summary: Changes in brain activity in the anterior cingulate cortex may be the best predictors of depression severity.
saucee: Elsevier
Clinical depression is a common psychiatric condition with often devastating consequences.
a new research of biological psychiatry To develop a basic understanding of depression neural circuitry in the human brain.
Treatment of depression is complex because the disease is highly heterogeneous and remarkably complex. Medications are available to treat depression, but her third of patients do not respond to these first-line medications.
Other treatments, such as deep brain stimulation (DBS), can provide substantial relief to patients, but results to date have been inconsistent. A better understanding of the neurophysiological mechanisms of depression is needed to develop more individualized treatments and improve outcomes.
Together with Wayne Goodman, M.D., and Nader Pourathian, M.D., led by Samir Sheth, M.D., Baylor College of Medicine, the researchers collected electrophysiological recordings from the prefrontal cortical regions of three subjects, all of whom underwent severe I received treatment. – Resistant Depression.
The prefrontal cortex plays a key role in psychiatric and cognitive disorders, influencing our ability to set goals and form habits. Data collected from human brain activity are particularly valuable because these highly evolved brain regions are particularly difficult to study in non-human models.
The researchers used implanted intracranial electrodes to make electrophysiological recordings of neural activity from the surface of the brain and measured each participant’s severity of depression for nine days. The patient had undergone brain surgery as part of a feasibility study of treatment with DBS.
The researchers found that less severe depression correlated with decreased low-frequency neural activity and increased high-frequency neural activity. They also found changes in the anterior cingulate cortex (ACC) to be the best predictor of depression severity.
Beyond the ACC, in keeping with the diverse nature of depression pathways and symptoms, they also identified a set of individual-specific features that successfully predicted severity.
“Using neuromodulation techniques to treat complex psychiatric or neurological disorders ideally requires an understanding of their underlying neurophysiology,” said Dr. Sheth.
“We are excited to have made the first advances in understanding how mood is encoded in the human prefrontal cortex circuitry. We hope that we can identify which patterns are common among individuals and which are specific. This information will be important in designing and individualizing next generation depression treatments such as DBS. is.”
John Krystal, MD, Editor biological psychiatrysaid of the study: This knowledge will guide the next generation of brain stimulation therapy and inform the way we understand and treat depression. ”
About this Depression Research News
author: Eileen Leahy
sauce: Elsevier
contact: Eileen Leahy – Elsevier
image: image is public domain
Original research: open access.
“Decoding depression severity from intracranial neural activity” by Samir Sheth et al. biological psychiatry
overview
Decoding depression severity from intracranial neural activity
Background
Mood and cognitive disorders are prevalent, notoriously disabling and difficult to treat. Spurring this challenge in therapy are the major gaps in our understanding of their neurophysiological underpinnings.
method
We recorded high-density neural activity from intracranial electrodes implanted in depression-related prefrontal cortical regions in three subjects with severe depression. Neural recordings were labeled with depression severity scores over a wide dynamic range using an adaptive rating that allows sampling at higher temporal frequencies than is possible with typical rating scales. To decipher depression severity from prefrontal cortex recordings, we modeled these data using a regularized regression approach with region selection.
result
Across the prefrontal cortex, we found that decreased depression severity was associated with decreased low-frequency neural activity and increased high-frequency neural activity. When the model was constrained to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all three of her subjects. Relaxing this constraint revealed a unique individual-specific set of spatial-spectral features that predict symptom severity, reflecting the heterogeneous nature of depression.
Conclusion
The ability to decipher depression severity from neural activity will advance our fundamental understanding of how depression manifests itself in the human brain and provide targeted neural signatures for personalized neuromodulation therapy. To do.
