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Updated: Sep 30, 2025

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Decoding naturalistic affective behaviour from spectro-spatial features in multiday human iEEG.

Maryam Bijanzadeh¹, Ankit N Khambhati¹, Maansi Desai²

¹Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.

Nature Human Behaviour

|March 11, 2022

Summary

This summary is machine-generated.

Brain activity in the mesolimbic network predicts everyday affective behaviors. Specific electrical patterns in areas like the insula and amygdala differentiate positive, negative, and neutral states.

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Area of Science:

Neuroscience
Affective Science
Computational Psychiatry

Background:

The neural underpinnings of everyday affective behaviors remain largely unknown.
Understanding these mechanisms is crucial for diagnosing and treating affective disorders.

Purpose of the Study:

To investigate the neurological basis of spontaneous affective behaviors in daily life.
To identify brain activity patterns associated with positive, negative, and neutral affective states.

Main Methods:

Continuous intracranial electroencephalography (iEEG) recordings from the human mesolimbic network in 11 epilepsy patients.
Hand-annotated behavioral data from over 100 hours of video recordings.
Machine learning models (random forests) to decode affective states from neural activity.

Main Results:

Machine learning models accurately decoded affective from neutral behaviors (up to 93% accuracy).
Both positive and negative affective states showed altered high- and low-frequency brain activity in the mesolimbic network.
The insula and anterior cingulate cortex were key in distinguishing affective from non-affective behaviors.

Conclusions:

Spectro-spatial features of brain activity within the mesolimbic network are significantly associated with everyday affective behaviors.
Specific brain regions, notably the insula and ACC, play critical roles in processing affect.
This research provides a foundation for understanding the neural basis of emotion regulation in real-world contexts.