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Changes in Anterior Cingulate Cortex May Predict Depression Severity
New research has suggested that spectral changes in the anterior cingulate cortex (ACC) may be a key region in the prefrontal cortex (PFC) for predicting depression severity. Findings were published in Biological Psychiatry.
“Finding neurophysiological features that characterize and even predict symptom severity is critical for improving our understanding of and developing precise treatments for depression,” wrote first author Jiayang Xiao, PhD student, department of neurosurgery, Baylor College of Medicine, Houston, Texas, and co-authors.
“Here, we accurately decoded fluctuations in depression severity over time from intracranially recorded neural activity in 3 patients with treatment-resistant depression (TRD). We found that decreased low-frequency power and increased high-frequency power in the prefrontal cortex correlate with lower depression severity.”
The research team aimed to chart the neurophysiological mechanisms underlying psychiatric and cognitive disorders using electrophysiological recordings and stimulation with intracranial electrodes. For this study, they utilized data from 3 subjects with TRD who were enrolled in an early feasibility trial for individualized deep brain stimulation informed by intracranial recordings. Each individual had permanent deep brain stimulation leads for stimulation delivery as well as temporary stereoelectroencephalography (SEEG) electrodes for neural recordings implanted into their brains.
Subjects were monitored in an in-patient setting for 9 days. The authors measured depression severity using the Computerized Adaptive Test—Depression Inventory (CAT-DI). They also recorded neural signals from the prefrontal SEEG electrode contacts during CAT-DI administration. To examine neural activity variance, the authors “extracted spectral power from 6 frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (35–50 Hz), and high-gamma (70–150 Hz), yielding 6 spectral power features per channel for each depression severity measurement.”
After analysis, the authors found that the spectral power features in the prefrontal channels correlated strongly with the severity of symptoms. Despite individualized cases of TRD across the 3 subjects, power in low frequencies (including delta, theta, alpha, and beta) was positively correlated with symptom severity (92.0% in patient 1, 99.2% in patient 2, and 88.5% in patient 3). Power in high frequencies (including gamma and high-gamma) was negatively correlated with symptom severity (92.9% in patient 1, 93.2% in patient 2, and 100.0% in patient 3). In all 3 patients, at moments when symptoms were less severe, a majority of the brain regions exhibited decreased low-frequency power and increased high-frequency power.
Researchers then utilized the recorded spatiospectral features to train a regularized regression model to predict depression severity and identify potential predictive features. The “transparency and explainability” of the model indicated that the ACC, alongside some individual-specific neurophysiological features across the PCF, were the most significant subregions for revealing depression severity in the 3 patients.
“We consider these feature sets to be personalized neural biomarkers for depression severity,” the authors concluded in the study discussion.
The study was funded through the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative and made possible by an investigation device exemption granted by the US Food and Drug Administration.
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