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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
3U54MH091657-03S1
Application #
8658893
Study Section
Special Emphasis Panel (ZMH1-ERB-C (04))
Program Officer
Farber, Gregory K
Project Start
2010-09-15
Project End
2015-08-31
Budget Start
2013-06-01
Budget End
2013-08-31
Support Year
3
Fiscal Year
2013
Total Cost
$354,625
Indirect Cost
$13,000
Name
Washington University
Department
Neurosciences
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Raemaekers, Mathijs; Schellekens, Wouter; Petridou, Natalia et al. (2018) Knowing left from right: asymmetric functional connectivity during resting state. Brain Struct Funct 223:1909-1922
Gray, Joshua C; Owens, Max M; Hyatt, Courtland S et al. (2018) No evidence for morphometric associations of the amygdala and hippocampus with the five-factor model personality traits in relatively healthy young adults. PLoS One 13:e0204011
Robinson, Emma C; Garcia, Kara; Glasser, Matthew F et al. (2018) Multimodal surface matching with higher-order smoothness constraints. Neuroimage 167:453-465
Liu, Chihuang; JaJa, Joseph; Pessoa, Luiz (2018) LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data. Neuroimage 169:363-373
Slopsema, Julia P; Peña, Edgar; Patriat, Remi et al. (2018) Clinical deep brain stimulation strategies for orientation-selective pathway activation. J Neural Eng 15:056029
Amico, Enrico; Goñi, Joaquín (2018) The quest for identifiability in human functional connectomes. Sci Rep 8:8254
Guell, Xavier; Schmahmann, Jeremy D; Gabrieli, John DE et al. (2018) Functional gradients of the cerebellum. Elife 7:
Aganj, Iman (2018) Automatic Verification of the Gradient Table in Diffusion-Weighted MRI Based on Fiber Continuity. Sci Rep 8:16541
Archer, Derek B; Coombes, Stephen A; Chu, Winston T et al. (2018) A widespread visually-sensitive functional network relates to symptoms in essential tremor. Brain 141:472-485
Du, Haixiao; Xia, Mingrui; Zhao, Kang et al. (2018) PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data. Hum Brain Mapp 39:1869-1885

Showing the most recent 10 out of 380 publications