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.

晚年抑郁症 (LLD) 是一种特别使人衰弱的疾病。患有抑郁症的老年人通常会因抗抑郁药治疗、医疗合并症和日常功能下降而出现不良反应。本综述旨在进一步了解 LLD 背后的脑网络功能障碍，这些功能障碍导致认知和情感过程中断以及相应的临床表现。我们概述了 LLD 的网络模型，该模型集成了显着网络、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，重点是它们与临床亚型的联系，这些亚型通常对可用的治疗没有反应。了解构成这些中断过程的大脑网络可以为 LLD 有针对性的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。