Population-averaged atlas of the macroscale human structural connectome and its network topology

A comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N = 842). This was achieved by creati...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2018-09, Vol.178, p.57-68
Hauptverfasser: Yeh, Fang-Cheng, Panesar, Sandip, Fernandes, David, Meola, Antonio, Yoshino, Masanori, Fernandez-Miranda, Juan C., Vettel, Jean M., Verstynen, Timothy
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Sprache:eng
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Zusammenfassung:A comprehensive map of the structural connectome in the human brain has been a coveted resource for understanding macroscopic brain networks. Here we report an expert-vetted, population-averaged atlas of the structural connectome derived from diffusion MRI data (N = 842). This was achieved by creating a high-resolution template of diffusion patterns averaged across individual subjects and using tractography to generate 550,000 trajectories of representative white matter fascicles annotated by 80 anatomical labels. The trajectories were subsequently clustered and labeled by a team of experienced neuroanatomists in order to conform to prior neuroanatomical knowledge. A multi-level network topology was then described using whole-brain connectograms, with subdivisions of the association pathways showing small-worldness in intra-hemisphere connections, projection pathways showing hub structures at thalamus, putamen, and brainstem, and commissural pathways showing bridges connecting cerebral hemispheres to provide global efficiency. This atlas of the structural connectome provides representative organization of human brain white matter, complementary to traditional histologically-derived and voxel-based white matter atlases, allowing for better modeling and simulation of brain connectivity for future connectome studies.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2018.05.027