Marc J Dubin1, Conor Liston2, Michael A Avissar3, Irena Ilieva4, Faith M Gunning4. 1. Department of Psychiatry, Weill Cornell Medical College, New York, NY, 10065, USA; Feil Family Mind and Brain Institute, Weill Cornell Medical College, New York, NY, 10065, USA. 2. Department of Psychiatry, Weill Cornell Medical College, New York, NY, 10065, USA; Feil Family Mind and Brain Institute, Weill Cornell Medical College, New York, NY, 10065, USA; Sackler Institute for Developmental Psychobiology, Weill Cornell Medical College, New York, NY, 10065, USA. 3. Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA; Division of Experimental Therapeutics, New York State Psychiatric Institute. 4. Department of Psychiatry, Weill Cornell Medical College, New York, NY, 10065, USA; Institute of Geriatric Psychiatry, Weill Cornell Medical College, New York, NY, 10065, USA.
Abstract
PURPOSE OF REVIEW: First, we will identify candidate predictive biomarkers of antidepressant response of TMS based on the neuroimaging literature. Next, we will review the effects of TMS on networks involved in depression. Finally, we will discuss ways in which our current understanding of network engagement by TMS may be used to optimize its antidepressant effect. RECENT FINDINGS: The past few years has seen significant interest in the antidepressant mechanisms of TMS. Studies using functional neuroimaging and neurochemical imaging have demonstrated engagement of networks known to be important in depression. Current evidence supports a model whereby TMS normalizes network function gradually over the course of several treatments. This may, in turn, mediate its antidepressant effect. SUMMARY: One strategy to optimize the antidepressant effect of TMS is to more precisely target networks relevant in depression. We propose methods to achieve this using functional and neurochemical imaging.
PURPOSE OF REVIEW: First, we will identify candidate predictive biomarkers of antidepressant response of TMS based on the neuroimaging literature. Next, we will review the effects of TMS on networks involved in depression. Finally, we will discuss ways in which our current understanding of network engagement by TMS may be used to optimize its antidepressant effect. RECENT FINDINGS: The past few years has seen significant interest in the antidepressant mechanisms of TMS. Studies using functional neuroimaging and neurochemical imaging have demonstrated engagement of networks known to be important in depression. Current evidence supports a model whereby TMS normalizes network function gradually over the course of several treatments. This may, in turn, mediate its antidepressant effect. SUMMARY: One strategy to optimize the antidepressant effect of TMS is to more precisely target networks relevant in depression. We propose methods to achieve this using functional and neurochemical imaging.
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