Resume: 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 circuits of depression in the human brain.
The treatment of depression is complicated by the high heterogeneity and remarkable complexity of the disease. Medications to treat depression are available, but one-third of patients do not respond to these first-line drug treatments.
Other treatments such as deep brain stimulation (DBS) can provide patients with significant relief, but previous results have been inconsistent. The development of more personalized treatments and improved 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 from prefrontal cortical areas in three subjects, all of whom had undergone severe treatment. underwent. resistant depression.
The prefrontal cortex plays an important role in psychiatric and cognitive disorders, affecting a person’s 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 over nine days. The patients underwent brain surgery as part of a feasibility study for treatment with DBS.
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) served as the best predictive area of depression severity.
In addition to the ACC, and consistent with the diverse nature of the pathways and symptoms of depression, they also identified individual-specific sets of traits that successfully predicted severity.
“Ideally, to use neuromodulation techniques to treat complex psychiatric or neurological conditions, we need to understand their underlying neurophysiology,” said Dr. Sheth.
“We are thrilled to have made the first advances in understanding how mood is encoded in human prefrontal circuits. As more such data becomes available, we will hopefully be able to identify which patterns are common among individuals and which are specific. This information will be critical in designing and customizing next-generation therapies for depression, such as DBS.”
John Krystal, MD, editor of Biological psychiatrysaid of the work: “We now have a growing collection of approaches that can be applied to map the circuitry and characterize the neural codes underlying depression. This knowledge will guide the next generation of brain stimulation treatments and provide information about the way we understand and treat depression broadly.”
About this news about depression research
Author: Eileen Leahy
Source: Elsevier
Contact: Eileen Leahy-Elsevier
Image: The image is in the public domain
Original research: Open access.
“Deciphering depression severity from intracranial neural activity” by Sameer Sheth et al. Biological psychiatry
Abstract
Decoding depression severity from intracranial neural activity
Background
Mood and cognition disorders are widespread, disabling and notoriously difficult to treat. Fueling this treatment challenge represents a significant gap in our understanding of their neurophysiological basis.
methods
We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in three human subjects with major depression. Neural recordings were labeled with depression severity scores over a wide dynamic range using an adaptive rating that allowed sampling at a temporal frequency higher than is possible with typical rating scales. We modeled this data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings.
Results
In all prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When 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 that predict symptom severity, reflecting the heterogeneous nature of depression.
Conclusions
The ability to decipher depression severity from neural activity advances our fundamental understanding of how depression manifests in the human brain and provides a neural target signature for personalized neuromodulation therapies.