brain pathways

Network architecture of the long-distance pathways in the macaque brain Dharmendra S. Modha a,1 and Raghavendra Singh b a IBM Research-Almaden, San Jose, CA 95120; and b IBM Research-India, New Delhi 110070, India Communicated by Mortimer Mishkin, National Institute of Mental Health, Bethesda, MD, June 11, 2010 (received for review March 27, 2009) Understanding the network structure of white matter communica- tion pat hways is ess ential for unr avelin g the mys ter ies of the brain’s function, organization, and evolution. To this end, we derive a uni que net wor k inc orpora ting 410 anatomical tracin g studie s of themacaque brain from theCollat ionof Connec ti vit y dat a on the Macaque brain (CoCoMac) neuroinformatic database. Our network consists of 383 hierarchically organized regions spanning cortex, thalamus, and basal ganglia; models the presence of 6,602 directed long-distance connec tions ; is three times large r thanany previously derive d brainnetwork; and contains subnet work s corre spond ing to classic corticocortical, corticosubcortical, and subcortico-subcortical fiber systems. We found that the empirical degree distribution of the network is consistent with the hypothesis of the maximum entropy exponential distribution and discovered two remarkable bridges between the brain’s structure and function via network- theoretical analysis. First, prefrontal cortex contains a dispropor- tionate share of topologically central regions. Second, there exists a t ightl y integ rated core circu it, spanni ng parts of premot or corte x, prefr ontal cortex, temporal lobe, parie tal lobe, thalamus, basal ganglia, cingulate cortex, insula, and visual cortex, that includes much of the task-posit ive and task- negative netwo rks and might play a special role in higher cognition and consciousness. neuroanatomy | brain network | network analysis | structural | functional I n 1669, Nicolaus Steno (1) referred to white matter as “nature’s finest master piece. ” Whitemat ter pat hway s in thebrain mediat e information flow and facilitate information integration and co- operati on across functionally differ entiat ed distri buted centers of sensa tion, perception , action, cognitio n, and emotion. Uncov- eri ng the glob al topo log ical reg ula rit ies of the log ica l long- distanceconnecti ons thatare subserve d by the physi calwhite matt er pathways is a key prerequisite to any theory of brain function, dys- function, organization, dynamics, and evolution. Anatomical tracing in experi mental animals has histori cally been the pervasive technique for mapping long-distance white matter projectio ns (2–4). Given the resolut ion of anatomical tracing experiments, they typically furnish data at a macroscale of cortical areas or, more generally, brain regions. The associated network description* models brain regions as vertices and the presence of reported long-distance connections as directed edges betwee n them. The most well-known network of the macaque monkey visual cortex consists of 32 vertices and 305 edges (2). Other networks of the macaque cortex consist of 70 vertices and 700 edges (5) and 95 vertices and 2,402 edges (6). The largest network of the cat cortex has 95 vertices and 1,500 edges (7). Network-theoretical analyses have uncovered a number of remarkable insights: distributed and hierarchical structure of cortex (2); topological organization of cortex(8); indete rmin acy of uniquehierarch y (9);functional small - world characteristics, optimal set analysis, and multidimensional scaling (10); small-world characteristics (11); nonoptimal compo- nent pla cement for wire len gth (6) ; str uct ura l andfuncti ona l mot ifs (12); and hub identification and classification (13). However, even the largest previous network (6) completely lacks edges corre- sponding to corticosub corti cal and subcor tico-s ubcortical long- distance connections and has signi ficant gaps even among corticocortical long-distance connections ( SI Appendix, Fig. S1). To gain a bet ter understanding of the str uct ure and org anization of the brain, a network spanning the entire brain would be ex- tre mel y use ful . Suc h a net wor k wil l be an indis pensab le founda tion for clinical, systems, cognitive, and computational neurosciences (14). No such network has been reported. We undertake the challenge of constructing, visualizing, and analyzing such a netwo rk. Our net wor k ope ns the door to the appli cat ion of la rge -scale network-theoretical analysis that has been so successful in understanding the Internet (15), metabolic networks, protein in- tera ctionnetworks(16), vari ous socia l netwo rks (17) , and sear ching the World-Wide Web (18, 19). Model: Deriving the Network Description Collation of Connectivity data on the Macaque brain (CoCo- Mac), a seminal contribution to neuroinformatics, is a publicly avail able databa se (20–22). Conscie ntiousl y and meticul ously, the database curators have collated and annotated information on over 2,500 anatomical tracer injections from over 400 pub- lished experimental studies. CoCoMac is an objective, coordinate-independent collection of annotations that captures two relationships between pairs of bra in reg ions, wher e eac h bra in reg ion ref ers to cor tica l and subcortical subdivisions as well as to combinations of such subdivisions into sulci, gyri, and other large ensembles. The first re- lationship is connectivity — whether a brain region in one study projects to another region in (possibly) a different study. There are 10,681 connect ivity relations. † The second rel ati onsh ip is mapping— whether a brain region in one study is identical to, a sub structure of, or a supr astruct ure of another reg ion in (possi bly ) a different study. There are 16,712 mapping relations. Unfortu- nately, because of a multiplicity of brain maps, divergent nomen- clature, boundary uncertain ty, and differing resolutio ns in different studies, mapping relations are often conflicting and connectivity information is typica lly scattered acrossrelated brain regions . The situation is aptly described by Van Essen (23): “Our fragmentary and rapidly evolving understanding is reminiscent of the situation faced by cartographers of the earth’s surface many centuries ago, when maps were replete with uncertainties and divergent por- trayal s of mos t of the pl anet ’s surf ace .” Consolidating connecti vity information by merging logically equivalent brain regions and aggregating their connectivity is a necessary prerequisite to any net work-analytical stud y. Further, it is des ira ble to pla ce the merged brain regions into a coherent, uni fied, hierarchical brain map that recursively partitions brain and its constituents into Author contributions: D.S.M. and R.S. designed research, performed research, analyzed data, and wrote the paper. The authors declare no conflict of interest. Freely available online through the PNAS open access option. 1 To whom correspondence should be addressed. E-mail: [email protected]. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1008054107/-/DCSupplemental . *It is important to draw a distinction between the actual physical network in a macaque brain and its logical description in network-theoretical terminology using reported data. Because we are primarily concerned with the latter usage in this paper, we will refer to network description as network. † CoCoMac also reports 13,498 plausible connections that were tested for but were not found. This substantially reduces the possibility that projections present in the brain are dramatically undersampled or underreported. www.pnas.org/cgi/doi/10.1073/pnas.1008054107 PNAS | July 27, 2010 | vol. 107 | no. 30 | 13485–13490 N E U R O S C I E N C E

brain pathways

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