Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

Abstract

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, has been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group-level, which might neglect meaningful individual differences. Here, we estimate individual CAP states at both subject- and scan-level based on a densely-sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and intersubject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best discrimination ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12~15 mins/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 mins). Overall, this work has constructed reliable subject-specific CAP states with meaningful individual differences and provides a starting point for the subsequent applications of individual brain dynamics.