Back

Aperiodic neural activity is a biomarker for depression severity.

Hacker, C.; Mocchi, M. M.; Xiao, J.; Metzger, B.; Adkinson, J.; Pascuzzi, B.; Mathura, R.; Oswalt, D.; Watrous, A.; Bartoli, E.; Allawala, A.; Pirtle, V.; Fan, X.; Danstrom, I.; Shofty, B.; Banks, G.; Zhang, Y.; Armenta-Salas, M.; Mirpour, K.; Provenza, N.; Mathew, S.; Cohn, J.; Borton, D.; Goodman, W.; Pouratian, N.; Sheth, S.; Bijanki, K.

2023-11-08 psychiatry and clinical psychology
10.1101/2023.11.07.23298040 medRxiv
Show abstract

A reliable physiological biomarker for Major Depressive Disorder (MDD) is necessary to improve treatment success rates by shoring up variability in outcome measures. In this study, we establish a passive biomarker that tracks with changes in mood on the order of minutes to hours. We record from intracranial electrodes implanted deep in the brain - a surgical setting providing exquisite temporal and spatial sensitivity to detect this relationship in a difficult-to-measure brain area, the ventromedial prefrontal cortex (VMPFC). The aperiodic slope of the power spectral density captures the balance of activity across all frequency bands and is construed as a putative proxy for excitatory/inhibitory balance in the brain. This study demonstrates how shifts in aperiodic slope correlate with depression severity in a clinical trial of deep brain stimulation for treatment-resistant depression (TRD). The correlation between depression severity scores and aperiodic slope is significant in N=5 subjects, indicating that flatter (less negative) slopes correspond to reduced depression severity, especially in the ventromedial prefrontal cortex. This biomarker offers a new way to track patient response to MDD treatment, facilitating individualized therapies in both intracranial and non-invasive monitoring scenarios. One sentence summaryThe aperiodic component of the power spectral density robustly tracks depression severity on the order of minutes to hours.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.