Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.
The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks.
Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Georg
G
Langs
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Danhong
D
Wang
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Polina
P
Golland
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA Ludwig Maximilian University Munich, Institute of Clinical Radiology, Munich, Germany.
Sophia
S
Mueller
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA Department of Radiology.
Ruiqi
R
Pan
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Mert R
MR
Sabuncu
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Wei
W
Sun
Department of Radiology.
Kuncheng
K
Li
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Hesheng
H
Liu
D000465:Algorithms; D000076103:Biological Variation, Individual; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000502:physiology*; D016000:Cluster Analysis; D063132:Connectome; D005260:Female; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007802:Language; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000502:physiology*; D012146:Rest
functional parcellation; individual differences; resting-state fMRI