Network architecture of the cerebral nuclei (basalganglia) association and commissural connectomeLarry W. Swansona,1, Olaf Spornsb, and Joel D. Hahna

aDepartment of Biological Sciences, University of Southern California, Los Angeles, CA 90089; and bDepartment of Psychological and Brain Sciences, IndianaUniversity, Bloomington, IN 47405

Contributed by Larry W. Swanson, August 10, 2016 (sent for review July 18, 2016; reviewed by Ann M. Graybiel and Liqun Luo)

The cerebral nuclei form the ventral division of the cerebral hemi-sphere and are thought to play an important role in neural systemscontrolling somatic movement and motivation. Network analysiswas used to define global architectural features of intrinsic cerebralnuclei circuitry in one hemisphere (association connections) andbetween hemispheres (commissural connections). The analysis wasbased on more than 4,000 reports of histologically defined axonalconnections involving all 45 gray matter regions of the rat cerebralnuclei and revealed the existence of four asymmetrically intercon-nected modules. The modules form four topographically distinctlongitudinal columns that only partly correspond to previous inter-pretations of cerebral nuclei structure–function organization. The net-work of connections within and betweenmodules in one hemisphereor the other is quite dense (about 40% of all possible connections),whereas the network of connections between hemispheres isweak and sparse (only about 5% of all possible connections). Par-ticularly highly interconnected regions (rich club and hubs withinit) form a topologically continuous band extending through two ofthe modules. Connection path lengths among numerous pairs ofregions, and among some of the network’s modules, are relativelylong, thus accounting for low global efficiency in network commu-nication. These results provide a starting point for reexamining theconnectional organization of the cerebral hemispheres as a whole(right and left cerebral cortex and cerebral nuclei together) andtheir relation to the rest of the nervous system.

behavior | connectomics | mammal | neural connections | neuroinformatics

The paired adult vertebrate cerebral hemispheres are differ-entiations of the embryonic neural tube’s endbrain (telen-

cephalic) vesicle, which in turn forms a ventral nonlaminatedpart, the cerebral nuclei, and then a dorsal laminated part, thecerebral cortex (1–3). In mammals the largest parts of the cerebralnuclei by volume are the caudoputamen (striatal) and globuspallidus (pallidal). Together, they are commonly regarded as theendbrain parts of the basal ganglia (3), which play an importantrole in controlling skeletomuscular (somatic) movements and inthe etiology of movement disorders (4).A major shift in thinking about the basal ganglia occurred with

the recognition that certain ventral parts of the cerebral nucleidisplay the same basic circuit organization, but involve differentparts of a cerebral cortex to cerebral nuclei to thalamus to cerebralcortex loop (5). This finding led to an expanded view of the basalganglia, with dorsal and ventral striatopallidal subsystems involvedprimarily in movement (dorsal) and motivational functionality(ventral) (6). Complete expansion of the view was then proposedon the basis of connectional, gene expression, embryological, andfunctional evidence (7). It was hypothesized that the entire cere-bral cortex, cerebral nuclei, and thalamus can be divided into fourbasic subsystems involving dorsal, ventral, medial, and caudorostraldomains of the striatopallidum.The present study reexamines the organization of axonal con-

nections between all parts of the cerebral nuclei, using systematic,data-driven, network analysis methods (8, 9). The analysis is basedon a directed, weighted macroconnectome of association con-nections (between ipsilateral parts) and commissural connections(between parts on one side and those on the other side), using the

same basic strategy and methodology applied to the rat cerebralcortical association macroconnectome (10) but with additional an-alytical approaches and curation tools. In this approach a macro-connection is defined as a monosynaptic axonal (directed, from/to)connection between two nervous system gray matter regions orbetween a gray matter region and another part of the body (such asa muscle) (11, 12). All 45 gray matter regions of the cerebral nucleion each side of the brain were included in the analysis. The goal ofthis analysis was to provide global, high-level, design principles ofintrinsic cerebral nuclei circuitry as a framework for more detailedresearch at the meso-, micro-, and nanolevels of analysis (13).

ResultsSystematic curation of the primary neuroanatomical literatureyielded no reports of statistically significant male/female, right/left, or strain differences for any association or commissuralconnection used in the analysis, which therefore simply applies tothe adult rat in the absence of further data. The entire datasetwas derived from 4,067 connection reports expertly curated from40 peer-reviewed original research articles; 2,731 or 67.2% of theconnection reports were from the L.W.S. laboratory. A standardrat brain atlas nomenclature (Dataset S1) was used to describeall connection reports (Dataset S2), which were based on a va-riety of experimental monosynaptic anterograde and retrogradeaxonal pathway tracing methods identified for each connectionreport in Dataset S2.

Single-Hemisphere Connection Number. The curation identified 731rat cerebral nuclei association macroconnections (RCNAMs) aspresent and 1,051 as absent, between the 45 gray matter regionsanalyzed for the cerebral nuclei as a whole; this result yields aconnection density of 41% (731/1,782). No adequate published

Significance

The cerebral nuclei together with the cerebral cortex form thecerebral hemispheres that are critically important for the con-trol of voluntary behavior and motivation. Network analysis ofmicroscopic connectional data collected since the 1970s in asmall, intensely studied mammal provides a new way to un-derstand overall design features of circuitry coordinating ac-tivity in the various parts of the cerebral nuclei on both sides ofthe brain. Basically, intracerebral nuclei circuitry is organizedinto four modules on each side of the brain, with connectionswithin and between modules on one side being quite denseand connections between the cerebral nuclei on either sidebeing quite sparse. The results provide insight into cerebralnuclei structure and function.

Author contributions: L.W.S. designed research; L.W.S. and O.S. performed research; O.S.and J.D.H. contributed new reagents/analytic tools; O.S. analyzed data; and L.W.S. wrotethe paper.

Reviewers: A.M.G., Massachusetts Institute of Technology; and L.L., Stanford University.

The authors declare no conflict of interest.

Data deposition: Network analysis tools are available at the Brain Connectivity Toolbox.1To whom correspondence should be addressed. Email: larryswanson10@gmail.com.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1613184113/-/DCSupplemental.

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Fig. 1. Rat cerebral nuclei association connectome. Directed synaptic macroconnection matrix with gray matter region sequence in the topographicallyordered nomenclature hierarchy is provided in Dataset S1. Color scale of connection weights and properties is at the bottom. Abbreviations: AAA, anterioramygdalar area; ACB, accumbens nucleus; BA, bed nucleus of accessory olfactory tract; BAC, bed nucleus of anterior commissure; BSTal, anterolateral area ofbed nuclei of terminal stria; BSTam, anteromedial area of bed nuclei of terminal stria; BSTd, dorsal nucleus of bed nuclei of terminal stria; BSTdm, dorsomedialnucleus of bed nuclei of terminal stria; BSTfu, fusiform nucleus of bed nuclei of terminal stria; BSTif, interfascicular nucleus of bed nuclei of terminal stria;BSTju, juxtacapsular nucleus of bed nuclei of terminal stria; BSTmg, magnocellular nucleus of bed nuclei of terminal stria; BSTov, oval nucleus of bed nuclei ofterminal stria; BSTpr, principal nucleus of bed nuclei of terminal stria; BSTrh, rhomboid nucleus of bed nuclei of terminal stria; BSTse, strial extension of bednuclei of terminal stria; BSTtr, transverse nucleus of bed nuclei of terminal stria; BSTv, ventral nucleus of bed nuclei of terminal stria; CEAc, capsular part ofcentral amygdalar nucleus; CEAl, lateral part of central amygdalar nucleus; CEAm, medial part of central amygdalar nucleus; CP, caudoputamen; FS, striatalfundus; GPl, lateral globus pallidus; GPm, medial globus pallidus; IA, intercalated amygdalar nuclei; LSc.d, dorsal zone of caudal part of lateral septal nucleus;LSc.v, ventral zone of caudal part of lateral septal nucleus; LSr.dl, dorsolateral zone of rostral part of lateral septal nucleus; LSr.m.d, dorsal region of medialzone of rostral part of lateral septal nucleus; LSr.m.v, ventral region of medial zone of rostral part of lateral septal nucleus; LSr.vl, ventrolateral zone of rostralpart of lateral septal nucleus; LSv, ventral part of lateral septal nucleus; MA, magnocellular nucleus; MEAad, anterodorsal part of medial amygdalar nucleus;MEAav, anteroventral part of medial amygdalar nucleus; MEApd, posterodorsal part of medial amygdalar nucleus; MEApv, posteroventral part of medialamygdalar nucleus; MS, medial septal nucleus; NDB, diagonal band nucleus; OT, olfactory tubercle; SF, septofimbrial nucleus; SH, septohippocampal nucleus;SI, innominate substance; TRS, triangular septal nucleus.

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data were found for 198 (10.0%) of all 1,980 (452 – 45) possibleassociation macroconnections; this result yields a matrix coverage(fill ratio) of 90% (Fig. 1). Assuming the curated literature repre-sentatively samples the 45-region matrix, the complete RCNAMdataset would contain ∼812 macroconnections (1,980 × 0.41), witha projected average of 18 input/output association macroconnectionsper cerebral nuclei region (812/45). For network analysis, values of“unclear” and “no data” are assigned to and combined with valuesin the “absent” category, resulting in a connection density of 37%(731/1,980). Considering only connections that have been unam-biguously identified yields an average of input/output macro-connections of 16.2 (731/45), with significant variations forparticular cerebral nuclei regions (input range 1–38, output range0–36; Fig. 2 A and B). Individual regions show large variations inthe ratio of the number of distinct inputs and outputs (their indegree and their out degree; Fig. 2A) as well as the aggregatedstrength of these inputs and outputs (their in strength and their outstrength; Fig. 2B). Strong imbalance implies that some regionsspecialize as “receivers” of inputs and others as “senders” of out-puts. The distribution of weight categories for the 731 connectionsreported as present is shown in Fig. S1A. The weight scale used forweighted network analysis is shown in Fig. S1B.

Network Analysis for Modules. For single-hemisphere interconnec-tions, modules were detected by modularity maximization whilesystematically varying a spatial resolution parameter γ to assessmodule stability (9, 14). Varying γ within the interval [0.5–1.5]centered on the default setting of γ = 1 resulted in four stable so-lutions (Fig. 3) with four to seven modules each. One module (M4)was stable across the entire range of γ, whereas modules M1–M3were stable across most of the range. As the resolution parameterwas increased toward finer and finer partitions, M3 split into twoand then three submodules. The four-module solution was stableover the broadest range of γ and had the largest ratio of within-to-between module connection density in the range of γ examinedhere; hence, it was chosen for further analysis. The four-modulesolution with all regions and connections can be displayed as aweighted matrix (Fig. 4) or as a spring-embedded layout (Fig. 5A),and the regional composition of each module is provided in Fig. S2.

Connection Patterns. The simplest way to view the between-moduleinteraction pattern is with aggregated between-module connec-tions (Fig. 5B), which show that M1 and M3 are predominantlysending modules, M2 is predominantly a receiving module, andM4 is only weakly connected with the other modules. There areat least weak bidirectional connections between all four modules.

Fig. 2. (A and B) In degree/out degree (A) and in strength/out strength (B) for all regions of the single-hemisphere rat cerebral nuclei association macroconnectionnetwork. Regions are ranked by total degree, in descending order. Bar colors indicate the asymmetry in in/out degree and in/out strength, respectively, computed as(in degree – out degree)/(in degree + out degree) and (in strength – out strength)/(in strength + out strength). A value of −1 (cyan) indicates strong prevalence of outdegree/strength (the area is a “sender”) and a value of +1 (magenta) indicates a strong prevalence of in degree/strength (the area is a “receiver”). For abbreviationssee Fig. 1.

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Corresponding average module-by-module connection densities(binary and weighted) further illustrate this point and are pro-vided in Table S1. Table S2 lists counts and percentages ofconnection classes by matrix block. Strongly asymmetric averageconnection weights between modules result in strongly directed(asymmetric) connection patterns among modules.The weighted RCNAM network can be decomposed into a sym-

metric (fully reciprocated) and a directed (fully nonreciprocated)component (15). To quantify the extent to which the distribution ofconnection weights in the RCNAM network is asymmetric, we com-puted the network’s reciprocity ρ, a quantity that was then scaled withrespect to a degree-preserving randomized null model. The reciprocityof the RCNAM network is ρ = 0.262, which indicates a tendency toreciprocate that is less strong than that of the network of rat corticalassociation macroconnections (RCAMs) that yields ρ = 0.331.The 45 regions of the RCNAM network comprise striatal (23

regions) and pallidal (22 regions) parts (Fig. 1). Whereas connec-tions from pallidal to striatal regions are denser (45%) than striatalto pallidal connections (33%), the aggregated weight of striatal topallidal connections is twice as strong as for the reverse direction.Stronger striatal to pallidal connections are also encountered withineach of the four modules.

Efficiency, Hubs, and Rich Club. The overall network topology doesnot display classic small-world attributes, which is due to itsrelatively long path length (resulting in relatively low efficiency).

Whereas clustering is greater than in randomized controls, thenetwork is also significantly less efficient [clustering coefficient(CC) = 0.0189 (0.0108 ± 7.35 × 10−4; mean and SD of a populationof random networks that were globally rewired while preservingthe degree sequence (SI Materials and Methods); global efficiency(GE) = 0.0837 (0.1351 ± 0.0104)]. This trend prevails when nodesthat lack identified association outputs [triangular septal nucleus(TRS), strial extension of bed nuclei of terminal stria (BSTse), bednucleus of anterior commissure (BAC), and bed nucleus of acces-sory olfactory tract (BA)] are removed from the matrix [CC =0.0179 (0.0109 ± 5.48 × 10−4), GE = 0.0959 (0.1512 ± 0.0116)]. Thelow efficiency of the RCNAM network is due to the existence oflong paths between many of the networks’ regions and modules.Whereas the mean path length between region pairs is 3.16 steps(computed from the weighted RCNAM network), 75 region pairsare linked by a minimal path with a length of 6 steps or greater.Among module pairs, modules M1 and M4 are topologically mostdistant from each other (see Fig. 5A) and are separated by, onaverage, 4.9 (M4 → M1) and 4.6 (M1 → M4) steps.Centrality measures (degree, strength, betweenness, closeness)

are summarized in Fig. S3. Regions innominate substance (SI),anteromedial area of bed nuclei of terminal stria (BSTam), ante-rolateral area of bed nuclei of terminal stria (BSTal), rhomboidnucleus of bed nuclei of terminal stria (BSTrh) and anterodorsalpart of medial amygdalar nucleus (MEAad) (abbreviations in Fig. 1)rank in the top 20th percentile on all four measures, thus forming

Fig. 3. Stability of module partitions under variation of spatial resolution parameter γ.A–D show four stablemodule partitions encountered in the range γ = [0.5–1.5],with regions within modules arranged by their total node strength. E plots the number of modules encountered at each level of γ. All four partitions shown hereremain stable across their respective ranges of γ, as verified by computing the variation of information across partitions. Green arrows between A–D indicate modulesthat remain unchanged across different solutions. Note thatM4 is robustly detected at all levels of γ. An unstable solution encountered around γ = 1.2 is excluded fromthe analysis. The four-module solution (A) is adopted in the remainder of the study. Region abbreviations along the axes for A are provided in the same sequence as inFig. 4 and are defined in the legend of Fig. 1. The color scale refers to the seven weight categories (1, very weak; 2, weak; 3, weak to moderate; 4, moderate; 5,moderate to strong; 6, strong; 7, very strong) of existing connections, and 0 corresponds to connections that are shown to be absent or for which there are no data.

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putative hubs in the association network topology. These five re-gions form a fully connected subgraph with an average connectionweight of 0.39 (compared with a density of 37% and an averageconnection weight of 0.04 for the entire network). Four of the fivecandidate hubs are located in M2, with a sole hub (MEAad) in M3.Rich club organization is present, as indicated by significantly

greater density of connections among high-degree nodes com-pared with that in a degree-sequence–preserving null model(Fig. S4). Significance was assessed after correcting P values formultiple comparisons. The rich club shell where the correctedP value was minimal (P = 3 × 10−7) contains nine member regions,including the five putative hubs listed above: SI, BSTam, ventralnucleus of bed nuclei of terminal stria (BSTv), dorsomedial nu-cleus of bed nuclei of terminal stria (BSTdm), BSTal, BSTrh,interfascicular nucleus of bed nuclei of terminal stria (BSTif),transverse nucleus of bed nuclei of terminal stria (BSTtr), andMEAad. Rich club members overlap exclusively with modules M2and M3.

Topographic Arrangement of Modules, Rich Club, and Hubs. Thespatial distribution of the four modules and their 45 individualregional components was mapped onto a standard brain atlas(16) to distinguish whether module components are topograph-ically either interdigitated or segregated (Fig. 6). Clearly, themodules form four spatially segregated longitudinal columns thatmay be described as dorsal (M1), ventral (M2), medial (M4), androstrocaudal (M3), the latter of which consists of two segments(rostral and caudal) separated by a gap formed by region SI. Therich club also forms a spatially segregated mass of regions in M2and M3 (Fig. 6), with the putative hubs nested within it.The dorsal module (M1) corresponds to the classic dorsal

striatopallidal system (3, 4), whereas the ventral module (M2)includes the classic ventral striatopallidum (5) and the centralamygdalar nucleus and anterior division of bed nuclei of terminalstria (17). The caudal segment of M3 is formed by the medialamygdalar nucleus (striatal) and related regions, whereas therostral segment is formed by the posterior division of bed nuclei

Fig. 4. Weighted connection matrix (log10 scale) for 45 (single-hemisphere) areas. Ordering is as for the four-module matrix in Fig. 3A, with regions withinmodules arranged by total node strength. For abbreviations see Fig. 1.

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of terminal stria (pallidal) and adjacent ventral parts of the lateralseptal nucleus (17). Finally, the medial module (M4) consists ofthe bulk of the septal region, which has both striatal and pallidalcomponents (7, 18).

Connections Between Hemispheres. Curation identified 96 com-missural connections as present and 1,693 as absent (that is, of1,789 connections for which adequate data were available, 5.4%are present, indicating a relatively sparse commissural network)between the 45 gray matter regions analyzed for all cerebralnuclei in each hemisphere. No adequate published data werefound for 236 (11.7%) of all 2,025 (452) possible commissuralmacroconnections, for a matrix coverage of 88.3%. Assuming thecurated literature representatively samples the 45-region matrix,the complete rat cerebral nuclei commissural macroconnectome(RCNCM) dataset would contain ∼109 macroconnections (2,025 ×0.054), with an average of 2.4 output commissural macro-connections per cerebral nuclei region (109/45). The matrix ofidentified projections has a density of 96/2,025 (4.7%). Whereasthis density corresponds to an average of 2.1 commissural mac-roconnections per region, their actual number varies over abroad range (input range 0–8; output range 0–13). All 96 iden-tified commissural connections are in the very weak, weak, andweak to moderate weight categories. Twelve of 45 regions gen-erated a homotopic commissural connection (to the same regionon the contralateral side) and 84 of the commissural connectionswere heterotopic (to a different region on the contralateral side);all commissural connections were deemed symmetric with respectto the two sides because no evidence of right–left asymmetrieswas found.

A complete (both hemispheres) connection matrix for thecerebral nuclei is formed by the addition of commissural connec-tions to the association connections (Fig. S5). Modules for the two-hemisphere (90-region) network were detected as for the singlehemisphere, by varying the resolution parameter. By far the moststable solution contained eight modules, four in each hemisphere,matching the four-module solution found in the single-hemisphereanalysis (Fig. 7).The density and weight of commissural connections by mod-

ules are summarized in Table S3 and a layout of the network forthe two hemispheres is shown in Fig. 8. Module M1 maintains nocommissural connections, whereas in contrast, M2 generates by farthe most commissural connections, particularly with M2 and M3.Within modules M2–M4, regions BSTam, BSTdm, BSTal, BSTrh,and fusiform nucleus of bed nuclei of terminal stria (BSTfu)maintain the most commissural connections. The extent and dis-tribution of association and commissural connections arising in onehemisphere is clearly illustrated by removing the association andcommissural connections arising in the other hemisphere from thefully bilateral network (Fig. 9; compare with Fig. 8).The number of intrahemispheric connections maintained by

each region strongly correlates with the number of its commissuralconnections (Fig. 10). Computing the shortest paths for the 90-region network reveals that paths connecting modules across thetwo hemispheres vary greatly in length. M4(side1↔ side2) is linkedby relatively short paths, whereas paths between M1(side1↔ side2)are among the longest in the entire network.Inclusion of commissural connections (and inclusion of

communication paths that span the two hemispheres) resultsin changed hub score rankings. Regions BSTal, BSTrh, and

Fig. 5. Layout diagrams of connection patterns in a single hemisphere. (A) Nodes (regions) and edges (connections) are projected onto two dimensions, usinga Fruchterman–Reingold energy minimization layout algorithm. Nodes are color coded by module assignment (M1, blue; M2, yellow; M3, green; M4, red). Tosimplify the plot, connections are drawn without reference to directionality (gray color level and thickness of line proportional to log10 of connection weight).(B) Summary layout of aggregated connection weights between modules M1–M4. Arrows show directionality of connections and their thickness is pro-portional to the average connection weights of between-module connections. For abbreviations see Fig. 1.

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Module 1 Module 2 Module 3 Module 4 Rich club

Red abbreviations = rich club member only; Bold red = rich club member and hub

CP CPMS

NDB

MS

SI SI

SI

FS

FS

CP

SF

GPl

GPl

SI

CP

CP

CP

ACB

OT OT

ACB

LSc.d

LSc.d

LSc.v

LSc.v

SH

SH

LSr.vl

LSr.m.vLSvlLSv

BSTovBSTamBSTju

GPlGPm

BSTseBSTprBSTif

BSTifBSTvCEAc

CEAcCEAc

IAIA

CPGPlSI

CEAlCEAc

IAMEApdMEApv

AAA MEAadMAMEAav

NDB BA

BSTtrBSTal

BSTrh

BSTmgBSTdm

BSTal

BSTfu IA

BSTse

MA

MA

OT OTNDB NDB

LSr.m.dLSr.m.v

SFTRS

LSr.dl

SI

Rostral

Caudal

A (AL12) B (AL15)

C (AL18) D (AL20)

E (AL22) F (AL26)

G (AL29)

Hypothalamus

Hypo- thalamus

Thalamus

Cerebral cortex

Cerebral cortex

Cerebral cortex

A B C ED F G H

Fig. 6. Topographic distribution of modules and rich club. Regions, modules, and rich club are plotted on a standard atlas of the rat brain (16), with atlaslevels (AL in A–G) arranged from rostral to caudal, as indicated in the dorsal view of the rat brain (H). For clarity, the rich club (red) is shown only on the leftside of the brain. Modules are color coded as in Figs. 5 and 8; for abbreviations see Fig. 1.

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MEAad maintain their status as putative hubs with 80thpercentile rankings in all four centrality measures (degree,strength, betweenness, closeness), whereas regions SI and BSTamdrop out.

DiscussionThis study yielded four basic results. First, the intracerebralnuclei network is arranged in four asymmetrically interconnectedand topographically distinct network modules based on number

Fig. 7. Module stability for connections between the cerebral nuclei in each hemisphere, displayed here for the bihemispheric 90-region network.A shows the numberof modules, and B and C show stable module partitions encountered in the range γ = [0.5–1.5], with regions within modules arranged by their total node strength. Onepartition into 4+ 4modules (identical to that in Fig. 3A) remains stable across most of the parameter range. Regions in C are arranged in the same order as for Fig. 3A, inboth hemispheres.

Fig. 8. Layout for the 90-region network betweenthe cerebral nuclei regions on both sides of thebrain. Methodology and all conventions are as inFig. 5A. Region (node) labels are shown only for side1 (Left), and region positions are symmetric acrossthe midline, with homotopic connections extendinghorizontally as they connect symmetrically arrangednode pairs on the two sides.

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and weight of connections. Second, the association connectionsubnetwork is much denser than the commissural subnetwork.Third, a topographically continuous band of rich club and hubregions (nodes) stretches through two of the modules. And fourth,the network as a whole does not show small-world organization.There are obvious similarities and differences between the rat

cerebral nuclei association network described here and the ratcerebral cortical association network recently analyzed withsimilar methodology (10). Both networks are quite dense (in thesense that around 40% of all possible connections exist), bothdisplay four modules that are asymmetrically interconnected andtopographically distinct, and both have a topographically segre-gated band of rich club and hub regions. The most striking dif-ference is that the cortical association network shows muchstronger expression of small-world attributes (specifically shortpath length conferring high efficiency), which are not as clearlyapparent in the cerebral nuclei association network. This findingwas unexpected because small-world topology was a commonfeature in earlier analyses of nervous system organization, many ofthem carried out on representations of cortical networks (19).Instead, the intracerebral nuclei network has several connec-

tional characteristics that set it apart from previous cortico-corticaldatasets, including the rat cerebral cortical association macro-connection network (10). For example, the network displays alesser extent of reciprocity (that is, a greater tendency toward uni-directional or asymmetrically weighted connections), at the level ofboth individual nodes and network modules. This results in lessdirect communication patterns, longer path lengths, and lowerglobal efficiency. Another major difference with important im-plications for network modeling is that the sign of most cerebralcortical association connections is presumably positive (excitatory)whereas the sign of most cerebral nuclei association connectionsis presumably negative (inhibitory) (3). These differences in ar-chitectural features may reflect differences in function, with thesmall-world topology of cortico-cortical networks promoting theintegration of information (requiring globally short paths) whereasintracerebral nuclei networks mediate the flow of neural signals

between cortex and subcortical regions involved in controllingspecific behaviors (biased toward parallel and independent paths).The structure–function significance of the four network mod-

ules in each hemisphere and the connections within and betweenmodules (in both hemispheres; Fig. 7) are not immediately obvious.However, one generalization seems clear: Module M1 correspondsto the classical striatopallidum, which receives its major input fromisocortex and is involved in somatomotor control mechanisms

Fig. 9. Layout for association (Left) and commis-sural (Right) connections originating from the 45cerebral nuclei regions in one hemisphere. Associa-tion and commissural connections originating fromthe cerebral nuclei regions in the other hemisphereare assumed to be symmetric in rat (Fig. 8) based oncurrent data. All conventions are as in Figs. 5A and 8.

Fig. 10. Scatter plot of each region’s intrahemisphere degree (the numberof its distinct input plus output connections, absent any commissural con-nections) vs. the region’s interhemisphere degree (the number of its distinctcommissural connections, both homo- and heterotopic inputs plus outputs).The two measures are significantly correlated (Pearson correlation, R =0.619, P = 5.84 × 10−6; Spearman correlation, R = 0.641, P = 2.14 × 10−6). Forabbreviations see Fig. 1.

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(3, 4), whereas the other three modules (M2–M4) receive theirmajor input from limbic cortex and are involved in motivatedbehavioral mechanisms (4, 5, 7, 17, 18). As broad generaliza-tions, module 2 contains the central amygdalar nucleus andanterior division of the bed nuclei of the terminal stria and hasbeen most notably implicated in homeostatic behaviors (forexample, eating and drinking) and anxiety (17, 20, 21, 22, 23);module 3 contains the medial amygdalar nucleus and posterior di-vision of the bed nuclei of the terminal stria and has been impli-cated most notably in social interactions and responding to threatsin the environment (17, 24, 25); and module 4 receives its majorinputs from the hippocampus and may thus play a role in spatialand mnemonic influences on motivated behavior in general (18).Furthermore, the nine members of the rich club (and the

subset of putative hubs within it: SI, BSTam, BSTv, BSTdm,BSTal, BSTrh, BSTif, BSTtr, and MEAad) all reside in M2 andM3, and the extrinsic connections of the rich club predict thatcollectively they coordinate somatic, autonomic, and neuroen-docrine responses in motivated survival behaviors, including repro-ductive, fight or flight, eating and drinking, and foraging (5, 20–25).There are three main limitations of the present study. First,

the analysis was based on macroconnections, that is, connectionsbetween one gray matter region and another. Macroconnectomicsprovide a high-level view of network design principles, but do nottake into account the critical role of connections between distinctneuron types making up each gray matter region or the connectionsof individual neurons forming each neuron-type population. Rel-atively few systematic data currently exist for connections betweenneuron types and individual neurons in the mammalian cerebralnuclei, but our analysis provides a framework for interpreting fu-ture datasets at these deeper levels of analysis.The second limitation is that the database used here is not

complete. We did not identify data for ∼10% of all possibleconnections, and not all connection reports relied on the best

available methodology; importantly, however, these gaps havebeen identified systematically. In addition, the results could varywith different parceling schemes for cerebral nuclei regionali-zation and with the addition of more accurate and reliableconnection data. From a neuroinformatics perspective, this issimply version 1.0 of the rat intracerebral macroconnectome, andfuture versions can be easily computed from updated data inDataset S3. Presumably results will vary in proportion to themagnitude of differences between versioned datasets, but it hasbeen shown that, at least qualitatively, the results of modularityanalysis become stable with adjacency matrix fill ratios greaterthan 60–70% (10).The third main limitation of this study is that the macro-

connectome assembled here examined only the organization ofintrinsic circuitry between the 45 parts of the cerebral nuclei. Theequally important extrinsic connections remain to be assembledfor systematic network analysis. This project is underway but tounderstand fully the role of cerebral nuclei connectivity will requireplacing it within the context of the nervous system connectome as awhole, including its interactions with the rest of the body—theneurome.

Materials and MethodsMethods for the underlying network analysis are essentially the same as thosedescribed in detail elsewhere (10) and in SI Materials and Methods. All rel-evant data in the primary literature were interpreted in the only availablestandard, hierarchically organized, annotated nomenclature for the ratbrain (Dataset S1), using descriptive nomenclature defined in the founda-tional model of connectivity (11, 12). Association and commissural connec-tion reports were assigned ranked qualitative connection weights based onpathway tracing methodology, injection site location and extent, and ana-tomical density. All connection reports are provided in an Excel worksheet(Dataset S2), and the data used just to construct connection matrices areprovided in another Excel worksheet (Dataset S3).

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Swanson et al. PNAS | Published online September 19, 2016 | E5981

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