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In Biological psychiatry. Cognitive neuroscience and neuroimaging

BACKGROUND : Older 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.

METHODS : We assessed whether pre-treatment 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.

RESULTS : Two 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 non-responders; permutation testing, p<0.005); these models significantly outperformed (corrected t-test, p=0.025) models using clinical and demographic predictors alone (60±10% balanced accuracy).

CONCLUSIONS : These 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.

Lissemore Jennifer I, Mulsant Benoit H, Bonner Anthony J, Butters Meryl A, Chen Robert, Downar Jonathan, Karp Jordan F, Lenze Eric J, Rajji Tarek K, Reynolds Charles F, Zomorrodi Reza, Daskalakis Zafiris J, Blumberger Daniel M


TMS, cortical excitability, genetic algorithm, geriatric depression, late-life depression, neurophysiology, predictive biomarker, support vector machine