Author(s):
Borrione, Lucas ; Bellini, Helena ; Razza, Lais Boralli ; Ávila, Ana G. ; Baeken, Chris ; Brem, Anna-Katharine ; Busatto, Geraldo ; Carvalho, Andre F. ; Chekroud, Adam ; Daskalakis, Zafiris J. ; Deng, Zhi-De ; Downar, Jonathan ; Gattaz, Wagner ; Loo, Colleen ; Lotufo, Paulo A ; Martin, Maria da Graça M. ; McClintock, Shawn M. ; O'Shea, Jacinta ; Padberg, Frank ; Passos, Ives C. ; Salum, Giovanni A. ; Vanderhasselt, Marie-Anne ; Fraguas, Renerio ; Benseñor, Isabela ; Valiengo, Leandro ; Brunoni, Andre R.
Date: 2020
Persistent ID: https://hdl.handle.net/10316/105843
Origin: Estudo Geral - Universidade de Coimbra
Subject(s): Major depressive disorder; transcranial magnetic stimulation; transcranial direct current stimulation; electroconvulsive therapy; precision medicine; Brain; Deep Brain Stimulation; Depression; Depressive Disorder, Major; Humans; Transcranial Magnetic Stimulation; Treatment Outcome; Electroconvulsive Therapy; Transcranial Direct Current Stimulation
Description
Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.
AGA is supported by Fundac¸a˜o para a Cieˆncia e Tecnologia and Programa COMPETE, Portugal (grant PTDC/ MHC-PAP/5618/2014 [POCI-01-0145-FEDER-016836]; http://www.poci-compete2020.pt/). Z-DD is supported by the National Institute of Mental Health Intramural Research Program (grant ZIAMH002955) and by a Young Investigator Award from the Brain & Behavior Research Foundation (grant 26161). SMM receives research support from the National Institutes of Health (NIH) and is a consultant to Pearson Assessment. JO’S is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant 215451/Z/19/Z). ICP is supported by funding from Secretaria Nacional de Polı´ticas sobre Drogas (SENAD) and Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq). LBR is supported by Fundac¸a˜o de Amparo a` Pesquisa do Estado de Sa˜o Paulo (FAPESP; grant 2019/07256-7). ARB is supported by productivity grants from CNPq-1B and the Programa de Incentivo a` Produtividade Acadeˆmica (PIPA), Faculdade de Medicina, Universidade de Sa˜o Paulo (USP).
