Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2020
Precision non-implantable neuromodulation therapies: a perspective for the depressed brain.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Borrione Lucas, Bellini Helena, Razza Lais Boralli, Avila 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
Аннотация
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.