Importance Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets. Objective To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment. Design, Setting, and Participants In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021. Main Outcomes and Measures Computational model-based analyses of participants' choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT). Results Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model-based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = -0.14; 95% credible interval [CrI], -0.12 to -0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = -2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = -0.11; 95% CrI, -0.20 to -0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = -0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77). Conclusions and Relevance In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.

中文翻译：

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认知行为疗法后抑郁症和对症状变化敏感的个体的强化学习中断。

重要性 重度抑郁症是普遍存在的并且具有损害性。解析抑郁症患者强化学习的神经计算基础可能有助于从机制上理解该疾病并提出新的认知治疗目标。目的确定计算模型衍生的强化学习参数、抑郁症状和治疗后症状变化之间的关联。设计、设置和参与者在这项混合横断面队列研究中，个体在基线和后续的功能性磁共振成像期间执行概率学习任务的奖励和损失变体。从社区招募了一个有和没有抑郁症诊断的志愿者样本。参与者于 2011 年 7 月至 2017 年 2 月接受评估，并分析了 2017 年 5 月至 2021 年 5 月的数据。主要结果和措施基于计算模型的参与者选择分析评估了关于基于奖励和基于损失的学习的组成部分与抑郁症状之间关联的先验假设。然后在接受认知行为治疗 (CBT) 的一部分参与者中评估学习参数和症状的变化。结果 101 名成人中，69 名 (68.3%) 为女性，平均 (SD) 年龄为 34.4 (11.2) 岁。基线时共有 69 名诊断为抑郁症的参与者和 32 名未诊断为抑郁症的参与者；48 名参与者（28 名接受 CBT 的抑郁症患者和 20 名无抑郁症患者）被纳入随访（平均 [SD] 为 115.1 [15.6] 天）。基于计算模型的行为选择分析和神经数据分别确定了奖励学习和损失学习期间学习与症状的关联。仅在奖励学习期间，快感缺失（而非负面影响或唤醒）与模型衍生的学习参数相关（学习率：后验平均回归 β = -0.14；95% 可信区间 [CrI]，-0.12 至 -0.03；结果敏感性：后验平均回归 β = 0.18；95% CrI，0.02 至 0.37）和神经学习信号（纹状体预测误差与预期值信号之间关联的缓和：t97 = -2.10；P = .04）。仅在损失学习期间，负面影响（而不是快感缺失或唤醒）与学习参数相关（结果偏移：后验平均回归 β = -0.11；95% CrI，-0.20 至 -0。01) 并破坏了学习信号的神经编码（与膝下前扣带回预测误差信号相关：r = -0.28；P = .005）。CBT 后的症状改善与基线时被破坏的学习参数的标准化相关（奖励学习率：后验平均回归 β = 0.15；90% CrI，0.001 至 0.41；损失结果转变：后验平均回归 β = 0.42；90% CrI , 0.09 至 0.77)。结论和相关性 在这项研究中，强化学习成分与重度抑郁症症状的映射揭示了与这些症状相关的机制特征，并指出了可能的基于学习的治疗过程和目标。CBT 后的症状改善与基线时被破坏的学习参数的标准化相关（奖励学习率：后验平均回归 β = 0.15；90% CrI，0.001 至 0.41；损失结果转变：后验平均回归 β = 0.42；90% CrI , 0.09 至 0.77)。结论和相关性 在这项研究中，强化学习成分与重度抑郁症症状的映射揭示了与这些症状相关的机制特征，并指出了可能的基于学习的治疗过程和目标。CBT 后的症状改善与基线时被破坏的学习参数的标准化相关（奖励学习率：后验平均回归 β = 0.15；90% CrI，0.001 至 0.41；损失结果转变：后验平均回归 β = 0.42；90% CrI , 0.09 至 0.77)。结论和相关性 在这项研究中，强化学习成分与重度抑郁症症状的映射揭示了与这些症状相关的机制特征，并指出了可能的基于学习的治疗过程和目标。