Summary: Changes in brain activity in the anterior cingulate cortex may be the best predictor of depression severity.
Sourcee : Elsevier
Clinical depression is a common psychiatric condition with often devastating consequences.
A new study In Biological psychiatry advances our fundamental understanding of the neural circuitry of depression in the human brain.
The treatment of depression is complicated by the great heterogeneity and notable complexity of the disease. Drugs to treat depression are available, but a third of patients do not respond to these first-line drug treatments.
Other treatments such as deep brain stimulation (DBS) can provide substantial relief to patients, but previous results have been inconsistent. The development of more personalized treatments and better outcomes requires a better understanding of the neurophysiological mechanisms of depression.
Led by Sameer Sheth, MD, PhD, at Baylor College of Medicine, along with Wayne Goodman, MD, and Nader Pouratian, MD, PhD, the researchers collected electrophysiological recordings of the prefrontal cortical regions in three human subjects, all of whom underwent severe treatment – resistant depression.
The prefrontal cortex plays an important role in psychiatric and cognitive disorders, influencing its ability to set goals and form habits. These highly evolved brain regions are particularly difficult to study in non-human models, so data collected on human brain activity is particularly valuable.
The researchers made electrophysiological recordings of neural activity from the surface of the brain using implanted intracranial electrodes, and they measured the severity of each participant’s depression for nine days. The patients were undergoing brain surgery as part of a feasibility study for DBS treatment.
The researchers found that lower depression severity correlated with decreased low-frequency neural activity and increased high-frequency activity. They also found that changes in the anterior cingulate cortex (ACC) were the best predictor of depression severity.
Beyond the ACC, and consistent with the diverse nature of depression pathways and symptoms, they also identified sets of individual-specific characteristics that successfully predicted severity.
“In order to use neuromodulation techniques to treat complex psychiatric or neurological disorders, we ideally need to understand their underlying neurophysiology,” Dr. Sheth said.
“We are excited to have made initial progress in understanding how mood is encoded in human prefrontal circuitry. As such data becomes available, we hope to be able to identify common patterns across individuals. and which ones are specific. This information will be critical in the design and personalization of next-generation therapies for depression such as DBS.”
John Krystal, MD, editor of Biological psychiatry, said of this work: “We now have a growing body of approaches that can be applied to circuit mapping and characterization of the neural codes underlying depression. This knowledge will guide next-generation brain stimulation treatments and inform how we understand and treat depression, in general.
About this depression research
Author: Eileen Leahy
Contact: Eileen Leahy – Elsevier
Picture: Image is in public domain
Original research: Free access.
“Decoding Depression Severity from Intracranial Neural Activity” by Sameer Sheth et al. Biological psychiatry
Decoding Depression Severity from Intracranial Neural Activity
Mood and cognition disorders are widespread, debilitating and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis.
We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in three human subjects with severe depression. Neural recordings were tagged with depression severity scores over a wide dynamic range using adaptive rating that allowed sampling with a higher temporal frequency than possible with typical rating scales. We modeled these data using region-selection regularized regression techniques to decode depression severity from prefrontal recordings.
In all prefrontal regions, we found that a reduction in depression severity is associated with a decrease in low-frequency neural activity and an increase in high-frequency activity. Constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all three subjects. Relaxing this constraint revealed unique, individual-specific sets of spatio-spectral features predictive of symptom severity, reflecting the heterogeneous nature of depression.
The ability to decode the severity of depression from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.