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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Aug 11, 2025

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

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Published on: July 7, 2023

Decoding Depression Severity From Intracranial Neural Activity.

Jiayang Xiao¹, Nicole R Provenza², Joseph Asfouri³

¹Department of Neurosurgery, Baylor College of Medicine, Houston, Texas; Department of Neuroscience, Baylor College of Medicine, Houston, Texas.

Biological Psychiatry

|February 3, 2023

Summary

This summary is machine-generated.

Researchers decoded depression severity from brain activity in patients with severe depression. Specific patterns of neural activity in prefrontal regions, particularly the anterior cingulate cortex, correlate with symptom severity.

Keywords:

Anterior cingulate cortex Biomarker Decoding Depression Intracranial recording Spatiospectral features

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Published on: October 4, 2016

Area of Science:

Neuroscience
Psychiatry
Computational Biology

Background:

Mood and cognitive disorders, such as depression, are widespread and challenging to treat.
A key obstacle in treatment is the limited understanding of the neurophysiological underpinnings of these conditions.

Purpose of the Study:

To investigate the neurophysiological basis of depression by decoding symptom severity from neural activity.
To identify potential neural signatures for personalized neuromodulation therapies.

Main Methods:

High-density neural activity was recorded using intracranial electrodes in prefrontal cortical regions of three individuals with severe depression.
Neural recordings were correlated with depression severity scores obtained through an adaptive assessment.
Regularized regression techniques with region selection were employed to decode depression severity from the recorded neural data.

Main Results:

Reduced depression severity correlated with decreased low-frequency and increased high-frequency neural activity across prefrontal regions.
Spectral changes in the anterior cingulate cortex were the strongest predictors of depression severity when considering a single region.
Individual-specific spatiospectral features predicted symptom severity, highlighting the heterogeneity of depression.

Conclusions:

Decoding depression severity from neural activity enhances understanding of its manifestation in the brain.
Identified neural signatures offer potential targets for personalized neuromodulation treatments.