Predictive Biomarkers of Treatment Response in Major Depressive Disorder
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 electroe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/13/11/1570 |
| _version_ | 1827640393945055232 |
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| author | Louise A. Stolz Jordan N. Kohn Sydney E. Smith Lindsay L. Benster Lawrence G. Appelbaum |
| author_facet | Louise A. Stolz Jordan N. Kohn Sydney E. Smith Lindsay L. Benster Lawrence G. Appelbaum |
| author_sort | Louise A. Stolz |
| collection | DOAJ |
| description | 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. Logistic regression modeling was used to predict treatment response, defined as at least a 50% improvement on the Beck’s Depression Inventory, in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). The results show that both age and baseline symptom severity 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. These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies. |
| first_indexed | 2024-03-09T16:59:04Z |
| format | Article |
| id | doaj.art-e63debfcb81c4d1b8c064d08b7aaba85 |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-09T16:59:04Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-e63debfcb81c4d1b8c064d08b7aaba852023-11-24T14:32:46ZengMDPI AGBrain Sciences2076-34252023-11-011311157010.3390/brainsci13111570Predictive Biomarkers of Treatment Response in Major Depressive DisorderLouise A. Stolz0Jordan N. Kohn1Sydney E. Smith2Lindsay L. Benster3Lawrence G. Appelbaum4Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USADepartment of Psychiatry, University of California San Diego, La Jolla, CA 92093, USADepartment of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USADepartment of Psychiatry, University of California San Diego, La Jolla, CA 92093, USADepartment of Psychiatry, University of California San Diego, La Jolla, CA 92093, USAMajor 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. Logistic regression modeling was used to predict treatment response, defined as at least a 50% improvement on the Beck’s Depression Inventory, in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). The results show that both age and baseline symptom severity 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. These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.https://www.mdpi.com/2076-3425/13/11/1570depressionpredictive biomarkerselectroencephalographytranscranial magnetic stimulation |
| spellingShingle | Louise A. Stolz Jordan N. Kohn Sydney E. Smith Lindsay L. Benster Lawrence G. Appelbaum Predictive Biomarkers of Treatment Response in Major Depressive Disorder Brain Sciences depression predictive biomarkers electroencephalography transcranial magnetic stimulation |
| title | Predictive Biomarkers of Treatment Response in Major Depressive Disorder |
| title_full | Predictive Biomarkers of Treatment Response in Major Depressive Disorder |
| title_fullStr | Predictive Biomarkers of Treatment Response in Major Depressive Disorder |
| title_full_unstemmed | Predictive Biomarkers of Treatment Response in Major Depressive Disorder |
| title_short | Predictive Biomarkers of Treatment Response in Major Depressive Disorder |
| title_sort | predictive biomarkers of treatment response in major depressive disorder |
| topic | depression predictive biomarkers electroencephalography transcranial magnetic stimulation |
| url | https://www.mdpi.com/2076-3425/13/11/1570 |
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