Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

• Main Authors: Xuetong Wang, Qiongling Li, Yan Zhao, Yirong He, Baoqiang Ma, Zhenrong Fu, Shuyu Li
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: NeuroImage
• Subjects: RSFC; Individual difference; Individual-specific connectivity; Predict behavioral scores; Fingerprint analysis
• Online Access: http://www.sciencedirect.com/science/article/pii/S1053811921005292

Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.