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

Year: 2026
Type: article
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 two 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 interindividual FC differences rather than reproducible group-level dynamic brain states.
DOI: 10.1162/netn.a.602
Language: English
Open Access status: gold
Estimated APC paid: $1,750
OpenAlex ID: W7171969607
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