Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy.
Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy.
复制标题
认知行为治疗后抑郁症和对症状变化敏感的个体的强化学习中断。
DOI：
10.1001/jamapsychiatry.2021.1844
复制
发表时间：
2021-10-01
期刊：
影响因子：
25.8
通讯作者：
Chiu PH
中科院分区：
文献类型：
--
作者：
Brown VM;Zhu L;Solway A;Wang JM;McCurry KL;King-Casas B;Chiu PH
抑郁症状是否与强化学习的特征相关，如果是，治疗相关的症状变化是否与学习变化相关？在这项包括101名参与者的混合横断面队列研究中，患有和不患有抑郁症的参与者在功能性磁共振成像期间完成了概率学习任务;患有抑郁症的参与者在认知行为治疗（CBT）后进行了重新评估。基于计算模型的行为选择和神经数据的分析确定了奖励学习和损失学习期间的学习与症状的关联; CBT后的症状改善与学习参数的正常化相关。将强化学习过程映射到抑郁症症状揭示了这些症状的机械特征，并指出了可能的基于学习的治疗过程和目标。这项混合横断面队列研究评估了认知行为治疗后计算模型衍生的强化学习参数、抑郁症状和症状变化之间的关联。重度抑郁症是一种普遍存在的损害性疾病。解析抑郁症患者强化学习的神经计算基质可能有助于对这种疾病的机械理解，并提出新的认知治疗目标。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。在这项混合横断面队列研究中，个体在基线和随访时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。从社区招募了一名有抑郁症诊断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月进行评估，并于2017年5月至2021年5月进行数据分析。基于计算模型的分析参与者的选择评估了先验假设之间的关联的奖励为基础的和损失为基础的学习与抑郁症状。然后在接受认知行为疗法（CBT）的一部分参与者中评估学习参数和症状的变化。在纳入的101例成人中，69例（68.3%）为女性，平均（SD）年龄为34.4（11.2）岁。在基线时，共有69名被诊断为抑郁症的参与者和32名未被诊断为抑郁症的参与者被纳入; 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）在随访时被纳入（平均[SD]为115.1 [15.6]天）。基于计算模型的行为选择和神经数据的分析确定了奖励学习和损失学习过程中学习与症状的关联。仅在奖励学习期间，（而不是负面影响或唤醒）与模型衍生的学习参数相关（学习率：后验均值回归β = −0.14; 95%可信区间[CrI]，−0.12至−0.03;结果敏感性：后验均值回归β = 0.18; 95%CrI，0.02 - 0.37）和神经学习信号（纹状体预测误差和预期值信号之间的关联适度：t97 =-2.10; P = 0.04）。仅在丧失学习期间，负面情绪（而不是快感缺乏或唤醒）与学习参数（结果转移：后验均值回归β =-0.11; 95%CrI，-0.20至-0.01）和学习信号的神经编码中断（与膝下前扣带回预测误差信号相关：r =-0.28; P = 0.005）相关。CBT后的症状改善与基线时中断的学习参数的正常化相关（奖励学习率：后验平均回归β = 0.15; 90%CrI，0.001至0.41;损失结果变化：后验平均回归β = 0.42; 90%CrI，0.09至0.77）。在这项研究中，强化学习组件与抑郁症症状的映射揭示了与这些症状相关的机械特征，并指出了可能的基于学习的治疗过程和目标。
Are depression symptoms associated with features of reinforcement learning, and if so, is treatment-related symptom change associated with learning changes? In this mixed cross-sectional–cohort study including 101 participants, participants with and without depression completed a probabilistic learning task during functional magnetic resonance imaging; participants with depression were reassessed after cognitive behavioral therapy (CBT). Computational model–based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively; symptom improvement following CBT was associated with normalization of learning parameters. Mapping reinforcement learning processes to symptoms of depression reveals mechanistic features of these symptoms and points to possible learning-based therapeutic processes and targets. This mixed cross-sectional–cohort study evaluates associations among computational model–derived reinforcement learning parameters, depression symptoms, and symptom changes after cognitive behavioral therapy. 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. To determine associations among computational model–derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment. 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. 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). 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). 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.
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DOI：
10.1176/appi.ajp.2012.12010014
发表时间：
2013-02
期刊：
The American journal of psychiatry
影响因子：
--
作者：
Heller AS;Johnstone T;Light SN;Peterson MJ;Kolden GG;Kalin NH;Davidson RJ
通讯作者：
Davidson RJ
影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
DOI：
10.1146/annurev-clinpsy-032210-104535
发表时间：
2011-01-01
影响因子：
18.4
作者：
Dimidjian, Sona;Barrera, Manuel, Jr.;Lewinsohn, Peter M.
通讯作者：
Lewinsohn, Peter M.
影响因子：
25.8
作者：
Gillan, Claire M.;Kalanthroff, Eyal;Simpson, H. B.
通讯作者：
Simpson, H. B.
影响因子：
14.5
作者：
Gradin, Victoria B.;Kumar, Poornima;Steele, J. Douglas
通讯作者：
Steele, J. Douglas