Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics

• Published in: Human brain mapping Vol. 42; no. 5; pp. 1416 - 1433
• Main Authors: Fan, Liangwei, Zhong, Qi, Qin, Jian, Li, Na, Su, Jianpo, Zeng, Ling‐Li, Hu, Dewen, Shen, Hui
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01-04-2021
• Subjects: Adult; Atlases as Topic; Brain; Brain mapping; brain network; Cerebellum - diagnostic imaging; Cerebellum - physiology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiology; Cognitive ability; Computational neuroscience; Connectome - methods; Connectome - standards; Default Mode Network - diagnostic imaging; Default Mode Network - physiology; dynamic functional connectivity; functional connectivity degree; Functional magnetic resonance imaging; Functional morphology; Homogeneity; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Independent component analysis; Intelligence; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Nerve Net - diagnostic imaging; Nerve Net - physiology; Nervous system; Neural networks; Neuroimaging; Neurosciences; resting‐state fMRI; Support Vector Machine; Time Factors; dynamic functional connectivity; independent component analysis; resting-state fMRI; brain network; functional connectivity degree
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.25303

Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time‐varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA‐driven parcellation and random parcellation, demonstrated that the ROI‐definition strategy based on the proposed dFC‐driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience.
We presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations for capturing functional homogeneous regions in temporal variance of functional connectivity.