Characterization of task-free and task-performance brain states via functional connectome patterns

The flowchart of our computational framework for characterization of task-free and task-performance brain states via functional connectome patterns. (1) Resting state (R-fMRI)/task-based fMRI (T-fMRI) signal extraction for each DICCCOL (Dense Individualized and Common connectivity-based Cortical Landmarks (DICCCOL), Zhu et al., 2013) landmark; (2) Construction of functional connectomes; (3) Interactive segmentation of WQCPs (whole-brain quasi-stable connectome patterns) training samples; (4) FDDL (Fisher discriminative dictionary learning) sparse learning and classification. (5) Examination of the distributions of atomic connectome patterns (ACPs) at the individual and population levels.