Jennifer I Lissemore1, Benoit H Mulsant2, Anthony J Bonner3, Meryl A Butters4, Robert Chen5, Jonathan Downar6, Jordan F Karp7, Eric J Lenze8, Tarek K Rajji9, Charles F Reynolds4, Reza Zomorrodi10, Zafiris J Daskalakis11, Daniel M Blumberger12. 1. Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 2. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. 3. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. 4. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. 5. Division of Neurology, Department of Medicine, University of Toronto and Krembil Research Institute, Toronto, Ontario, Canada. 6. Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; MRI-Guided rTMS Clinic and Krembil Research Institute, University Health Network, Toronto, Ontario, Canada. 7. Department of Psychiatry, College of Medicine-Tucson, University of Arizona, Tuscon, Arizona. 8. Healthy Mind Lab, Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri. 9. Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. 10. Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. 11. Department of Psychiatry, School of Medicine, UC San Diego Health, San Diego, California. 12. Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada. Electronic address: daniel.blumberger@camh.ca.
Abstract
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 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. 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 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). 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.
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 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. 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 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). 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.
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