Individual differences in functional connectivity during naturalistic viewing conditions

Abstract

Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and task-evoked BOLD-signal changes. These task-evoked changes result in cortical activity that is synchronized across subjects and involves large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in functional connectivity (FC) across two distinct movie conditions and eyes-open rest (n=34 healthy adults, 2 scan sessions each). At the whole-brain level, we found that movies have higher intra- and inter-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-subject variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching (or \"finger-printin\") algorithm that identifies individual subjects from within a group based on functional connectivity patterns, quantifying the accuracy of the algorithm across the three conditions. We also evaluated the impact of parcellation resolution, scan duration, and number of edges on observed inter-individual differences. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 62 and 100%. Overall, pairings involving movies outperformed rest, and the more social and faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are distinct at the individual level.\n\nHighlightsO_LIIntra- and inter-subject FC correlations are compared across rest and movies.\nC_LIO_LIMovies outperformed rest in an unsupervised identification algorithm based on FC.\nC_LIO_LIMovies outperform rest regardless of parcellation, scan length, or number of edges.\nC_LIO_LIWatching movies enhances the detection of individual differences in FC.\nC_LI