The Human Connectome Project Bruce Rosen2, Van J. Wedeen2, John D. Van Horn1, Bruce Fischl2, Randy L. Buckner3, Lawrence Wald2, Matti Hamalainen2, Steven Stu ebeam2, Joshua Ro man2, David W. Shattuck1, Paul M. Thompson1, Roger P. Woods1, Nelson Freimer5, Robert Bilder4, and Arthur W. Toga1 1Laboratory of Neuro Imaging (LONI), Department of Neurology, UCLA, Los Angeles, CA 90025; 2Athinoula A. Martinos Center for Biomedical Imaging, MGH, Harvard Medical School, Boston, MA; 3Cognitive Neuroscience Laboratory, Department of Psychology, Harvard University, Cambridge, MA; 4Jane & Terry Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA 90025; 5Center for Neurobehavioral Genetics, UCLA, Los Angeles, CA 90025 Introduction Mapping of the human connectome o ers a unique opportunity to understand the complexity of neural connectivity (Sporns et al., 2005, Wedeen et al., 2008, Hag- mann et al., 2007). The Human Connectome Project (HCP) is a project to construct a map of the complete structural and functional neural connections in vivo within and across individuals. The HCP represents the rst large-scale attempt to collect and share data of a scope and detail su cient to begin to address fundamental questions about human connectional anatomy, inter-regional communication, and their variations. Via a close collaboration between MGH and UCLA, this portion of the overall HCP is being developed to employ advanced neuroimaging methods, and to construct an extensive informatics algorithms infrastructure to analyze these data, derive detailed connectivity models capable of also accommodating phe- nomic and genomic data, building upon existing multidisciplinary and collaborative (a) With the advent of di usion neuroimaging methodologies, whole-brain tractography is now easily obtained. (b) How- ever, detailed information on speci c ber tracts is frequently limited and subject to cumulative errors in the tractography e orts currently underway. process. (c) Additionally, the details of crossing- bers are important to localize and quantify. Through this joint UCLA- MGH component of the HCP, we will de to connectomics resolution and e ciency. Methods The HCP is leveraging key scienti c domains that together yield a steady release of increasingly detailed connectomics data and tools. First, we have begun collecting data for the release of a very large, existing connectomic, behavioral and genomic dataset, including connectivity data from MZ/DZ twin pairs, to encourage broad participation in the HCP by the larger re- search community. These rich data will also allow us to quantify genetic (Chiang et al., 2009) and behavioral variation of white matter ber pathways and functional correlations for analy- sis by the entire community, and help de ne an optimized methodology for collection of a de nitive connectome dataset using di usion spectrum imaging (DSI, V. J. Wedeen, 2005). Con- currently, we are re ning and optimizing the spatial and functional resolution of our connectome neuroimaging techniques, to enhance acquisition of still further optimized HCP data, to be shared with the community as the data are acquired. This necessitates deployment of the next generation of MRI scan technology, speci cally built to extend the limits of DSI. Addi- tionally, our connectome e orts include the acquisition of high resolution neuroimaging data in a small subset of ex vivo whole brain specimens, as well as detailed chemo- and cyto- architectonic analysis and planar polarimetry of these specimens. These datasets will allow us and others to examine correlations between cytoarchitecture and the connectome (Burgel et al., 2006), as well as help validate in vivo results. All the while, we will continuously build and re ne vital infrastructure to support the analysis, archiving, and broad-scale dissemination of HCP data and informatics tools.
Conclusions This project is presently working to achieve the following goals: 1 ) optimize advanced imaging technologies and computational methods to map the in vivo human connectome using DSI; 2) validate these data against those collected at other HCP sites (Washington University in St. Louis and the University of Minne- sota); 3) through joint e ort between UCLA and MGH, construct sophisticated software tools for high-throughput connectomics analysis; 5) develop and disseminate data acquisition and analy-
(c) sis, educational, and training outreach materials.
Results Through this comprehensive white matter and functional con- nectivity mapping project – using next generation neuroimaging Representations of Anatomy and Connectivity Several visual representations of neuroanatomy and connectivity are shown: (a) pair-wise, regional white matter pathways connecting two ROIs in the default-mode network technologies – we will provide the neuroscience research com- (b) a connectivity matrix, where each cell in the matrix maps to an individual connection munity withnovel data and capabilities for measuring connectiv- (c) a smoothed cortical surface model parceled according to anatomical areas showing the connectivity links between several key areas ity that will signi cantly enhance our understanding of the neu- roanatomical and functional connectedness of the human brain. References BURGEL, U., AMUNTS, K., HOEMKE, L., MOHLBERG, H., GILSBACH, J. M. & ZILLES, K. (2006) White matter ber tracts of the human brain: three-dimensional mapping at microscopic resolution, topography and intersubject variability. Neuroimage, 29, 1092-105. CHIANG, M. C., BARYSHEVA, M., SHATTUCK, D. W., LEE, A. D., MADSEN, S. K., AVEDISSIAN, C., KLUNDER, A. D., TOGA, A. W., MCMAHON, K. L., DE ZUBICARAY, G. I., WRIGHT, M. J., SRIVASTAVA, A., BALOV, N. & THOMPSON, P. M. (2009) Genetics of brain ber architecture and intellectual performance. J Neurosci, 29, 2212-24. HAGMANN, P., KURANT, M., GIGANDET, X., THIRAN, P., WEDEEN, V. J., MEULI, R. & THIRAN, J.-P. (2007) Mapping Human Whole-Brain Structural Networks with Di usion MRI. PLoS ONE, 2, e597. SPORNS, O., TONONI, G. & KOTTER, R. (2005) The human connectome: A structural description of the human brain. PLoS Comput Biol, 1, e42. V. J. WEDEEN, P. H., W.-Y. I. TSENG, T. G. REESE AND R. M. WEISSKOFF. (2005) Mapping complex tissue architecture with di usion spectrum magnetic resonance imaging. . Mag. Res. Med., 54, 1377-86. WEDEEN, V. J., WANG, R. P., SCHMAHMANN, J. D., BENNER, T., TSENG, W. Y., DAI, G., PANDYA, D. N., HAGMANN, P., D'ARCEUIL, H. & DE CRESPIGNY, A. J. (2008) Di usion spectrum magnetic resonance imaging (DSI) tractography of crossing bers. Neuroimage, 41, 1267-77.