Precision non-implantable neuromodulation therapies: a perspective for the depressed brain

Lucas Borrione, Helena Bellini, Lais Boralli Razza, Ana G Avila, Chris Baeken, Anna-Katharine Brem, Geraldo Busatto, Andre F Carvalho, Adam Chekroud, Zafiris J Daskalakis, Zhi-De Deng, Jonathan Downar, Wagner Gattaz, Colleen Loo, Paulo A Lotufo, Maria da Graça M Martin, Shawn M McClintock, Jacinta O'Shea, Frank Padberg, Ives C Passos, Giovanni A Salum, Marie-Anne Vanderhasselt, Renerio Fraguas, Isabela Benseñor, Leandro Valiengo, Andre R Brunoni, Lucas Borrione, Helena Bellini, Lais Boralli Razza, Ana G Avila, Chris Baeken, Anna-Katharine Brem, Geraldo Busatto, Andre F Carvalho, Adam Chekroud, Zafiris J Daskalakis, Zhi-De Deng, Jonathan Downar, Wagner Gattaz, Colleen Loo, Paulo A Lotufo, Maria da Graça M Martin, Shawn M McClintock, Jacinta O'Shea, Frank Padberg, Ives C Passos, Giovanni A Salum, Marie-Anne Vanderhasselt, Renerio Fraguas, Isabela Benseñor, Leandro Valiengo, Andre R Brunoni

Abstract

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.

Conflict of interest statement

Z-DD is listed as inventor on patents/patent applications related to brain stimulation technology, assigned to Columbia University and NEVA Electromagnetics, not licensed, and with no remuneration. Although he is an employee of the U.S. government, the views expressed are his own and do not necessarily represent the views of the National Institutes of Health, the Department of Health and Human Services, or the U.S. government. The other authors report no conflicts of interest.

Figures

Figure 1. Precision non-implantable neuromodulation (NIN). In…
Figure 1. Precision non-implantable neuromodulation (NIN). In a precision NIN framework, advancements in related areas of research and knowledge directly influence treatment protocols (parameters such as stimulation site, timing, and dose, as well as combined behavioral/pharmacological interventions), aiming to increase individual antidepressant response.
Figure 2. Examples of NIN techniques (top…
Figure 2. Examples of NIN techniques (top panel) and the corresponding electric field distribution in the brain (bottom panel): A) tDCS using 5 × 5 cm electrodes placed over the bilateral DLPFC; electrodes are colored red and blue to distinguish anode (red) vs cathode (blue). B) TMS using the MagVenture B70 coil over the left DLPFC. C) Right unilateral ECT; conventional ECT uses a bipolar waveform and therefore does not distinguish between anodal vs. cathodal electrodes. Electric field strengths are normalized to their respective maximum value (Emax); absolute field strengths are very different across the modalities ( 100 V/m for TMS and ECT). Figure produced using SimNIBS software. DLPFC = dorsolateral prefrontal cortex; ECT = electroconvulsive therapy; NIN = non-implantable neuromodulation; tDCS = transcranial direct current stimulation; TMS = transcranial magnetic stimulation.
Figure 3. Example of a machine learning…
Figure 3. Example of a machine learning pipeline. Analysis pipeline. A) Treatment outcomes of group are predicted according to the feature dataset. B) Models are trained to classify responders and non-responders at the study endpoint. Performance is evaluated in a repeated nested cross-validation paradigm. C) Features with the highest contribution to the model can be identified. RCT = randomized clinical trial.

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