Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial 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 spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by β-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 subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.
Bibliographical noteFunding Information:
We thank the anonymous reviewers for their constructive comments. This research was partially funded by the Defense Advanced Research Projects Agency (DARPA) under cooperative agreement W911NF-14-2-0043 issued by the Army Research Office contracting office in support of DARPA’s SUBNETS program. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the United States government. This work was also supported by a Swartz Foundation postdoctoral fellowship 127713C (to L.A.K).
© 2018 Elsevier Inc.
- brain networks
- human brain
- intracranial electroencephalography
- machine learning