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.

Introduction

Schnitzler and Gross, 2005 Schnitzler A.

Gross J. Normal and pathological oscillatory communication in the brain. Although these studies have revealed fundamental insights into the neural networks of emotion, they suffer from two major limitations. First, non-invasive imaging is restricted to short recording periods (1 to 2 hr) and often requires averaging across measurements to detect effects. As a result, these experiments cannot identify real-time neural correlates for slower changes in emotional state, such as changes in mood, that evolve over hours or days. Second, fMRI and PET are both indirect measures of neural activity with low temporal resolution on the order of seconds. As such, they cannot resolve sub-second oscillatory brain activity, which is thought to underlie information processing and cognitive function (). Thus, almost nothing is known about how rapid-timescale interactions between limbic brain regions contribute to changes in mood.

To address these two questions, we took advantage of a unique dataset: multi-site, semi-chronic intracranial electroencephalography (iEEG) recordings from the human limbic system, collected over several days as participants periodically rated their mood. These recordings were performed in patients with epilepsy for the primary clinical purpose of seizure localization and treatment.

Phelps and LeDoux, 2005 Phelps E.A.

LeDoux J.E. Contributions of the amygdala to emotion processing: from animal models to human behavior. Bickart et al., 2014 Bickart K.C.

Dickerson B.C.

Barrett L.F. The amygdala as a hub in brain networks that support social life. Figure S1 Electrode Locations, Related to Figure 1 Show full caption (A) Sagittal views of electrode locations for each individual subject in native coordinates. STC: subtemporal cortex (blue); AMY: amygdala (magenta); HPC: hippocampus (orange); OFC: orbitofrontal cortex (purple); CIN: cingulate cortex (red); INS: insular cortex (yellow). (B) Sagittal view of the centroid locations of AMY (magenta) and HPC (orange) electrodes superimposed onto reconstructed brain in MNI coordinate space for subjects with and without β-AH ICN. The overlapping centroids within each brain region for each group indicate that no marked differences in electrode locations between these two groups exists. Given that the amygdala is a major hub in brain networks that support emotional processing (), we postulated that amygdala-containing subnetworks would also underlie mood variation. Subjects in our study were thus selected on the basis of amygdala electrode coverage in addition to three or more other limbic regions that are connected to the amygdala (). These included the hippocampus (ventral), cingulate cortex (inferior or superior), insular cortex, orbitofrontal cortex (anterior or posterior), and subtemporal cortex (anterior, middle, or posterior). However, not all subjects had electrode coverage in all of these regions because this was determined by clinical needs ( Figure S1 Table S1 ).

Because iEEG measures neural activity directly from the brain, these recordings offer the potential to detect fast oscillatory network interactions that correlate in real time with changes in mood. However, they also have an inherent complication: because the number of network interactions is much greater than the number of mood ratings from a subject, there is a risk of detecting spurious correlations. To overcome this risk, we first used pattern recognition techniques (unsupervised machine learning) to define subnetworks that represent dominant interactions across the brain regions defined above. We next used multivariate regression (supervised machine learning) to determine how activity within these subnetworks related to subjects’ mood ratings. Our study identifies a conserved subnetwork, driven by β-frequency (13–30 Hz) interactions between the amygdala (AMY) and hippocampus (HPC) that was present in 62% (13 of 21) of study subjects. Furthermore, when present, increased temporal variance of activity within this AMY-HPC β-frequency subnetwork consistently predicted worsening mood.