ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2022
Transcranial Magnetic Stimulation Indices of Cortical Excitability Enhance the Prediction of Response to Pharmacotherapy in Late-Life Depression.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 2 citations in OpenAlex.
- Transcranial magnetic stimulation in forensic populations: scoping review.Frontiers in psychology · 2026Pooled it
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Cortical high-threshold and low-activation characteristics in adolescent depression: a cross-age differential analysis.Frontiers in psychiatry · 2026Article
- Exploratory genome-wide analyses of cortical inhibition, facilitation, and plasticity in late-life depression.Translational psychiatry · 2023Article
- Factors associated with antidepressant responses to repetitive transcranial magnetic stimulation in antidepressant-resistant depression.Frontiers in neuroscience · 2022Article
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundOlder adults with late-life depression (LLD) often experience incomplete or lack of response to first-line pharmacotherapy. The treatment of LLD could be improved using objective biological measures to predict response. Transcranial magnetic stimulation (TMS) can be used to measure cortical excitability, inhibition, and plasticity, which have been implicated in LLD pathophysiology and associated with brain stimulation treatment outcomes in younger adults with depression. TMS measures have not yet been investigated as predictors of treatment outcomes in LLD or pharmacotherapy outcomes in adults of any age with depression.
methodsWe assessed whether pretreatment single-pulse and paired-pulse TMS measures, combined with clinical and demographic measures, predict venlafaxine treatment response in 76 outpatients with LLD. We compared the predictive performance of machine learning models including or excluding TMS predictors.
resultsTwo single-pulse TMS measures predicted venlafaxine response: cortical excitability (neuronal membrane excitability) and the variability of cortical excitability (dynamic fluctuations in excitability levels). In cross-validation, models using a combination of these TMS predictors, clinical markers of treatment resistance, and age classified patients with 73% ± 11% balanced accuracy (average correct classification rate of responders and nonresponders; permutation testing, p < .005); these models significantly outperformed (corrected t test, p = .025) models using clinical and demographic predictors alone (60% ± 10% balanced accuracy).
conclusionsThese preliminary findings suggest that single-pulse TMS measures of cortical excitability may be useful predictors of response to pharmacotherapy in LLD. Future studies are needed to confirm these findings and determine whether combining TMS predictors with other biomarkers further improves the accuracy of predicting LLD treatment outcome.
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