The Human Connectome Project: Computational Challenges and Opportunities
David Van Essen (Anatomy & Neurobiology, Washington University)
(January 28, 2013 3:00 PM - 3:50 PM)
Recent advances in noninvasive neuroimaging have set the stage for the systematic exploration of human brain circuits in health and disease. One such effort is the Human Connectome Project (HCP), which will characterize brain circuitry and its variability in healthy adults. A consortium of investigators at Washington University, University of Minnesota, University of Oxford, and 7 other institutions is engaged in a 5-year project to characterize the human connectome in 1,200 individuals (twins and their non-twin siblings). Information about structural and functional connectivity will be acquired using diffusion MRI and resting-state fMRI, respectively. Additional modalities will include task-evoked fMRI and MEG/EEG, plus extensive behavioral testing and genotyping. Advanced visualization and analysis methods will enable characterization of brain circuits in individuals and group averages at high spatial resolution and at the level of functionally distinct brain parcels (cortical areas and subcortical nuclei). Comparisons across subjects will reveal aspects of brain circuitry which are related to particular behavioral capacities and which are heritable or related to specific genetic variants. Data from the HCP will be made freely available to the neuroscience community. A user-friendly informatics platform will enable investigators around the world to carry out many types of data mining on these freely accessible, information-rich datasets. The emergence of massive amounts of high quality and consistently acquired neuroimaging and behavioral data from the HCP and other large-scale projects raises exciting opportunities and challenges on the computational and informatics fronts. Just as bioinformatics emerged as an exciting new discipline once vast amounts of genomic and proteomic data became available, it is likely that neuroinformatics will rapidly evolve as new methods and approaches are developed to capitalize on the ongoing explosion of human neuroimaging data.