The Human Connectome Project's neuroimaging approach

• Published in: Nature neuroscience Vol. 19; no. 9; pp. 1175 - 1187
• Main Authors: Glasser, Matthew F, Smith, Stephen M, Marcus, Daniel S, Andersson, Jesper L R, Auerbach, Edward J, Behrens, Timothy E J, Coalson, Timothy S, Harms, Michael P, Jenkinson, Mark, Moeller, Steen, Robinson, Emma C, Sotiropoulos, Stamatios N, Xu, Junqian, Yacoub, Essa, Ugurbil, Kamil, Van Essen, David C
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-09-2016; Nature Publishing Group
• Subjects: 59; 59/57; 631/378/2649/1594; 631/378/3917; 631/378/3920; Animal Genetics and Genomics; Behavioral Sciences; Biological Techniques; Biomedicine; Brain - diagnostic imaging; Brain - physiology; Care and treatment; Connectome - methods; Connectome - trends; Diagnosis; Diagnostic imaging; Humans; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - trends; Mental disorders; Methods; Neurobiology; Neuroimaging; Neuroimaging - methods; Neuroimaging - trends; Neurosciences; Quality management; review-article
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/nn.4361

This paper describes an integrated approach for neuroimaging data acquisition, analysis and sharing. Building on methodological advances from the Human Connectome Project (HCP) and elsewhere, the HCP-style paradigm applies to new and existing data sets that meet core requirements and may accelerate progress in understanding the brain in health and disease.
Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease.