An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood

Area of Science:

• Neuroscience
• Computational Psychiatry
• Systems Neuroscience

Background:

• Understanding brain networks that regulate mood is crucial for mental health research.
• Naturalistic mood variations and their neural underpinnings are not well-understood.

Purpose of the Study:

• To identify human brain subnetworks associated with daily mood fluctuations.
• To explore the relationship between brain network dynamics and psychological traits like anxiety.

Main Methods:

• Utilized multi-site, semi-chronic intracranial electroencephalography (iEEG) from the human limbic system.
• Applied machine learning to analyze spectral coherence and identify brain subnetworks.
• Correlated subnetwork variability with self-reported mood and trait anxiety levels.

Main Results:

• A common subnetwork characterized by beta-frequency (13-30 Hz) coherence between the amygdala and hippocampus was identified in 13/21 subjects.
• Increased variability in this amygdala-hippocampus subnetwork correlated with worsening mood.
• Subjects with this subnetwork exhibited higher trait anxiety compared to those without it.

Conclusions:

• A conserved amygdala-hippocampus brain subnetwork is linked to mood regulation and trait anxiety.
• The study demonstrates a method for extracting network-behavior relationships from complex iEEG data.
• This finding offers insights into the neural basis of mood variability and anxiety.