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基于大脑的老年期抑郁症发病机制:对新型干预措施的启示。

Brain-based mechanisms of late-life depression: Implications for novel interventions.

机构信息

Department of Psychiatry, Weill Cornell Medicine, New York, NY 10065, USA.

Department of Psychiatry, Weill Cornell Medicine, New York, NY 10065, USA.

出版信息

Semin Cell Dev Biol. 2021 Aug;116:169-179. doi: 10.1016/j.semcdb.2021.05.002. Epub 2021 May 12.

Abstract

Late-life depression (LLD) is a particularly debilitating illness. Older adults suffering from depression commonly experience poor outcomes in response to antidepressant treatments, medical comorbidities, and declines in daily functioning. This review aims to further our understanding of the brain network dysfunctions underlying LLD that contribute to disrupted cognitive and affective processes and corresponding clinical manifestations. We provide an overview of a network model of LLD that integrates the salience network, the default mode network (DMN) and the executive control network (ECN). We discuss the brain-based structural and functional mechanisms of LLD with an emphasis on their link to clinical subtypes that often fail to respond to available treatments. Understanding the brain networks that underlie these disrupted processes can inform the development of targeted interventions for LLD. We propose behavioral, cognitive, or computational approaches to identifying novel, personalized interventions that may more effectively target the key cognitive and affective symptoms of LLD.

摘要

老年期抑郁症(LLD)是一种特别使人虚弱的疾病。患有抑郁症的老年人通常对抗抑郁治疗、合并症和日常功能下降的反应不佳。本综述旨在进一步了解导致认知和情感过程中断以及相应临床表现的 LLD 大脑网络功能障碍的基础。我们提供了一个 LLD 的网络模型概述,该模型整合了突显网络、默认模式网络 (DMN) 和执行控制网络 (ECN)。我们讨论了 LLD 的基于大脑的结构和功能机制,重点是它们与经常对现有治疗方法无反应的临床亚型的联系。了解这些中断过程背后的大脑网络可以为 LLD 的靶向干预措施的发展提供信息。我们提出了行为、认知或计算方法来识别新的、个性化的干预措施,这些干预措施可能更有效地针对 LLD 的关键认知和情感症状。

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