Whole-brain <i>k</i> -means clustering of dynamic functional connectivity is dominated by individual-participant static connectivity in temporal lobe epilepsy and controls

Abstract Dynamic functional connectivity (FC) analyses of resting-state functional magnetic resonance imaging (fMRI) commonly apply k-means clustering to time-resolved whole-brain connectivity matrices to identify recurring “brain states” presumed to reflect shared neural dynamics. However, whether this approach reliably identifies group-level states in relatively small and heterogeneous clinical cohorts remains unclear. Here, we systematically evaluated k-means clustering in temporal lobe epilepsy (TLE) subgroups (bilateral, left, and right TLE) and healthy controls by incrementally increasing the number of clusters (k) from 2 to the number of participants in each group. Across all groups, clustering solutions converged toward participant-specific rather than shared group-level FC patterns. Identified FC states increasingly resembled participants’ static FC matrices, with higher k values approaching near one-to-one correspondence between states and participants. Elbow and silhouette analyses failed to identify a consistent optimal k and generally favored larger clustering solutions, consistent with increasing participant-specific structure. State occupancy analyses further showed prolonged residence within a single dominant state, providing limited evidence for shared dynamic transitions. These findings persisted under a leave-same-participant-out condition designed to reduce participant-specific influence. Together, these findings suggest that whole-brain k-means clustering in small clinical cohorts may primarily capture inter-individual FC differences rather than reproducible group-level dynamic brain states.