Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical "negative prediction mechanism" is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual's capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update individual's brain so that it increasingly predicts and reinforces negative experiences over time-pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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抑郁认知是如何形成的？预测处理的状态依赖型网络模型。


抑郁症的症状、神经影像学数据和治疗反应存在很大异质性。因此，描述它在网络层面的发展是出了名的困难。为了克服这个问题，提出了一种理论上的“负预测机制”。在这里，八个关键的大脑区域在一个短暂的、依赖于状态的、核心的病理通信网络中连接起来，这可能促进抑郁认知的发展。在预测处理的背景下，建议这种机制被激活，作为对外部和/或内部环境中超出弱势个体认知评估能力的负面/不利刺激的反应。具体来说，建议通过该网络的重复激活来更新个人的大脑，以便随着时间的推移，它越来越多地预测和加强负面体验——将有患抑郁症风险或患有抑郁症的人推向更深的精神疾病。其中，负预测机制准备解释预后结果的各个方面，描述抑郁症如何在动态变化的复杂环境中在多个时间尺度上起伏。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。