Abstract:

Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracra- nial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects’ self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spec- tral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by b-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved sub- network associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.