This project will characterize adult human brain circuitry, including its variability and its relation to behavior and genetics. To achieve this ambitious objective, a broad-based multi-institutional consortium of distinguished investigators will acquire cutting-edge neuroimaging data in 1,200 healthy adult humans along with behavioral performance data and blood samples for genotyping. The main cohort of subjects will be twins plus non-twin siblings - a strategy that enables powerful analyses of heritability and genetic underpinnings of specific brain circuits. Comprehensive connectivity maps will be generated for each individual and for population averages using sophisticated data analysis methods. This human connectome will be expressed relative to functional subdivisions (parcels) defined by connectivity and by classical architectonic methods. Data from these maps will reveal fundamental aspects of brain network organization. A powerful, user-friendly informatics platform will be implemented to facilitate the management, analysis, visualization, and sharing of these rich and complex datasets. Because these tools and datasets will have Immediate and long range potential to influence neuroscience research in health and disease, extensive outreach efforts are planned for promoting their widespread awareness and usage. The imaging modalities include three types of magnetic resonance imaging: (i) diffusion imaging using HARDI methods to map structural connectivity;(ii) resting-state fMRI (R-fMRI) to reveal maps of functional connectivity;(iii) task-fMRI (T-fMRI) to reveal brain activation patterns associated with a broad set of behavioral tasks. Magneto-encephalography (MEG) and also EEG will be used to characterize dynamic patterns of neural activity that can be related to structural and functional connectivity maps. Imaging will benefit from a customized 3T scanner developed for this project and ultimately installed at Washington University, a new 7T scanner at the University of Minnesota, and improved pulse sequences and custom coils to be implemented during the project's optimization phase. By scanning all subjects at 3T and subsets at 7T and with MEG, the complementary strengths of each imaging modality will be utilized and the overall impact of the data collection and analysis strategy will be maximized. Consortium members have contributed greatly to the recent progress in data acquisition and analysis strategies that make the Human Connectome Project technically feasible. Major additional advances anticipated during the project's optimization phase will lead to unprecedented fidelity of the structural and functional connectivity maps to be obtained during the production phase.
Successful execution of this vision will have a transformative impact on our understanding of the human brain. It will pave the way for follow-up studies that examine how brain circuitry changes during the normal lifespan and how it differs in various neurological and psychiatric disorders and conditions.
|Cole, Michael W; Yang, Genevieve J; Murray, John D et al. (2016) Functional connectivity change as shared signal dynamics. J Neurosci Methods 259:22-39|
|Hearne, Luke J; Mattingley, Jason B; Cocchi, Luca (2016) Functional brain networks related to individual differences in human intelligence at rest. Sci Rep 6:32328|
|Glasser, Matthew F; Coalson, Timothy S; Robinson, Emma C et al. (2016) A multi-modal parcellation of human cerebral cortex. Nature 536:171-8|
|Jakobsen, Estrid; BÃ¶ttger, Joachim; Bellec, Pierre et al. (2016) Subdivision of Broca's region based on individual-level functional connectivity. Eur J Neurosci 43:561-71|
|Takemura, Hiromasa; Caiafa, Cesar F; Wandell, Brian A et al. (2016) Ensemble Tractography. PLoS Comput Biol 12:e1004692|
|Braga, Rodrigo M; Fu, Richard Z; Seemungal, Barry M et al. (2016) Eye Movements during Auditory Attention Predict Individual Differences in Dorsal Attention Network Activity. Front Hum Neurosci 10:164|
|Kim, Joo-won; Naidich, Thomas P; Ely, Benjamin A et al. (2016) Human habenula segmentation using myelin content. Neuroimage 130:145-56|
|Yeh, Fang-Cheng; Badre, David; Verstynen, Timothy (2016) Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage 125:162-71|
|Kasenburg, Niklas; Liptrot, Matthew; Reislev, Nina Linde et al. (2016) Training shortest-path tractography: Automatic learning of spatial priors. Neuroimage 130:63-76|
|Roe, Catherine M; Barco, Peggy P; Head, Denise M et al. (2016) Amyloid Imaging, Cerebrospinal Fluid Biomarkers Predict Driving Performance Among Cognitively Normal Individuals. Alzheimer Dis Assoc Disord :|
Showing the most recent 10 out of 205 publications