Stolz, L.A.; Kohn, J.N.; Smith, S.E.; Benster, L.L.; Appelbaum, L.G. Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci.2023, 13, 1570.
Stolz, L.A.; Kohn, J.N.; Smith, S.E.; Benster, L.L.; Appelbaum, L.G. Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci. 2023, 13, 1570.
Stolz, L.A.; Kohn, J.N.; Smith, S.E.; Benster, L.L.; Appelbaum, L.G. Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci.2023, 13, 1570.
Stolz, L.A.; Kohn, J.N.; Smith, S.E.; Benster, L.L.; Appelbaum, L.G. Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci. 2023, 13, 1570.
Abstract
Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain’s electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Specifically, hierarchical regression modeling was used to predict treatment response from baseline demographics, symptom severity, and resting-state EEG features in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). Across models, both age and baseline symptom severity, assessed by the Beck’s Depression Inventory, were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance (p~0.07 in multiple models). These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.
Keywords
depression; predictive biomarkers; electroencephalography; transcranial magnetic stimulation
Subject
Biology and Life Sciences, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
