
neuroanatomical literature. There is a strong bias towards a small
number of model structures (e.g. areas 17, 18 and LGN in the
case of the cat) for studies on laminar origin and termination
patterns, cellular targets, synaptic morphology, physiological
function, interindividual variability or development of connections. It is not clear if these structures are representative of the
rest of the brain. In contrast, systematic collation, while not
free from sampling bias, can yield relatively comprehensive
catalogues of the gross cortical connections (Young, 1993;
Scannell and Young, 1993; Scannell et al., 1995; Burns, 1997;
Scannell et al., 1997, and this paper). Therefore, although
lacking much of the richness and detail of the original reports,
the approach presented here is unusual in its breadth. We hope
that it can compliment the more traditional approaches to
neuroanatomy, upon which it is entirely dependent for data.
Cerebral Cortex Apr/May 1999;9:277–299; 1047–3211/99/$4.00
We are currently extending our collation methods (e.g.
http://riposte.usc.edu/NeuroScholar) to emphasize available
data on laminar origin and termination patterns, cellular targets,
synaptic morphology, physiological function, interindividual
variability and development. We intend to apply systematic
analysis to these aspects of connectivity as and when data
become available for a wider range of connections.
Before systematically analysing connection data, it is necessary to produce an accurate catalogue of connections. This step
presents major practical problems. The primary data differ in,
for example, individual animals, quality, method, anatomist,
parcellation and nomenclature. There is considerable variability
in the cortical connectivity of individuals, though less so in
thalamic connectivity (MacNeil et al., 1997). Parcellation is
difficult, especially in brain regions that lack systematic maps of
the sensor y periphery (Colby and Duhamel, 1991). Furthermore, connection tracing is rarely quantitative, ‘interesting’
parts of the brain have been studied more than ‘boring’ parts,
and connections are not all the same. Therefore, it is necessary to
exercise considerable judgement at this stage of the study. These
problems notwithstanding, systematic collation is the only way
to produce an explicit summary of known connectivity. The first
part of this paper presents such a collation of the connectional
neuroanatomy of the cat thalamo-cortico-cortical network.
The second part of the paper presents the results of three
formal analyses that summarize the thalamo-cortical system’s
connectional architecture in a relatively compact and comprehensible way. The analyses illustrate the ‘place’ of individual
structures in the global connectional scheme and show global
features of the organization of the system that are not apparent
on casual inspection of the primary connection data. We hope
that both the collation and the analyses will guide new empirical
research and test organizational models of the thalamo-cortical
network as they have already done in the cortical network
(Scannell et al., 1996; Hilgetag et al., 1996; Scannell, 1997).
Materials and Methods
Full details of cortical and thalamic parcellation, connections and
citations of the original connection and mapping studies are published at
http://ww w.psychology.ncl.ac.uk/jack/cor_thal.html.
Table 1 gives a list of abbreviations used.
Cortical Parcellation
The cortical parcellation used in this study was based on Reinoso-Suarez
(1984), adapted by Scannell et al. (1995). Parcellation of the prefrontal
cortex and cortex on the medial side of the hemisphere (sometimes
termed ‘limbic’ cortex) was modified from Scannell et al. (1995) to bring
it into line with the schemes of Musil and Olson (1988a,b, 1991), Olson
and Jeffers (1988), and Olson and Musil (1992). All prefrontal cortex on
the lateral side of the hemisphere has been assigned to the area PFCl.
Areas ALG, SSF, SVA, DP, Amyg and 5m of the parcellation of Scannell et
al. (1995) have been omitted from this study because of a lack of data on
their connections with the thalamus or because of our uncertainty over
their identity or existence. Areas 4g and 4 of Scannell et al. (1995) have
been retained, but are probably cytoarchitectonic subregions of a single
primary motor area. Similarly, areas 3b, 1 and 2 probably constitute a
single primar y somatosensor y area, SI (Felleman et al., 1983). The
auditor y cortical area V P is termed ‘V Pc’ to distinguish it from the
ventroposterior nucleus of the thalamus.
Thalamic Parcellation
Parcellation of the thalamus was based on the atlas of Berman and Jones
(1982). In some thalamic regions (e.g. the lateral posterior complex, the
medial geniculate body, the ventrobasal complex) Berman and Jones’
terminology and parcellation were modified to conform to more recent
anatomical and physiological studies. The scheme used in this paper, and
278 Cat Cortico-thalamic Network • Scannell et al.
Table 1
Anatomical abbreviations
1
17
area 1
area 17
LGLI
LIc
18
area 18
Llo
19
2
area 19
area 2
LM-Sg
LPI
20a
area 20a
LPm
20b
21a
21b
area 20b
area 21a
area21b
MD
MGDd
MGDdd
35
area 35
MGDds
36
area 36
MGM
3a
3b
area 3a
area 3b
MGV
MGvl
4
4g
5Al
5Am
5Bl
5Bm
61
6m
7
AAF
AD
AES
AI
areas 4f, 4sf and 4d
area 4γ
lateral area 5A
medial area 5A
lateral area 5B
medial area 5B
lateral area 6
medial area 6
area 7
anterior auditory field
anterodorsal nucleus
anterior ectosylvian sulcus
primary auditory field
MV-RE
P
PAC
PARA
PARP
PAT
PF
PFCI
PFCMd
PFCMil
PLLS
PMLS
POi
AII
ALLS
secondary auditory field
anterolateral lateral suprasylvian
area
anteromedial nucleus
anteromedial lateral
suprasylvian area
anteroventral nucleus
anterior cingulate cortex
posterior cingulate cortex
central lateral nucleus
centre median nucleus
central medial nucleus
dorsolateral suprasylvian area
entorhinal cortex
POl
POm
AM
AMLS
AV
CGa
CGp
CLN
CM
CMN
DLS
Enr
EPp
posteror part of posterior
ectosylvian gyrus
Hipp
hippocampus proper
Ia
agranular insula
Ig
granular insula
LD
lateral dorsal nucleus
LGLA
A laminae of the lateral
geniculate
LGLC
magnocellular C laminae of the
lateral geniculate
LGLC 1–3 parvocellular C laminae of the
lateral geniculate
LGLGW
geniculate wing
PS
pSb
medial interlaminar nucleus
caudal region of the lateral
intermediate nucleus
oral region of the lateral intermediate
nucleus
lateral medial-suprageniculate complex
lateral nucleus of the lateral posterior
complex
medial nucleus of the lateral posterior
complex
mediodorsal nucleus
dorsal nucleus of the medial geniculate
deep dorsal nucleus of the medial
geniculate
superficial dorsal nucleus of the medial
geniculate
magnocellular division of the medial
geniculate
ventral division of the medial geniculate
ventrolateral nucleus of the medial
geniculate
medioventral-reuniens nucleus
posteror auditory field
paracentral nucleus
anterior paraventricular nucleus
posterior paraventricular nucleus
parataenial nucleus
parafasicular nucleus
lateral prefrontal cortex
dorsal medial prefrontal cortex
infralimbic medial prefrontal cortex
posterolateral lateral suprasylvian area
posteromedial lateral suprasylvian area
intermediate region of the posterior
complex
lateral region of the posterior complex
medial region of the posterior complex
SUM
Tem
VA-VL
VLS
VMB
posterior suprasylvian area
presubiculum, parasubiculum and
postsubicular cortex
pulvinar
rhomboid nucleus
retrosplenial area
subiculum
suprageniculate nucleus
second somatosensory area
fourth somatosensory area
inner (deep) suprasylvian sulcal region
of area 5
outer suprasylvian sulcal region of
area 5
submedian nucleus
temporal auditory field
ventroanterior-ventrolateral complex
ventrolateral suprasylvian area
basal ventromedial nucleus
VMP
principal ventromedial nucleus
VPc
ventroposterior auditory field
VPL
lateral part of the ventroposterior
nucleus
medial part of the ventroposterior
nucleus
PUL
RH
RS
Sb
SG
SlI
SlV
SSAi
SSAo
VPM
its relation to Berman and Jones (1982) and to other recent studies, is
outlined in the Appendix.
The Appendix shows that there is a lack of consistency in
the nomenclature and parcellation used in different studies of certain
thalamic regions. Similar problems apply in the cortex (Scannell et al.,
1995). In some cases, this is due to methodological differences.
Connection tracing (the bulk of the data used in this study), cyto- or
chemo-architectonics and electrophysiological mapping do not always
yield divisions that map neatly onto one another. These problems made
collation particularly difficult for connections of the anterior parts of
the lateral posterior complex, the posterior group of nuclei and the
suprageniculate region. Difficulties were also encountered in the lateral
posterior complex, the medial geniculate complex, the posterior group
and the ventroposterior complex, where there is controversy or
ambiguity as to the ‘best’ parcellation.
Collation of Connection Data
We produced a database of thalamo-cortico-cortical connections from
information in a large number of published studies of connectivity in the
adult cat. Only data from the cat were used and no extrapolations were
made from structures thought to be homologous in other species. We
considered data in the text and figures of papers on cat cortico-cortical,
cortico-thalamic and thalamo-cortical connectivity. For each reported
instance of a connection, the database gives the origin and target regions,
the weight or strength (see below), local origin or termination patterns
(where known), and the study from which the data were taken. For some
connections, the database has notes on alternative parcellation schemes.
Connections that were reported as dense or strong were given a strength
weighting of 3; weak or sparse connections were weighted as 1; and
connections of intermediate strength, or for which no strength
information was available, were weighted as 2. We assumed that
descriptions of the density of projections as strong, moderate or weak
made in different studies were equivalent.
Translation from Database to Connection Matrix
The areas that we have chosen to include in our connection matrix ref lect
current connection data. We have balanced the need to avoid ‘lumping’
together structures that are known to be distinct, with the need to have
sufficient data for each structure listed in the connection matrix. Where
the database contains fine areal or nuclear divisions that have not yet been
resolved in a substantial number of connection tracing studies, we have
tended to ‘lump’ them together for the analyses. For example, Clasca et al.
(1997) present compelling evidence that our cortical areas Ia and Ig both
contain a number of distinct cortical fields. However, there are no
published cortical connection data for these divisions at present, so we
include Clasca et al.’s areas ALd, ALv and PI within our area Ia, and Clasca
et al.’s areas AS, DI and GI within our area Ig. We hope that our
parcellation will improve as new data become available.
The database was used to produce our best estimate of thalamocortical connectivity, shown in Table 2. Where there was any doubt in
translation between the database and connection matrix, we checked the
original papers and reconsidered both text and figures. The translation
process was difficult when the reports in the literature could not be
unambiguously assigned to a single one of our areas or nuclei. In these
cases, we were careful to balance the need to include as many reported
connections as possible, with the need to avoid ‘extrapolating’
connections to areas or nuclei which do not, in fact, possess them. For
example, if one study identified a connection from a cortical area to the
‘lateral posterior nucleus’, LP, while other studies identified connections
to specific nuclei within LP (e.g. LPl and LPm), then we would to assign
the connections to LPl and LPm only.
Occasionally, given all the information in the database, a connection
could still not be assigned unambiguously to one of our areas or nuclei. In
these cases, where reasonable, we would assign the connection to our
areas included within the larger, ambiguous region. For example, if the
only available information showed that Sg projected to areas 35 and/or
36, then we would assign the connection to both 35 and 36. We are aware
that this risks one false positive connection. However, the alternative
(ignoring Sg’s projection to 35 and/or 36) is certain to incur at least one,
and may incur two, false negatives. In practice, we applied this reasoning
to a relatively small number of connections, which disproportionately
affected areas 35 and 36, and nuclei PAR A and PARP. Areas 35 and 36 are
thin, adjacent strips of cortex that are difficult to inject independently.
PAR A and PARP are small, adjacent thalamic nuclei that are difficult to
distinguish in connection tracing studies. Therefore, there is likely to be
some ‘cross-contamination’ in our connection matrix and analyses,
between areas 35 and 36, and between thalamic nuclei PAR A and PARP.
All measurements in all areas of science are likely to contain a degree
of error. This is true for our database and our translation from database to
connection matrix. While we have make great efforts to be as accurate as
possible, we encourage readers to check the primary literature before
uncritically accepting connections presented here.
Missing Connections
In principle, our analyses could distinguish between connections that
have been explicitly reported as absent and connections for which there
are no explicit reports of presence or absence. For several reasons, we
have chosen to analyse ‘confirmed absent’ and ‘unreported’ connections
as if they were equivalent. First, most areas or nuclei only connect with a
small fraction, 5–25%, of the other structures, so non-connections are the
norm. Non-connections are rarely reported explicitly unless they are
unexpected, controversial or otherwise of particular interest. Therefore,
there are few explicitly confirmed absent connections in the cat.
However, a substantial proportion of studies in the cat inspect large
regions of the brain (sometimes the entire cortex or thalamus) for labelled
cells or terminals. We take the lack of reports of connections in the text
or figures of such papers as implicit evidence of absence and think it
unlikely that there are many substantial undiscovered connections
between well-studied regions of the brain. Of course, this may change
with the introduction more sensitive connection tracing methods, or
more systematic attention to very small quantities of transported tracer
that may currently be dismissed as ‘background’ labelling.
Second, many ‘missing’ connections belong to poorly studied regions
of the brain. Therefore, we have chosen to omit some of the less studied
areas from the analysis. We also think that ‘missing’ connections are most
likely to be projections to regions of the brain that have received few
tracer injections. This is because retrograde tracing studies are more
common than anterograde tracing studies and projections from the
poorly studied brain regions show up when the better studied regions are
injected.
Third, it is likely that thalamo-cortical connections show complete
reciprocity (e.g. Raczkowski and Rosenquist, 1983; Burton and Kopf,
1984b; Symonds and Rosenquist, 1984; Jones, 1985). Therefore, assuming thalamo-cortical reciprocity should give a more accurate picture
than using only connections that have been directly demonstrated. For
example, many thalamo-cortical connections, found with cortical injections of retrograde tracers, have not been studied using cortical injections
of anterograde tracers, or thalamic injection of retrograde tracers. To
avoid ignoring such connections, our analyses assume that all connections between cortex and thalamus are strictly reciprocal.
Fourth, to see if ‘missing’ cortical reciprocal connections in Table 2
have a major effect on the results of our analyses, we have produced, and
analysed with non-metric multi-dimensional scaling (NMDS), alternative
matrices that assume cortical connections are also reciprocal. First, a
symmetrized matrix, ‘sym’, was produced by ref lecting the ‘raw’ matrix
(Table 2) about its leading diagonal and then adding the ref lected matrix
to the original. Second, a ‘winner takes all’ matrix, ‘wta’, was produced by
ref lecting the raw matrix about its leading diagonal and then adding it
back to the original in a ‘winner takes all’ fashion, so that the strongest
connection at any site was retained. The raw matrix (Table 1) and the wta
matrix have four ranks of connection strength: 0 (absent or unreported),
1 (weak or sparse), 2 (moderate) and 3 (strong or dense). The sym matrix
has seven ranks of connection strength (0–6).
Fifth, Young (1992) and Young et al. (1995) have examined
quantitatively the effects of different ways of treating non-connections
and found that they have a relatively small effect on the overall solution.
Few connections in the cat have been explicitly reported as absent, so
adding an extra rank to the connection matrix (Table 2) to distinguish the
few ‘confirmed absent’ connections from the many ‘presumed absent’
connections would have ver y little effect. We also note that all our
analyses on n structures seek to satisfy (n2 – n) connectional constraints
while each structure contributes only (2n – 1) connectional constraints.
Therefore, major changes in the connection patterns of a few poorly
studied structures will have a major impact on those structures in any
connectional analysis, but will have ver y little impact on the overall
analysis (Scannell et al., 1995; Young et al., 1995).
Cerebral Cortex Apr/May 1999, V 9 N 3 279
We think that this approach is reasonable and we prefer it to
mathematically based missing data estimation methods (Jouve et al.,
1998). The issue of missing connections can be properly resolved only by
further anatomical study.
Analysis Methods
Large connection matrices are too complicated for analysis with unaided
intuition. We have used three methods to summarize and represent the
connection data in a more transparent way. The first, and most widely
used to date (e.g. Young, 1992, 1993; Scannell and Young, 1993; Scannell
et al., 1995; Young et al., 1995a,b,c), is NMDS. Simmen et al. (1994) and
Goodhill et al. (1995) have criticized the use of NMDS on anatomical
connection data. While we think that NMDS can recover structure from
anatomical connection data (Young et al., 1994, 1995c), the comments of
Simmen et al. (1994) and Goodhill et al. (1995) have been very useful.
They have encouraged us to develop and apply a wide range of analysis
methods to help distinguish real connectional features from the
peculiarities of individual analysis methods. Therefore, we have analysed
connection data with NMDS, with non-parametric cluster analysis and
with a novel method, optimal set analysis.
Non-metric Multidimensional Scaling
NMDS is a method that finds a configuration of points (in this case
cortical areas and thalamic nuclei) that matches a set of experimentally
measured ‘proximities’ (in this case anatomical connections). In a
theoretically perfect arrangement, all areas or nuclei that are connected
by a connection weighted ‘3’ should be closer to each other than all areas
linked by a connection weighted ‘2’, which should in turn be closer than
areas linked by a ‘1’, and so on. Such an ideal configuration would
perfectly capture the connectional proximities present in the connection
data — a process exactly analogous to deriving a map from a mileage chart
in a road atlas.
NMDS is a form of data compression in which a large set of ordinal
measures (e.g. 95 × 95 connection matrix) is represented as a smaller set
of metric measures (e.g. 95 × 3 set of coordinates). This kind of data
compression is not without problems. For example, if the proximity
matrix is asymmetric (e.g. Table 2), the connectional constraints cannot
be perfectly satisfied by a spatial arrangement of points. Nevertheless,
extensive simulation studies (Young et al., 1994, 1995c), have shown that
this approach performs excellently in recovering metric structure from
test data that matches as closely as possible the structure of neuroanatomical connection data. Analyses of test data designed to match the
cat thalamo-cortical connection data has found that cat thalamo-cortical
connection data behaves like ‘noisy’ test data generated in between four
and six dimensions (Scannell et al., 1997; J. W. Scannell, unpublished
obser vations). NMDS configurations derived from such test data in
the appropriate number of dimensions (i.e. five dimensions for fivedimensional test data) typically account for ∼90% of the variability of the
original test structure. For practical purposes, we are limited to
presenting three-dimensional configurations, which typically account
for ∼50% of the variability of appropriate five-dimensional test data
(J.W. Scannell, unpublished observations).
Young et al. (1995c) have shown that for appropriate test data, some
matrix transforms can slightly enhance the ability of NMDS to recover
metric structure. Consequently, we have performed the wdsm1 transform (Young et al., 1995c) prior to NMDS for all the cortical systems
illustrated in this paper. The high degree of congruence between
solutions of the wdsm1-transformed matrices and the solutions derived
from the other matrices is shown in Table 3, and is illustrated in Figures 1
and 2 which show configurations derived with ALSCAL from wdsm1,
raw and sym matrices. Configurations derived with the other NMDS
approaches and have been inspected using computer animation.
Configurations of points derived from low-level ordinal data vary with
NMDS method. Previous studies (e.g. Young, 1992, 1993; Scannell and
Young, 1993) have derived two-dimensional configurations with the
ALSCAL algorithm (Takane et al., 1977; Young et al., 1978) in the SPSS
statistical environment (Young and Harris, 1990). In this paper we
present stereograms of three-dimensional configurations as they capture
more of the data structure. The configurations were derived using a
variety of NMDS methods to see which features of the configurations are
robust and consistent properties of the connection data. The methods
280 Cat Cortico-thalamic Network • Scannell et al.
were ‘MDS’ in the SAS statistical programming environment and ‘ALSCAL’
in the SPSS environment. All connection data (raw, sym, wta and wdsm1)
were scaled at the ordinal level of measurement using the secondary
approach to ties (Kruskal, 1964a,b; Young et al., 1995c). SAS solutions
were produced using options ‘FIT = 1’ (fits the data to distances) and ‘FIT
= 2’ (fits the data to squared distances). ‘FIT = 2’ emphasizes the
importance of long distances (i.e. non-connections) and produces output
similar to ALSCAL.
NMDS solutions can become trapped in local minima. To guard
against this, we shuff led the order of areas in each matrix while keeping
the connections the same. Shuff ling input order had no effect on all
solutions produced with MDS in SAS, and on solutions derived from
symmetric matrices (sym, wta, wdsm1) with ALSCAL in SPSS. However,
shuff ling input order did inf luence the apparent fit of solutions derived
from raw matrices with ALSCAL in SPSS. In these cases, we found that a
‘seeding’ method was effective in finding low-dimensional configurations
with relatively high apparent fit. Configurations derived from wta or sym
matrices were used to provide the initial coordinates for scaling the raw
matrix. The resulting raw configurations reached convergence in fewer
iterations and had higher apparent fit than configurations derived from
random co-ordinates.
NMDS configurations can be compared quantitatively using a
regression-like procedure, Procrustes’ rotation (Schonemann and Carrol,
1970; Gower, 1971). Procrustes’ rotation, implemented in the GENSTAT
statistical programming language, can compare structures in any number
of dimensions by rigidly ref lecting, rotating and then scaling them to find
an optimal fit. After finding the best rigid transform, Procrustes yields a
variance explained statistic (R2) that ref lects the goodness of fit. The
statistical rarity of each comparison may be evaluated using a
randomization test (Edgington, 1980) in which one of the structures is
repeatedly randomly shuff led and the R2 statistic recalculated.
Optimal Set Analysis
Optimal set analysis is an alternative and independent method for
analysing global connectional architecture. This approach views a ‘neural
system’ as something containing components more connected with each
other than with the components of other systems. So, for example, the
visual system might be thought of as the collection of cortical areas and
thalamic nuclei that are highly interconnected with each other and
relatively weakly connected with the other cortical systems. We wanted
to identify the systems within the thalamo-cortical network by arranging
areas and nuclei into sets with as few as possible connections between
the sets and as few as possible non-connections within the sets. We
wanted to let the connection data itself decide the number and the
composition of the sets of areas and nuclei.
Optimal set analysis presents a formidable computational problem so
connection data were analysed using CANTOR, a custom-written
software system for statistical optimization. The CANTOR system consists
of a database of objects, linked to each other through relational functions.
In the present application, the CANTOR objects represented cortical
areas and thalamic nuclei, while the relational functions represented their
anatomical interconnections. CANTOR arranges the objects by stochastic
optimisation to minimize the cost of the relational functions in a way that
is chosen by the experimenter. We chose costs that matched our idea of a
neural system; a thing containing components more connected with each
other than with the components of other systems. Therefore, the cost
measured non-connections within sets and connections between sets.
The CA NTOR environment provides routines that permit the
rearrangement of the objects within the database to minimize costs by
evolutionary optimization, a method based on simulated annealing (Van
Laarhoven and Aarts, 1987). More technical details of the optimization
algorithm adapted for connectivity analyses are described by Hilgetag et
al. (1996, 1997b, 1998). The software is written in ANSI-C and has been
implemented on a wide variety of Unix computer systems. Special
memory management ensures that the large quantities of complex data
can be analysed on medium-sized workstations. Our computations are
run on a DEC Alpha 3000–600 workstation with 256 MB of R AM. The
runs typically require several hours to weeks of CPU time, depending on
the size of the area-to-area connectivity matrices and the thoroughness of
search for optimal solutions.
In this study, CA NTOR minimized a two-component integer cost
function to find optimal connectional sets within the thalamo-cortical
system. The cost consisted two terms. The first was a repulsion term that
counted non-existing connections within the potential sets. Optimizing
the repulsion component alone breaks larger clusters into smaller
clusters. The second cost consisted of an attraction term counting
existing connections between sets. Optimizing the attraction term alone
will, in the limit case, result in one large cluster containing all the areas
and nuclei.
The costs of existing connections between sets were proportional to
the connection weights (‘1’, ‘2’ or ‘3’). This cost function assumes an
arithmetic relationship between connection ranks, in contrast to NMDS
which simply assumes a monotonic relationship between connection
ranks. Starting from a random assignment of areas to sets, the number and
structure of the sets were changed iteratively through relocation of one
area to another set or by swapping of two areas belonging to different
sets. Thresholds were set to ensure that structures made stepwise
improvements. These set manipulations and threshold parameters
ensured that the space of candidate arrangements was searched at the
smallest possible resolution and that the search did not become trapped
in local minima. Hence, the CANTOR system determined automatically
the number and composition of sets for the optimal overall arrangement
of the areas and nuclei of the thalamo-cortical system.
We changed the number and composition of the optimal sets by
var ying the strength of attraction and repulsion in the cost function.
Repulsion ranged from 30 to 1 (cost of non-connections within clusters),
with attraction set to 1. Attraction ranged from 1 to 10 (the attraction
term is multiplied by connection weight to compute the cost of
connections between clusters), while repulsion was held at 1. Increasing
the attraction yields larger sets, or only one in the limit case of
overwhelming attraction. Reducing attraction yields more, smaller sets.
These manipulations make it possible to explore the cluster structure of
the thalamo-cortical network over a range of scales. They emphasize
different aspects of the organization of the network. Perhaps the most
intuitively obvious, and the one shown in Figures 3 and 4 is the case
where attraction and repulsion are both set to 1. Here, the cost of
individual connections between clusters equals their connection weight
and the cost of non-connections within clusters equals 1. Since CANTOR
typically finds many different solutions with the same or similar low cost,
the results are summarized in a matrix showing the proportion of the
solutions in which two structures occupied the same set.
We estimated the statistical significance of the results of optimal set
analysis by comparing the cost of solutions calculated from connection
data with the cost of equivalent solutions calculated from randomized
connection matrices. The cost of solutions derived from connection data
was comparable with the cost of solutions calculated from four- to
six-dimensional test data (Scannell et al., 1997; J.W. Scannell, unpublished
observations).
Non-parametric Cluster Analysis
Cluster analysis is a statistical method that finds groups within a data set.
It has already been used in the analysis of connection data by Musil and
Olson (1991) to delineate the areas in the medial frontal cortex of the cat,
and by Sherk (1986) to map the suprasylvian region of the cat. Here, the
rationale behind our use of cluster analysis was similar to that behind
optimal set analysis, i.e. to find groups of connectionally associated areas
and nuclei (see Hilgetag et al., 1998).
To see the robust and reliable groupings within the thalamo-cortical
network, we performed a variety of cluster analyses on a variety of NMDS
configurations. We used the SAS/STAT system to make several fivedimensional NMDS configurations from the raw connection matrix,
setting the FIT option to 0.5, 1 and 2, with both the primar y and
secondary approach to ties. NMDS analyses in five dimensions were
chosen for two reasons. First, the accuracy of density estimation falls with
increased dimensionality (Epanechnikov, 1969, Silvermann, 1986).
Second, studies with test data indicate that NMDS in five dimensions is
adequate to reasonably represent the cat thalamo-cortical connection data
(J.W. Scannell, unpublished observations). We then analysed the configurations using the MODECLUS non-parametric cluster analysis function in
the SAS/STAT system.
Before clustering, MODECLUS calculates the probability density
within the NMDS configuration using either fixed or variable radius
spherical kernels. With fixed radius kernels, all points have identical
kernel radii. The variable radius kernel approach is based on finding the
distance to the ‘kth nearest neighbour’. Once a density function has been
estimated for a set of points (in this case, representing cortical areas and
thalamic nuclei), MODECLUS detects the local maxima of the density
function to define the cluster structure of the data. When fixed radii
kernels are used, MODECLUS can then test the significance of clusters by
comparing the maximum estimated density at points within the cluster to
the maximum estimated density at the cluster’s boundary.
Since cluster analysis is adept at finding clusters in data where no real
clusters exist (Gordon, 1996), we ran ten kinds of MODECLUS analysis
paradigm on each of the six NMDS configurations and then pooled the
results. We did this to ‘average out’ the spurious clusters. We also ensured
that our cluster paradigms used a wide range of kernel radii so that both
local and large-scale cluster structure were explored. The first paradigm
ran 30 cluster analyses with increasing fixed-radius density estimation
and clustering kernels. The radius of this kernel ranged from the
minimum interpoint distance in the configuration to the mean interpoint
distance in the configuration. The second, third and fourth paradigms
used a fixed radius kernel that was calculated from the cluster analysis
under the first paradigm. The kernel radius was chosen to produce an
initial number of clusters equal to the total number of points in the
analysis divided by 2, 4 and 8 respectively. A hierarchical cluster tree was
obtained for each scheme by testing the significance of the clusters. This
procedure compared the maximum estimated density of points within a
cluster to the maximum around the cluster’s border (referred to here as
the ‘saddle point’) in order to estimate the cluster’s significance. On
successive iterations, the least significant cluster was dissolved or their
members assigned to a neighbouring cluster if it was close enough. The
fifth, sixth and seventh paradigms were based on nearest-neighbour
kernels with K values set to 2, 3 and 4 (i.e. the density estimation and
clustering kernels were set to the value so that the number of neighbours
of each point was a minimum of 1, 2 and 3). The eight, ninth and tenth
paradigms used a nearest-neighbour clustering method and gave a
hierarchically organized cluster scheme. This involved the use of
fixed-radius density estimation kernel (with radius set to the value of the
configuration’s mean distance) and a clustering kernel based on a
nearest-neighbour CK value set to 2, 3 and 4.
Since the series of cluster analyses finds many different solutions, the
results are summarized in a matrix showing the proportion of the solutions in which two structures occupied the same cluster. The summary
matrix (Figure 6) was corrected so that each paradigm contributed
equally to the cluster-count score. Note that a structure from a cluster that
had been dissolved (e.g. paradigms 2, 3 and 4) would not be counted as
being in the same cluster as any other structures, including itself. Thus
there may be cluster counts along the leading diagonal of Figure 4 of
<100%.
Results
Parcellation and Connections
Table 2 summarizes the literature on the extrinsic connectivity
of the thalamo-cortical network. It is drawn from the full
database (see http://ww w.psychology.ncl.ac.uk/jack/cor_thal.
html), which provides considerably more detailed information.
The connection strengths are on an ordinal scale that assumes
only that the relationship between the ranks is monotonic; a ‘3’
is stronger than a ‘2’ which is stronger than a ‘1’ which is
stronger than a ‘0’. In quantitative terms, connections weighted
‘3’ may be 2 or 3 orders of magnitude stronger than connections
weighted ‘1’ (see e.g. Musil and Olson, 1988a,b). Table 2 shows
826 cortico-cortical connections. Each cortical area connects
with, on average, 15 other areas (SD = 8). This level of
connectivity corresponds to 28% of possible cortico-cortical
connections. Table 2 also shows 651 connections between
thalamus and cortex, which we assume are wholly reciprocal
(Raczkowski and Rosenquist, 1983; Burton and Kopf, 1984b;
Symonds and Rosenquist, 1984; Jones, 1985). On average, each
Cerebral Cortex Apr/May 1999, V 9 N 3 281
282 Cat Cortico-thalamic Network • Scannell et al.
Table 2
Matrix of cortico-cortical, thalamo-cortical and cortico-thalamic connections in the cat. Connections weighted ‘3’ are strong, connections weighted ‘2’ are intermediate, connections weighted ‘1’ are weak
or sparse, and connections marked ‘.’ have been explicitly reported as absent or are unreported. The upper square of the table shows cortico-cortical connections. Reading horizontally in this part of the
table, from a cortical structure, shows afferent connections and reading vertically shows efferent connections. The lower rectangle of the table shows cortico-thalamic and thalamo-cortical connections,
which we assume are invariably reciprocal (Raczkowski and Rosenquist, 1983; Burton and Kopf, 1984b; Symonds and Rosenquist, 1984; Jones, 1985). The right margin of the table summarizes the
frequency distribution of connection weighted 3, 2 and 1 for each anatomical structure. Afferent and efferent connections have been pooled. Darker shades indicate a greater number of connections. The
area to area connection data in this table formed the sole input to the NMDS, optimal set, and non-parametric cluster analyses.
←
Table 3
Different NMDS methods yield congruent result
Algorithm
ALSCAL
ALSCAL
ALSCAL
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
sym
wta
wdsm1
sym
wdsm1
sym
wdsm1
wta
wdsm1
wta
wdsm1
raw
wdsm1
raw
raw
raw
sym
sym
wta
wta
2
1
2
1
2
1
2
1
2
1
2
1
*
*
*
1.00
0.98
1.00
0.97
1.00
0.98
0.90
0.79
0.90
0.80
0.91
0.80
*
*
*
*
1.00
0.97
1.00
0.98
1.00
0.86
0.79
0.87
0.81
0.87
0.80
*
*
*
*
*
*
*
*
1.00
0.97
1.00
0.97
0.88
0.78
0.89
0.80
0.90
0.80
*
1.00
0.97
1.00
0.85
0.78
0.86
0.80
0.86
0.80
*
*
*
*
*
*
*
1.00
0.98
0.90
0.79
0.90
0.80
0.91
0.80
*
*
*
*
*
*
*
*
1.00
0.86
0.79
0.87
0.81
0.87
0.80
*
*
*
*
*
*
*
*
*
1.00
0.88
1.00
0.88
0.99
0.87
*
*
*
*
*
*
*
*
*
*
1.00
0.88
0.97
0.87
0.94
*
*
*
*
*
*
*
*
*
*
*
1.00
0.89
1.00
0.87
*
*
*
*
*
*
*
*
*
*
*
*
1.00
0.88
0.97
*
*
*
*
*
*
*
*
*
*
*
*
*
1.00
0.88
*
*
*
*
*
*
*
*
*
*
*
*
*
Matrix
raw
Fit
Algorithm Matrix
ALSCAL
ALSCAL
ALSCAL
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
MDS
raw
sym
wta
wdsm1 sym
wdsm1 sym
wdsm1 wta
wdsrn1 wta
wdsm1 raw
wdsm1 raw
raw
raw
sym
sym
wta
wta
Fit
2
1
2
1
2
1
2
1
2
1
2
1
1.00
1.00
0.99
0.88
0.84
0.86
0.83
0.88
0.84
0.99
0.88
0.99
0.88
0.98
0.86
*
1.00
1.00
0.89
0.85
0.87
0.84
0.89
0.85
0.99
0.88
0.99
0.88
0.98
0.86
*
*
1.00
0.89
0.85
0.88
0.84
0.89
0.85
0.99
0.87
0.99
0.88
0.99
0.87
1.00
2
The table shows the goodness of fit (variance explained, R , from Procrustes’ rotation) between configurations derived in three dimensions using a variety of NMDS methods on the raw, symmetrized,
winner-takes-all and wdsm1 transformed connection matrices (‘raw’, ‘sym’, ‘wta’ and ‘wdsm1’). ‘ALSCAL’ and ‘MDS’ show whether configurations were derived using ALSCAL or MDS. ‘f1’ and ‘f2’ were
derived using ‘FIT=1’ and ‘FIT=2’ respectively. The table shows that all NMDS methods yield quantitatively similar results.
thalamic nucleus projects to, and receives projections from, 15
cortical areas (SD = 10). This constitutes 28% of all possible
thalamo-cortical connections, were each nucleus to project to
each cortical area. There are no direct connections between the
thalamic nuclei.
The connection density summary of Table 2 shows that there
is great variability in the numbers of connections and the
distribution of connection weights between areas and nuclei.
Some structures (e.g. 17, SII) have a small number of dense
projections, while others (e.g. CMN, CLN, CGp and AES) have
much more widespread connectivity. In general, however, weak
connections are much more common than strong connections.
While some cortical areas and thalamic nuclei have received
vast amounts of experimental attention (e.g. 17, LG, PMLS),
some have been the focus of much less research. Further work is
required on parcellation in the posterior complex (POi, POl,
POm), the suprageniculate region (Sg, LM-Sg) and the anterior
parts of the lateral-posterior complex (LIc, LIo). Data are sparse
for cortical connections to the midline and intralaminar nuclei,
connections to and from the anterior group of nuclei, and connections to and from the thalamic regions where parcellation is
unclear. Therefore, we advise caution in interpreting the
database for these structures. Structures with very sparsely
documented connectivity can be problematic for our quantitative analyses. Consequently, POm, POl, POi, LIc and LIo
occupy peripheral or erratic locations in some of the figures.
NMDS Analyses
NMDS provided reasonable three-dimensional representations of
the connection data as the apparent fit of the configurations
derived from connection data was much better than the fit from
appropriate random test data (typically by 10 or more z-scores,
P < 10–12). The apparent fit of configurations derived from
connection data were comparable with the fit derived from
appropriate four- to six-dimensional test data (Scannell et al.,
1997; J.W. Scannell, unpublished observations).
Congruence of Different NMDS Approaches
Table 3 shows that across all input matrices the different NMDS
methods yield quantitatively similar structures (R2 varies
between 0.78 and 1.00). We have used computer animation to
visualize all the structures and the similarity of the qualitative
impression is even greater than the quantitative similarity might
suggest. In general, MDS with ‘FIT = 2’ and ALSCAL, when using
low-level ordinal data (wta, sym and raw), give configurations in
which the areas and nuclei lie in a shell around a hollow region.
This is because these scaling methods emphasize the nonconnections, or long distances (Takane et al., 1977; Young et al.,
1978), and the arrangement of points that has the most long
inter-point distances is a hollow shell (Fig. 1). In contrast, MDS
with ‘FIT = 1’ (Fig. 2) and all analyses with the wdsm1 matrix
(e.g. Fig. 1) give configurations with points more evenly
distributed within a volume of space. MDS with ‘FIT = 1’ fits the
Cerebral Cortex Apr/May 1999, V 9 N 3 283
Figure 1. Connectional relationships in the thalamo-cortical network. The figure is a stereogram showing the two least similar three-dimensional configurations yielded by the variety
of NMDS analyses of the different versions of the connection matrix. The first configuration (top) was derived using MDS on the wta wdsm1 matrix with FIT=2, and the second
(bottom) was derived using MDS on the raw matrix with FIT=1. The R2, by Procrustes’ rotation, between the configurations is 0.778. Anatomical structures within the visual system
are coloured blue, auditory structures are green, somato-motor structures are red and fronto-limbic structures are brown. CLN, CMN, PAC, PF and CM are black. The figure was
computed using all the connection data but, for clarity, only strong reciprocal connections, with a sum of weights in both directions >4, are drawn. All structures have some
connections (see Table 2). The different NMDS approaches arrange the points in different volumes of space, but the local organization within systems and the relationships between
the systems are broadly similar. The apparent badness of fit (Young et al. 1995a) for scaling the wdsm1 matrix was 0. 21, with a distance correlation of 0.94. The apparent badness
of fit for scaling the raw matrix was 0.26 with a distance correlation of 0.61. The stereograms in this paper may be free-fused to give three-dimensional images. Divergent viewing
may be easier for those used to ‘magic eye’ pictures. To view, hold the figure at arm’s length in front of the face, just below the line of sight. Look steadily at a distant object, something
outside the window, that is just visible over the top of the page, then switch your view to the page without converging the eyes. It helps if the hands are kept clear of the figures while
attempting to fuse them. One may practice by drawing two dots with a felt-tipped pen ∼4 cm apart at the very top of piece of paper. Repeat viewing the procedure above. At first,
you should see four dots at the top of the paper just below the distant object that you are fixating. If you switch your view from the distant object to the dots, the central two dots
should move towards each other and then merge when you have achieved divergent fixation. Practice with dots that are further apart until it is possible to fuse the figure. For practice,
see Johnstone (1995).
experimental disparities (connection data) to distance, so does
not emphasize non-connections. The wdsm1 transform interpolates extra ordinal ranks, converting the low-level connection
matrix into a higher-level ‘similarity of connection’ matrix. This
removes the large number of identical long distances (nonconnections) that are present in the raw data.
Figure 1 shows the two least similar NMDS configurations
(wdsm1 wta with ‘FIT=2’ and MDS raw with ‘FIT=1’, which
account for 78% of each other’s variability by Procrustes’
rotation). Despite the fact that they are the most quantitatively
dissimilar configurations, they give similar qualitative impressions of the connectional architecture of the thalamo-cortical
network.
The qualitative similarity of the NMDS configurations (Table
284 Cat Cortico-thalamic Network • Scannell et al.
3, Figs 1, 2 and 4) arises because the local relations between the
points are relatively conser ved despite the fact that the
configurations occupy different shaped volumes. For example,
Figures 1 and 2 show that the hippocampus is always distant
from the sensory periphery, that the four major cortical systems
are distinguishable, and that the visual system shows some
divergence at higher levels into structures associated with the
somato-motor system (A ES, area 7) and structures associated
with the fronto-limbic system (20b, PS). Some of the midline and
intralaminar nuclei (CLN, CMN, PAC, PF, CM) do not appear to
be closely allied with any particular system, but project widely to
a large number of cortical areas. The remaining midline and
intralaminar nuclei (RH, MV-RE, PAT, PAR A, PARP) appear to be
associated with the fronto-limbic complex.
Figure 2. Connectional relationships in the thalamo-cortical network. The figure shows the three-dimensional NMDS configuration derived using MDS with ‘FIT=2’ on the raw
matrix. Anatomical structures within the visual system are coloured blue, auditory structures are green, somato-motor structures are red and fronto-limbic structures are brown. CLN,
CMN, PAC, PF and CM are black. The figure was computed using all the connection data. The top figure illustrates only those connections where the sum of the weights in both
directions is ≥4. The lower figure shows all connections, and graphically illustrates the great complexity of the network of extrinsic connections between cortical areas and thalamic
nuclei. Stronger connections are darker grey and weaker connections are lighter grey. The apparent badness of fit of the configuration (Young et al., 1995) is 0.41 and the distance
correlation 0.61.
NMDS Representations of the Thalamo-cortical Network
The next section outlines NMDS analyses of the thalamo-cortical
network and focuses on the visual, auditory, somato-motor,
fronto-limbic and global systems, and on the midline and
intralaminar nuclei. We report only those characteristics of the
connectional architecture that appear robust and independent
of the details of the NMDS method. The NMDS configurations
here can best be regarded as provisional attempts at a systematic
map of the thalamo-cortical network. Like any early map, they
may provide a useful guide, but will also contain omissions and
distortions that may be improved with better analysis methods
and anatomical data.
Global Thalamo-cortico-cortical System
Figures 1 and 2 show that the basic connectional plan found in
the cortex, of three broadly hierarchical sensory/sensory-motor
systems and a fourth fronto-limbic system (Scannell and Young,
1993; Young, 1993), also provides a good summary description
of the thalamo-cortical network. The wdsm1 configuration in
Figure 1, for example, arranges points representing cortical
areas, and thalamic nuclei fall in a region of space that is
roughly the shape of a triangular pyramid. Three of the corners
correspond to the most topologically peripheral parts of the
sensory and sensory/motor systems, while the fourth corner
corresponds to the most topologically central parts of the frontolimbic complex; the hippocampus and associated structures.
The central region of the pyramid (Fig. 1) where the four systems
meet contains, as might be expected, those regions of cortex
where information from the different sensory modalities
converges (e.g. EPp, 7, A ES, Ia, Ig, CGp; 36).
Figure 2 shows all the documented connections of the
thalamo-cortical network and gives some indication of the
complexity of the thalamo-cortical network even at the crude
level of organization presented here. The complexity of Figure 2
shows why systematic collation and data analysis may prove
useful tools in the interpretation of primary neuroanatomical
data.
Visual System
Figures 1 and 2 illustrate the connectional architecture of the cat
Cerebral Cortex Apr/May 1999, V 9 N 3 285
thalamo-cortical visual system (blue). Figures 1 and 2 show
that the visual system has a complex, yet broadly hierarchical
organization. The periphery of the thalamo-cortical visual system
are the components of the lateral geniculate nucleus which are
in close connectional association with areas 17 and 18. Higher
are areas 19, 21a and 21b, which are connectionally close to LPl.
Higher still, there appears to be some divergence in the visual
system; PLLS and ALLS lead towards AES, 7 and 5Bl, which are in
close connectional association with the somato-motor system.
LM-Sg is closely associated with AES and Ig. In contrast, PS, 20a
and 20b lead towards 35 and 36, RS and limbic structures. Some
of these features are not apparent in Figure 2 from this particular
view, but are clearer at when the configuration is viewed from
other directions.
The connectional divergence in higher visual areas is
ref lected in a functional division into areas concerned with
motion and visuo-spatial functions (e.g. 7, AES) and those that
are involved in static object vision (e.g. 20a, 20b) (Lomber et al.,
1996a,b; Scannell et al., 1996). This arrangement is analogous
to the functional (Ungerleider and Mishkin, 1982) and
connectional (Young, 1992) divergence into somewhat distinct
object versus motion and visuo-spatial streams found in the
macaque visual system. Cats and macaques diverged from a small
insectivorous ancestor over 60 million years ago (Savage, 1986),
so the similarities in visual system organization may well ref lect
convergent evolution rather than homology. This, in turn, could
ref lect a wiring efficiency advantage in separating form and
motion vision (Jacobs and Jordan, 1992).
Auditory System
Figures 1 and 2 illustrate the connectional architecture of
the thalamo-cortical auditory system (green). The auditory
structures occupy a tight cluster, so the system appears less
hierarchical than the visual system and somato-motor systems.
MGV, MGDdd and MGDd constitute the peripher y of the
thalamo-cortical auditory system. These structures are in close
connectional association with the tonotopically organized ‘core’
fields VPc, A AF and AI (Brugge and Reale, 1985; Morel and Imig,
1987). Associated with the higher auditory area, Tem, is MGvl.
The highest stations in the auditory system appear to include
POl, SG and EPp, which are in close association with Ia, Ig, 35
and 36. MGDds tends to occupy a rather peripheral position,
which may be an artefactual consequence of the fact that it has
few documented connections.
Somato-motor System
Figures 1 and 2 show that area-to-area connection patterns define
a single somato-motor network (red) that contains both sensory
and motor structures in close connectional association. In terms
of extrinsic thalamo-cortical connection patterns, motor and
somatosensory structures form a single unit. The somato-motor
system is complex yet relatively hierarchical. VPL and VPM,
which are closely associated with the primary somatosensory
areas 1, 2, 3b and 3a, form the periphery of the somato-motor
network. These areas are very closely associated with the
primary motor cortex, area SII and some parts of area 5. VA-VL
is ‘higher’ than VPL and VPM and is closely associated with
areas 4 and 6. VMP is poised between the somato-motor and
fronto-limbic systems. Higher somato-motor areas, in particular
6l and 6m, are very closely associated with fronto-limbic
structures such as CGa, CGp, Ia and Ig. 5Bl, the most lateral part
of posterior area 5, is closely associated with visuo-motor
286 Cat Cortico-thalamic Network • Scannell et al.
structures such as AES and 7, suggesting a possible visual role for
this region.
Fronto-limbic System
We use the term ‘fronto-limbic system’ to describe a result that
we obtain when we analyse connection data with several formal
methods, namely the tendency of areas of the prefrontal cortex,
‘limbic system’ and associated thalamic nuclei to group together
(Scannell and Young, 1993; Young, 1993). Systematic and
principled analysis of connection data decides that these
structures belong together. We simply report the association and
give it a name. We are aware that the term ‘limbic system’ is
problematic (e.g. Kotter, 1992) so our use of the term
‘fronto-limbic’ does not imply any particular functional interpretation. Fronto-limbic structures include cingulate, insular,
prefrontal, perirhinal, entorhinal and retrosplenial cortex,
the subiculum, presubiculum, hippocampus, amygdala and
associated thalamic nuclei such as MD.
From Figures 1 and 2 it is clear that areas 35, 36, Ia, Ig, CGp
and CGa are connectionally interposed between the cortical
sensory and sensory-motor systems and the ‘deeper’ parts of the
fronto-limbic complex. Most distant from the sensory periphery
are ER, SB and Hipp. As would be expected, MD is in close
association with prefrontal cortical areas, and MV-RE with the
components of the hippocampal formation (Hipp, Sb, pSb).
Some of the rostral intralaminar nuclei also appear to have a
close association with components of the fronto-limbic complex,
in contrast to the caudal intralaminar nuclei (see below).
Midline and Intralaminar Nuclei
Figures 1 and 2 suggest that extrinsic connectivity defines two
broad categories of midline and intralaminar nuclei. In black
(Figs 1 and 2) are CLN, CMN and PAC (of the rostral intralaminar
group, see Appendix) and PF and CM (of the caudal intralaminar
group, Appendix 1). These nuclei connect very widely with most
areas of the visual, auditory, somato-motor and the fronto-limbic
systems. In brown, the same colour as the fronto-limbic
complex, are MV-RE, PAT, PAR A and PARP (of the caudal intralaminar group, Appendix 1). These nuclei make few connections
outside the fronto-limbic complex. This connectional difference
suggests a distinction between CLN, CMN, PAC, CM and PF on
the one hand, and RH, MV-RE, PAT, PAR A and PARP on the other.
CLN, CMN, PAC, CM and PF correspond to the ‘intralaminar
complex’ proposed by Berman and Jones (1982). These analyses
agree with Berman and Jones’ suggestion (1982) that RH, MV-RE,
PAT, PAR A and PARP do not necessarily constitute a functional
group.
Optimal Set Analysis
We can be confident that optimal set analysis found genuine sets
within the connection data because the low costs of the solutions when compared with solutions computed from randomly
shuff led connection matrices. For example, at balanced
attraction and repulsion (Figs 8 and 9) with real connection data,
the mean (± SEM) cost of the first 20 generations of the first
epoch was 1861 15. The mean of the equivalent cost calculated
from five randomly shuff led connection matrices was 2729, with
a SD of 42. Therefore, in this case, the cost of sets calculated
from connection data was ∼20 z-scores less than the mean cost of
sets calculated with random data (P < 10–12). In general, the costs
of solutions computed from connection data were broadly
comparable with the costs of solutions computed from four- to
Figure 3. Optimal set analysis at balanced attraction and repulsion. The figure summarizes the 31 solutions whose costs lie within 1% of the single lowest cost solution. Attraction
(the factor multiplied by connection weight to compute the cost of connections between sets) was 1 and repulsion (the cost of non-connections within sets) was 1. Cortical areas
and thalamic nuclei are listed along the top and left of the table. The frequency with which areas or nuclei co-localized within the same set is given by the colour (scale at top right
of table). Darker colours show frequent co-localization, while light colours show rare co-localization. The analysis consistently finds a number of sets. From top to bottom of Figure 3,
these are: a set of frontolimbic, multimodal and higher-order somato-motor structures (LM-Sg to PFCMil); a set of auditory structures (MGM to MGDd); a set of lower-order
somato-motor structures (4g to 3b); two small fronto-limbic sets (Sb to Hipp, and pSb to RS); and a large visual set (LPl to PMLS). Some structures do not have a strong affiliation
with any of the large sets, either because of few known connections (e.g. LGLGW) or because of strong connections with more than one set (e.g. 7 and ALLS). The number of sets
changes substantially when the attraction and repulsion parameters are at different values.
Cerebral Cortex Apr/May 1999, V 9 N 3 287
Figure 4. Optimal set analysis and NMDS are in excellent agreement. The figure is a stereogram that shows the major sets identified by optimal set analysis (Fig. 3) colour-coded
and plotted on three-dimensional MDS configurations (Fig. 1). The set of fronto-limbic, multimodal, and higher-order somato-motor structures (LM-Sg to PFCMil in Fig. 3) is coloured
brown; the set of auditory structures (MGM to MGDd in Fig. 3) is coloured green; the set of lower order somato-motor structures (4g to 3b in Fig. 3) is coloured red; The ‘hippocampal’
set (Sb to Hipp in Fig. 3) is coloured purple; the ‘anterior nuclei’ set (LD to RS in Fig. 3) is coloured blue/green; and the visual set (LPl to PMLS in Fig. 3) is coloured blue. The structures
outside these sets are black. The figure was computed using all the connection data but, for clarity, only strong reciprocal connections, with a sum of weights in both directions >4,
are drawn. The spatial configurations of points are exactly as in Figure 1. The sets identified by optimal set analysis map neatly onto compact regions of space identified by NMDS.
Furthermore, the unclassified (black) structures tend to occupy peripheral regions, or are near the borders between the sets. This shows that NMDS and optimal set analysis agree
over the major features of the connectional organization of the thalamo-cortical network.
six-dimensional test data (Scannell et al., 1997; J.W. Scannell,
unpublished observations).
We performed optimal set analysis with attraction weights
ranging from 1 to 10, with repulsion at 1, and with repulsion
weights ranging from 2 to 30, with attraction at 1. For each of
the levels of attraction and repulsion, we ran CANTOR for 50
epochs from 50 different random starting configurations. At any
given level of attraction and repulsion, optimal set analysis tends
to produce a lot of solutions with similar low costs and there is
no principled way of choosing which is ‘best’.
Figure 3 shows a summary of the 31 solutions we found with
costs within 1% of the single lowest-cost solution at balanced
attraction and repulsion. The balanced attraction and repulsion
case is the most intuitively obvious. In this condition, the cost of
a connection between sets is equal to the weight of the
connection (1, 2 or 3) and the cost of a non-connection within
sets is equal to 1. The difference between the 31 solutions is
small and all agree on several clear and consistent sets (solid dark
288 Cat Cortico-thalamic Network • Scannell et al.
blocks in Fig. 3). From top to bottom of Figure 3, these
correspond to a set containing higher somato-motor and some
multimodal and fronto-limbic structures (LM-Sg to PFCMil); an
auditory set (MGM to MGDd); a set of lower somato-motor
structures (4g to 3b); two small sets of frontolimbic structures
(one consisting of Hipp, Sb and MV-Re, and the other containing
pSb, LD, AD, AV and RH); and a large set of visual structures (LPl
to PS). Around 17 areas or nuclei have variable positions and do
not consistently lie in any of these sets. Some of these structures
have few connections, so are ‘pushed’ out of sets by the repulsive
effect of many non-connections (e.g. LGLA, LGLC). Other areas
and nuclei may have substantial connections with structures in
more than one of the major sets (e.g. 7, CMN, 36, Enr, POm, SG),
so fit into different sets with very little effect on the overall cost
of the configuration.
Optimal set analysis is in excellent agreement with NMDS
over the main features of the connectional architecture of the
cat thalamo-cortico-cortical network. Figure 4 plots the major
Figure 5. Summary of optimal set analysis with the attraction parameter ranging from 1 to 10 (repulsion set to 1), and repulsion parameter ranging from 2 to 30 (attraction set to
1). Cortical areas and thalamic nuclei are listed along the top and left of the table. The frequency with which areas or nuclei co-localized within the same set is given by the colour
(scale at top right of table). Darker colours show frequent co-localization within the same set, while light colours show rare co-localization.
Cerebral Cortex Apr/May 1999, V 9 N 3 289
Figure 6. Cluster structure of the cat thalamo-cortical network. The figure shows the relative frequency with which cortical areas occupied the same cluster in 10 clustering
paradigms applied to each of six alternative five-dimensional NMDS configurations (see Materials and Methods). Pooling results from a wide range of clustering and NMDS methods
aimed to average out the peculiarities of individual analysis methods, revealing robust connectional features. Dark squares represent areas that frequently occupied the same cluster,
while light squares represent areas that are rarely occupied the same cluster (key on right of figure). The names of anatomical structures are listed down the left margin and across
the top of the matrix. To ease comparison with Figure 3, the gross order of areas is comparable to that in Figure 3, although the order of areas has been changed locally (within the
basic ‘sets’ identified in Fig. 3) to illustrate additional features.
290 Cat Cortico-thalamic Network • Scannell et al.
sets illustrated in Figure 3 on a stereogram showing threedimensional configurations yielded by NMDS. Figure 4 shows
that the sets (Fig. 3) map neatly and compactly onto the
three-dimensional NMDS configurations with little stretching,
fracturing or overlap between the sets. It is clear that most of the
unclassified structures lie either at the edge of the NMDS
configurations (those with few connections), or else lie near the
borders between the sets (those with substantial connections
with more than one set). Thus there is excellent agreement
between two entirely independent data analysis methods.
Over the full range of attraction and repulsion parameters,
high attraction gave fewer, larger, clusters, identifying higherorder associations between the major thalamo-cortical systems.
High repulsion gave more, smaller, clusters, identifying the most
closely associated areas or nuclei. Figure 5 shows the pooled
results from the battery of attraction and repulsion levels.
Figures 5 and 3 are in good agreement, but the graded nature of
Figure 5 reveals both the local and the large-scale structure of the
thalamo-cortical network and allows one to look across levels
of organization. For example, the darkest squares show areas or
nuclei that ver y frequently co-localize across all levels of
repulsion so are therefore very strongly associated. The lightest
squares show areas or nuclei that very rarely co-localize, even
at the highest levels of attraction, so are therefore very connectionally distant.
In line with the balanced attraction and repulsion case (Figs 3
and 4), the somato-motor system is closely associated with
fronto-limbic multimodal cortical areas. The first large block of
structures in Figure 5 (6l to MV-Re) contains fronto-limbic,
somato-motor and multimodal structures. Figure 5 confirms that
the auditor y system (MGV to A II) is the most topologically
isolated of the thalamo-cortical systems, although MGV and Tem
occasionally co-localize with fronto-limbic and visual structures.
Within the visual system (LGLA to LGLGW), ALLS and PLLS
sometimes co-localize with AES and IG, and LM-Sg. 5Bl is closely
associated with many visual structures.
A feature of optimal set analysis applied to this data set is that
areas or nuclei with few reported connections often end up in
sets on their own (e.g. LIc, Hipp). Some areas or nuclei have few
reported connections because they genuinely make few, possibly
very strong, connections (e.g. LGLA, LGLC). Others have few
reported connections because of lack of study or lack of
consensus over their existence or classification (possibly LIc, and
PF). These aspects of the analyses would benefit from more
experiments on the less studied areas and from quantitative
connection data for all areas. Quantitative data, which would
provide a more accurate cost function, is provided by some
researchers (e.g. Sherk, 1986; Musil and Olson, 1991; Peters et
al., 1993, 1994; MacNeil et al., 1997; Van Duffel et al., 1997), but
is unavailable for the vast majority of connections reported in the
neuroanatomical literature.
radii kernels and the local structure has been revealed by the
analyses with small radii kernels.
The ability of the analysis to look across different scales means
that Figure 6 contains a wealth of information that cannot all be
explored in detail here. However, we outline several example
features to help the reader to explore the Figure. First, the region
corresponding to the first set from Figure 3 (containing higher
somato-motor and some multimodal and fronto-limbic structures) is clearly not homogenous. The areas of prefrontal cortex
are much more closely associated with each other than, for
example, with VA-VL, 6m, 6l and 7. Second, VA-VL, 6m, 6l, 7, 5Al
and 5Bl have associations with structures that lie within the ‘low
somatomotor’ set of Figure 3, namely 4g, 4, 5Am, SSAi, SSAo and
5Bm. Third, the region of Figure 6 corresponding to the visual
set of Figure 3 also shows considerable substructure. For
example, there are particularly close connectional associations
between 17 and 18, and between DLS, LPm and PLLS. These
three structures also have associations outside the visual set,
with AES and ALLS.
Figure 6 shows several distinct and robust small connectional
groups that are relatively invariant with NMDS method and
clustering paradigm (darkest regions in Fig. 6). These groups
must contain structures that are in very close connectional
association since they are consistently identified in different
NMDS configurations even by clustering methods with small
kernels. From top to bottom of Figure 6, particularly tight
subgroups include PFCMil, PFCMd and PFCL; CGa and CGp; 6l
and 6m; 5Al, 5Bl and 7; AES, SIV and ALLS; 35 and 36; A AF and
AI; Tem and MGvl; a low somato-motor group including 1, 2, 3a,
3b, SII, and VPL and VPM; Sb and MV-Re; areas 17 and 18; and
LPm and PLLS.
An alternative and informative reading of Figure 6 is to
concentrate on the very light regions which show those groups
of areas that are least likely to occur within the same clusters.
The unimodal visual structures (LGLGW to PLLS) are very distant
from most auditory structures (MGV to MGDd), and from the
early somato-motor structures (4g to SII). The early somatomotor structures (4g to SII) are very distant from the early
auditor y system (MGV to MGDd). A ll the early sensory and
sensory-motor groups are more distant to each other than they
are to the fronto-limbic and higher order sensory-motor regions
(e.g. PFCL to 36, and PAT to RS). This is consistent with the
notion that, at a gross level of description, the thalamo-cortical
network is organized into three distinct sensory or sensory
motor systems and a topologically central group of structures. In
this analysis, the topologically central group of structures
includes fronto-limbic structures, some higher somato-motor
(e.g. 6l, 5m) and possibly multimodal sensory structures (e.g.
AES, 7).
Cluster Analysis
Figure 6 shows the pooled results from the battery of nonparametric cluster analysis methods. The areas in Figure 6 have
been ordered so that the sets are comparable to Figure 3. It is
clear that the cluster analysis methods are in good agreement
with optimal set analysis (Figs 3 and 5). As Figure 6 shows pooled
results from a range of NMDS methods, and a range of clustering
paradigms with a widely differing kernel radii, it has a graded
nature that reveals considerable detail about both the local and
the large-scale structure of the thalamo-cortical network.
Large-scale structure has been identified by analyses using large
A New Approach
This paper presents an approach to the analysis of anatomical
connection data that is still relatively new and that frequently
invites questions. First, can one collate brain connectivity data
from many different studies on many different individuals?
Second, is it practical to produce simple representations of such
complicated data? Third, what are the advantages and
disadvantages of the different analysis methods? Fourth, what are
the limitations of the ‘grey box’ level of explanation (Douglas
and Martin, 1991) that considers cortical areas, thalamic nuclei
and extrinsic connections as the basic units of organization?
Discussion
Cerebral Cortex Apr/May 1999, V 9 N 3 291
Collation and Variability
There are several reasons to collate anatomical connection data.
First, collation may highlight gaps in current knowledge (e.g.
cortical connections to the midline and intralaminar nuclei, see
Table 2 and Results section; see also Hilgetag et al., 1996).
Second, it may direct further empirical study (e.g. Scannell et al.,
1996). Third, collation provides the opportunity to test theories
of neuroanatomical organization with a degree of quantitative
rigor (e.g. Cherniak, 1990; Nicolelis et al., 1990; Musil and
Olson, 1991; Young, 1992; Hilgetag et al., 1996; Scannell, 1997).
Fourth, primary connection data are distributed across a large
and, to the non-specialist, difficult literature. Collation can allow
a wide range of researchers to benefit from advances in
experimental neuroanatomy.
Despite these advantages, collation is complicated by variability in anatomists’ parcellation schemes, connection-tracing
methods, and by variability in the results of equivalent tracer
deposits in different animals. Variability has been highlighted in
recent quantitative studies and is substantial both in anatomical
structures (e.g. Rajkowska and Goldman-Rakic, 1995; Andrews
et al., 1997), and in the density of connections between them
(MacNeil et al., 1997). MacNeil et al. (1997) have counted the
number of labelled thalamic and cortical neurons following
injections in the medial suprasylvian visual area. They found that
visual cortico-cortical connection densities vary widely between
tracer deposits. At first sight, high variability (whether within or
between individuals) seems problematic for much of connectional neuroanatomy, and particularly so for studies such as this
one.
MacNeil et al.’s (1997) results have prompted us to examine
quantitative data from Musil and Olson (1988), Bowman and
Olson (1988), Olson and Jeffers (1987), Olson and Lawler
(1987), Olson and Musil (1992), and Musil and Olson (1991),
and unpublished quantitative connection data on the lateral
suprasylvian areas (S. Grant, personal communication). The data
suggest that for a wide range of cortico-cortical and thalamocortical connections, the distribution of connection densities
across equivalent experiments in different individuals is highly
non-normal. In fact, the distribution is closer to an exponential
(R.J. Baddeley and J.W. Scannell, unpublished observations).
This has important implications. First, statistical methods based
on the assumption of a normal distribution are not appropriate
for the analysis of such data. Second, the exponential distribution is extremely variable, so large amounts of data are required
for confident estimates of sample statistics. Third, the standard
measure of variability (sample SD) can show a relatively strong
bias when applied to exponentially distributed data. For small
sample sizes, the variability will be systematically underestimated. Table 4 (R.J. Baddeley and J.W. Scannell, unpublished
observations) provides a guide to confidence inter vals for
estimates of mean and SD connection density. It also provides
a guide to correcting the bias in sample SD. By providing
confidence inter vals, a simple indicator of statistical power,
Table 4 should help researchers to choose appropriate sample
sizes in connection tracing experiments.
Table 4 shows that small samples (five or fewer individuals)
have very wide confidence intervals, so are likely to suffer from
serious random sampling error. The benefits of increasing
sample size begin to tail off with sample sizes of 10 or more
individuals (Table 4). This suggests that studies of the connections to a particular cortical area should aim to inject the area
in ∼10 individuals if reasonable quantitative (or qualitative)
estimates of connection densities are required.
292 Cat Cortico-thalamic Network • Scannell et al.
Table 4
Guide to sampling in quantitative connection tracing experiments
N
(A) Mean
(B) Standard deviation
Upper
2
4
6
8
10
12
14
16
18
20
Lower
Upper
95%
70%
70%
95%
95%
70%
2.83
2.20
1.93
1.80
1.69
1.63
1.58
1.54
1.50
1.48
1.70
1.50
1.43
1.37
1.31
1.30
1.27
1.26
1.25
1.24
0.54
0.69
0.75
0.79
0.81
0.83
0.85
0.86
0.87
0.87
0.12
0.28
0.37
0.44
0.49
0.52
0.55
0.57
0.59
0.61
2.59
2.24
2.04
1.89
1.82
1.79
1.74
1.68
1.66
1.62
1.36
1.34
1.33
1.32
1.30
1.29
1.27
1.25
1.24
1.24
Bias
Lower
70%
95%
0.71
0.84
0.88
0.90
0.92
0.93
0.94
0.95
0.95
0.95
0.25
0.50
0.59
0.65
0.69
0.72
0.75
0.76
0.77
0.79
0.02
0.15
0.26
0.31
0.37
0.40
0.43
0.46
0.49
0.51
The table provides a guide to sampling from the exponential distribution, which bears a closer
resemblance to the quantitative distribution of connection densities than does the normal
distribution (R.J. Baddeley and J.W. Scannell, unpublished observations). The left column, N,
shows sample size (the number of individuals with tracer deposits). 70% confidence limits are
analogous to standard error. (A) Mean. Columns show the ratio of the upper and lower 95% and
70% confidence limits of the population mean to the sample mean. These are proportional to
sample mean for data from an exponential distribution. Therefore, the intervals for any given
sample can be calculated by multiplying sample mean by the appropriate confidence limits. For
example, with a sample size of six and a sample mean connection density of 1000 labelled
neurons, the 95% confidence interval for the population mean ranges from 370 to 1930 labelled
neurons. The 70% confidence interval ranges from 750 to 1430 neurons. (B) Standard deviation.
Columns show the ratio of the upper and lower 95% and 70% confidence limits of population SD
to uncorrected sample SD. The ‘bias’ column shows the expected ratio of sample SD to population
SD. Unbiased estimates of population standard deviation may be calculated by dividing sample
standard deviation by the appropriate bias. For example, with a sample size of four and a sample
SD of 1000 neurons, the unbiased estimate of population SD is 1192 (which equals 1000/0.84),
and the 95% confidence interval ranges from 150 to 2240 neurons.
Many primary studies of cortico-cortical and cortico-thalamic
connections are based on substantially fewer than 10 cases, so
risk serious random sampling error. However, where several
primary studies have examined the same connection, collation is
likely to reduce the problem of random sampling error in the
estimation of connection density. This gives some justification to
those who have made serious attempts to distil data from many
neuroanatomy papers into a single coherent and analysable form
(e.g. Pandya and Yeterian, 1985; Felleman and Van Essen, 1991).
However, with high variability, presenting any single connectional scheme as representative becomes difficult. This is
because most individuals depart considerably from the ‘average’
pattern of connection densities. Indeed, no single individual can
have a pattern of connection densities that is very representative
of the others. To ref lect this genuine aspect of connection data,
the nature of the distribution of connection densities should be
quantified (Cherniak, 1990), reported and represented in future
attempts at collation and modelling (MacNeil, 1997).
Complexity
The second question, that connection data are too complicated
to summarize, is serious and plausible, but, our data suggest,
untrue. One can imagine a brain in which the pattern of
area-to-area connections was so complex that it could be
described by nothing simpler than the connection matrix itself.
Putting random numbers in Table 2 would simulate the
complexity of connectivity of such a brain. Fortunately, the
goodness of fit of the NMDS configurations and the low cost of
the CANTOR solutions, when compared with analyses of test
data, show that the connectional architecture of the real
thalamo-cortical network can be well represented relatively
simply. This finding is important. It suggests that, in principle,
relatively few sources of variability can account for much of the
variability in the gross connection pattern of the thalamo-cortical
network. We think this apparent simplicity shows that a small
number of simple rules, ref lecting genetic and/or developmental
economy, have been favoured by evolution in the design of the
thalamo-cortical network.
Analysis Methods
Many anatomists have sought to present complex connection
data in a compact and comprehensible way. McCulloch (1944)
provides an excellent example. However, the complexity of
modern data make the problem considerable (Nicolelis et al.,
1990; Felleman and Van Essen, 1991). While our methods make
a contribution, none are an ideal solution.
NMDS works with ordinal data to give graphical representations of connectivity. However, there are many possible
NMDS algorithms and cost functions and it is not always clear
which, if any, is best. We are also limited to presenting
configurations in no more than three dimensions. We think this
acceptable for the thalamo-cortical data in this paper, but it may
not be appropriate for other anatomical data. Data that cannot be
represented by a low-dimensional spatial arrangement of points
(e.g. data with many one way connections, where the proximity
of A to B does not equal the proximity of B to A) are unsuitable
for graphical presentation with NMDS.
Optimal set analysis avoids dimensional reduction and allows
us to specify particular cost functions; in this case, functions
that embody an attractive definition of a neural system. Varying
the cost function allows us to look for both local and global
features of neural organization. However, the method tends to
produce a very large number of optimal solutions that then have
to be summarized (e.g. Hilgetag et al., 1996). Sometimes the
summaries are simple (e.g. Fig. 3), but they may be nearly as
complex as the connection matrix itself (e.g. Fig. 5). In addition,
our cost function assumes a metric relationship between
connection densities, which is a disadvantage when compared
with NMDS.
Non-parametric cluster analysis compromises between NMDS
and optimal set analysis. It balances the need to avoid dimensional reduction with the need to avoid the problems of density
estimation in high-dimensional spaces (Silvermann, 1986; Burns,
1997) by using five-dimensional NMDS configurations as a
starting point. The method can look for both local and global
organization. However, the method is less transparent than
either NMDS or optimal set analysis and we have to perform
many different cluster analyses and pool the pool the results, so
that spurious clusters (Gordon, 1996) are averaged out. This
makes the summary data complicated.
Despite the limitations of the different methods, we are
encouraged by the fact that they yield largely consistent results.
We hope they provide relatively comprehensible working
models which can, at best, serve as rough ‘route planners’ to the
large-scale organization of the thalamo-cortical network.
Levels of Analysis
The final question relates to the level of analysis presented in this
paper. Is it useful to pursue the somewhat abstract level of
analysis in which the cortical area, thalamic nucleus and
extrinsic connection are the basic units?
Our choice of level of analysis has been pragmatic and
utilitarian, seeking the greatest number of connections for
the greatest number of structures. We have tried to ref lect the
cortical areas and thalamic nuclei of the cat that have been
established by other authors and for which there are connection
data. However, the distinctions we have drawn are not necessarily the most principled. For example, we have subdivided the
LGN into several subnuclei on the basis of morphological,
connectional and physiological differences between neuronal
populations, but have treated area 17 as a single unit. However,
each layer of area 17 has different extrinsic connections,
physiological properties and morphology. It may also be the case
that cortical areas and thalamic nuclei are not an appropriate
level of organization for our kind of analysis of connectivity. For
example, in the early visual system, parts of different areas
representing similar regions of retinotopic space may be much
more functionally and connectionally associated than parts of
the same area representing different regions of space.
We see no easy answer to these questions. However, in the
long term, quantitative data on connectivity, morphology and
physiology are necessary to determine the most natural
parcellation schemes and levels of analysis.
Indeterminate Hierarchies and Directed Loops
Crick and Koch (1998) suggest that directed feed-forward loops
should not exist within the extrinsic thalamo-cortico-cortical
network, as they would cause runaway excitation (Crick and
Koch, 1998). They also suggest that by incorporating thalamocortical connections, ‘indeterminate’ hierarchies (Hilgetag et al.,
1996) might be avoided.
If the direction of connections is only known at the
categorical level of measurement (‘ascending’, descending’ or
‘lateral’), thalamo-cortical connections cannot remove indeterminacy from hierarchical analysis. Cortical hierarchies (e.g.
Felleman and Van Essen, 1991) are indeterminate (Hilgetag et al.,
1996) because connectivity is too sparse to constrain a unique
hierarchy, even if the direction of every cortical connection
were known. Indeterminacy will not go away if thalamo-cortical
connections are included. This is because the cortico-thalamocortical loops that could, in principle, provide additional
hierarchical information do, in fact, only link cortical areas that
already have direct cortical projections (Scannell et al., 1997).
The exceptions to this rule are connections to and from the
midline or intralaminar nuclei (Scannell et al., 1997). The lack of
directional thalamic ‘shortcuts’ between areas without direct
cortical connections means that thalamic connections cannot
provide the additional information necessary remove indeterminacy from sensory-motor hierarchies (cf. Crick and Koch,
1998). Indeterminacy may disappear if it proves possible to
obtain metric measures of the direction of cortical connections,
as suggested by recent work of Barbas and Rempel-Clower
(1997).
Symmonds and Rosenquist (1984) and Scannell et al. (1995)
noted that the origin and termination patterns of connections
between cat cortical areas frequently do not match the ‘feedforward’, ‘feed-back’ and ‘lateral’ templates that have been
applied in the primate (e.g. Felleman and Van Essen, 1991; Crick
and Koch, 1998). For example, cat area 17 sends projections that
originate predominantly in the layers II and III (Rosenquist,
1985) and terminate predominantly in layers II and III of areas 18
and 19 (Price and Zumbroich, 1989). The projection from area
18 to 17 originates in layers II, III and V and terminates in layers
I, II, III, IVA, V and superficial layer IV of area 17, while tending
to avoiding deep layer VI and layer IVB (Henry et al., 1991). At
present, the functional implications of the differences in laminar
origin and termination patterns in the cat and macaque cortices
are unclear. However, the fact that cats are frequently used for
studies of cortical microcircuitry, while macaques are favoured
Cerebral Cortex Apr/May 1999, V 9 N 3 293
for systems neuroscience, means that these differences require
further critical evaluation.
Structure–Function Relationships
Connectional neuroanatomy is driven by the idea that there is a
close link between brain connectivity and brain function (e.g.
Meynert, 1890). Van Duffel et al. (1997) have recently explored
this relationship in an ingenious experiment which correlated
quantitative measures of anatomic and metabolic connectivity.
However, we believe that the efforts of anatomists in finding and
describing connections have not been efficiently exploited to
explore the structure–function relationship. This is because
functional arguments based on extrinsic connectivity have
tended to be informal, non-quantitative and/or highly simplistic.
Recently, Hilgetag et al. (1997a) have developed more formal
network models, whose connectivity is based on the pattern of
extrinsic cortical connections collated by Scannell et al. (1995),
Young (1993) and others. These models provide excellent
quantitative accounts of the spread of electrical activity found in
old strychnine neuronographic experiments. It may be possible
to extended such methods to explore the functional consequences of cortical lesions. Since the properties of networks
may not be intuitively obvious, and since neuronal activity can
spread via direct and indirect connections, simple models
relying only on the strength of direct connections (e.g. Van
Duffel et al., 1997) may not provide a realistic account of
functional connectivity within the thalamo-cortical network.
Efficient Wiring
It has been suggested (Cherniak, 1992, 1994, 1995, 1996) that
the organization of the central nervous system can be explained
if ‘saving wire’ is the primary and overriding design constraint.
While efficient wiring has long been thought to contribute
to some aspects of brain organization (e.g. Cowey, 1984;
Mitchison, 1991; Young, 1992; Cherniak, 1994; Scannell et al.,
1995; Scannell, 1997; Van Essen, 1997), Young and Scannell
(1996) have argued that many other constraints are also
important. If saving wire were the overriding design constraint,
connectional proximity should map very neatly onto physical
proximity. Component placement optimization, therefore,
predicts a physical brain organization that resembles Figure 1. To
‘save wire’, thalamic nuclei should dissociate from their
neighbours, with which they do not connect, and migrate to be
close to the cortical areas with which they do connect. However,
the thalamic nuclei remain firmly clustered together near the
centre of the cerebral hemisphere. This may ref lect developmental and evolutionary constraints. The thalamus was well
developed in reptiles, long before the origin of neocortex
(Masterton, 1976). Furthermore, the development of the
thalamus and thalamo-cortical projections precedes that of the
cortical plate and cortico-cortical projections (O’Leary et al.,
1994; Payne et al., 1988). Therefore, constraints other than the
need to save wire may strongly constrain the organization of the
central nervous system (Young and Scannell, 1996; Scannell,
1997).
Global Architecture
Young (1993), Scannell and Young (1993) and Young et al.
(1995a,b) found that the extrinsic connectional architecture of
the cortico-cortical network of macaques and cats defined four
major systems. There were the visual, auditory and somatomotor systems and a fourth system, termed the ‘fronto-limbic’
system, composed of prefrontal cortex and structures in the
294 Cat Cortico-thalamic Network • Scannell et al.
medial part of the hemisphere (sometimes termed ‘limbic’
cortex). The sensory-motor systems all showed a degree of
hierarchical organization and their highest stations were in close
connectional association with the fronto-limbic complex that
therefore has access to the most highly processed sensory and
motor information. There was more limited cross-talk between
the systems outside the fronto-limbic complex, but this differed
between the cat and macaque.
Our results show that a similar plan holds for the thalamocortico-cortical network. Different analysis methods consistently
detect three groups of areas corresponding to the unimodal
components of the visual, auditory and somato-motor systems.
The higher stations of these systems are associated with a
topologically central group of structures (insular, cingulate and
prefrontal cortex, associated nuclei and, depending on analysis
method, the higher stations of the somato-motor system and
multimodal cortical areas). The analyses also identify a final
group of structures (e.g. Hipp, MV-Re, Sb, pSb, AM, AD, Enr),
that are at the furthest possible remove from the sensory-motor
periphery. This basic plan is a robust and reproducible feature of
three independent analyses of a comprehensive body of connection data. Therefore, it should represent a strong constraint
on large-scale theories of the organization of the brain.
Notes
This work was supported by the Wellcome Trust.
Address correspondence to J.W. Scannell, Neural Systems Group,
Psychology Department, Ridley Building, University of Newcastle,
Newcastle upon Tyne NE1 7RU, UK. Email: [email protected].
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Appendix
Parcellation of the cat thalamus
Thalamic division
Be&Jo 1982
Abbr.
Medial geniculate complex
Ventral division of the MG
MGP
MGV
5.4
10.0
0.5
MGDdd
4.5
4.5
9.0
9.0
2.0
1.0
MGDd
4.5
9.0
2.0
MGDds
MGvl
MGM
4.5
4.8
6.4
9.5
9.5
7.5
2.2
–1.0
2.0
6.0
6.0
6.0
7.2
6.4
9.5
9.5
9.5
7.5
7.7
6.0
5.3
4.7
7.0
5.2
includes laminae A and A1 (e.g. Dr86; Ro85)
the Magno C laminae (Ro85; Dr86)
The Parvo C laminae (Ro85; Dr86)
an extension of the Parvo C laminae onto the surface of the Pulvinar (Ra83; Gu80)
equivalent to MIN (Ro85; Dr86)
8.0
8.3
6.4
6.0
5.5
8.0
5.5
8.0
4.0
3.0
3.5
3.5
equivalent to POm of Ba91 and Bu&Ko84b
equivalent to Po of Mo&Im87 and Po of Wi92
equivalent to NS (Ca83; Ca&Ai83); partially overlaps with LM-Sg
equivalent to PoI (Cl&Ir90; Ro&Re83) and POI (Gu&Ma84)
Dorsal division of the MG
Deep dorsal nucleus
MGD
Dorsal nucleus
Superficial dorsal nucleus
Ventrolateral nucleus
Magnocellular medial geniculate nucleus
MGM
Dorsal lateral geniculate complex
Laminar dorsal lateral geniculate nucleus
A laminae
C lamina
C1-3 laminae
Geniculate wing
Medial interlaminar nucleus
LG
LGL
LGI
LGLA
LGLC
LGLC1–3
LGLGW
LGLI
Posterior complex
Medial region
Lateral region
Suprageniculate nucleus
Intermediate region
Po
POM
POL
SGN
POI
POm
POl
SG
POi
298 Cat Cortico-thalamic Network • Scannell et al.
A-P
M-L
D-V
Notes
equivalent to LV + OV (Mo64;Ca83; Ca&Ai83), Vl + Vo (An80), V (Mo&Im57), VD (Wi&Mo83a),
CGMv (Wi&Mo83b; Wi92); also includes ‘d’: dorsal cap nucleus of Mo&Im87
equivalent to CGMd (Wi&Mo83b; Wi92)
equivalent to DP (Mo64), Dd (An80; Ca83; Calford and Aitkin, 1983; Wi92), DD (Wi&Mo83a,b;
Wi92)
equivalent to D (Mo64; Wi&Mo83a,b; Wi92) and part of Dc (An80) and DC (Ca83; Ca&Ai83);
includes ‘cd’: caudodorsl nuleus of Mo&Im87
equivalent to DS (Mo64; Wi&Mo83a,b; Wi92), part of Dc (An80) and DC (Ca83; Ca&Ai83)
eqivalent to VL (Mo64; An80;Wi&Mo83a,b; Ca83; Ca&Ai83; Wi92), vl (Mo&Im87)
Appendix – continued
Thalamic divsion
Be&Jo 1982
Abbr.
Lateral nuclei
Lateral dorsal nuclei
Lateral posterior nucleus
Lateral division of LP
LD
LP
LD
9.5
3.5
9.3
equivalent to LD (Av90; Av85)
LPl
6.8
5.0
8.0
LPm
LM-Sg
PUL
6.8
6.4
7.2
7.2
4.5
4.5
6.5
7.5
7.5
5.0
8.0
5.5
equivalent to the ‘corticorecipient zone’ of Dr85 and LPl and PNR of Gr&Be80; corresponds to
postero-lateral part of LPl of Up83
equivalent to the ‘tectorecipient zone’ of Dr85, to LPm of Gr&Be80 and to LPi of Up83
8.3
9.5
9.1
4.0
5.5
2.0
8.0
7.0
4.5
9.1
9.1
7.0
5.5
3.5
4.0
Medial division of LP
Lateral medial and suprageniculate complex
Pulvinar
PUL
Posterior nucleus of Rioch
PNR
Intermediate nucleus of the lateral group
Caudal part of LI
Oral part of LI
Mediodorsal nucleus
MD
Ventroposterior complex
Lateral part of VP (spinothalamic)
Medial part of VP(trigeminal)
External part of VP
Ventroanterior-ventrolateral nucleus
Ventromedial complex
Principal ventromedial nucleus
Basal ventromedial nucleus
Submedial nucleus
Rostral intralaminar nuclei
Central medial nucleus
Paracentral nucleus
Central lateral nucleus
Caudal intralaminar nuclei
Centre median nucleus
Parafasicular nucleus
Rhomboid nucleus
Parataenial nucleus
Anterior paraventricular nucleus
Posterior paraventricular nucleus
Medioventral-reuniens nucleus
Anterior complex
Anterodorsal nucleus
Anteroventral nucleus
Anteromedial nucleus
VB
VBX
VBA
LI
LIc
LIo
MD
A-P
M-L
D-V
VA&VL
VPL
VPM
VPX
VPo
VPI
VPS
VA-VL
11.8
5.5
5.0
VMP
VMB
SUM
VMP
VMB
SUM
10.5
8.3
10.6
2.0
3.5
1.0
3.0
2.0
3.5
CMN
PAC
CLN
CMN
PAC
CLN
10.2
9.5
10.2
0.0
2.0
3.5
4.0
4.1
6.0
CM
PF
MV
CM
PF
RH
PAT
PARA
PARP
MV-RE
7.2
8.0
11.0
12.0
11.8
9.5
12.0
2.7
1.5
0.0
1.0
0.5
0.5
0.0
4.2
3.5
4.8
5.5
6.0
5.8
2.0
AD
AV
AM
AD
AV
AM
12.0
12.0
12.0
2.0
3.0
1.3
6.0
6.5
3.8
Notes
equivalent to ‘pretectorecipient zone’ of Dr85 and the pulvinar (Up83; Gr80)
NP of Gr&Be80 is connectionally similar to LPl, with which it has been lumped in this study
LI of Gr&Be80 corresponds to the rostral regions of LPl or Up83
LIc (Gr&Be80; Av85); rostral part of LPl of Up83
LIo (Gr&Be80; Av85); rostral part of LPl of Up83
equivalent to VPL (Bu&Ko84b; Zh86; Ba91); includes VPL of Dy86
equivalent to VPM (Bu&Ko84b; Zh86; Ba91); includes VPM of Dy86
VPO of Dy86: fine-grained division that has not been demonstrated connectionally in other studies
VPI of Dy86: fine-grained division that has not been demonstrated connectionally in other studies
VPS of Dy86: fine-grained division that has not been demonstrated connectionally in other studies
equivalent to nucleus ventralis medialis pars parvocellularis (e.g. Ru72)
known alternatively as the reuniens or the medioventral nucleus
The first column of the table shows the major thalamic divisions and subdivisions. The second column (Be&Jo, 1982) gives the nomenclature used in the Berman and Jones (1982) atlas. The third column
shows the nomenclature used in this paper. Where possible, we have used the same names as Berman and Jones (1982). Nuclei shown in bold typeface in the ‘Abbr.’ column had sufficient connection data
to be included in the quantitative analyses. To help identify our parcellation and its relation to other schemes, the stereotaxic coordinates of the approximate centres of the nuclei, taken from the frontal
sections of Berman and Jones (1982), are also given. ‘A-P’ corresponds to anteroposterior coordinate, ‘M-L’ to mediolateral coordinate, and ‘D-V’ to dorsoventral coordinate. The final column of the table
provides notes on alternative nomenclature and parcellation. The references are as follows: An80 = Andersen et al. (1980); Av85 = Avendano et al. (1985); Av90 = Avendano et al. (1990); Ba81 =
Barbaresi et al. (1981); Bu&Ko84b = Burton and Kopf (1984b); Ca&Ai83 = Calford and Aitkin (1983); Ca83 = Calford (1983); Cl&Ir90 = Clarey and Irvine (1990); Dr86 = Dreher (1986); Dy86 = Dykes et
al. (1986); Gr&Be80 = Graybiel and Berson (1980); Gu&Ma84 = Guildin and Markowitsch (1984); Gu80 = Guillery et al. (1980); Mo&Im87 = Morel and Imig (1987); Mo64 = Morest (1964); Raczkowski
and Rosenquist (1983); Ro&Re83 = Roda and Reinoso-Suarez (1983); Ro85 = Rosenquist (1985); Ru72 = Ruderman et al. (1972); Up83 = Updyke (1983); Wi&Mo83a = Winer and Morest (1983a);
Wi&Mo83b = Winer and Morest (1983b); Wi91 = Winer (1991); Zh86 = Zheng et al. (1986).
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