Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission.
Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission.
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抑制活动的吻侧前扣带皮层作为抑郁症缓解的生物标志物。
DOI：
10.1017/s0033291721004323
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发表时间：
2021-12-09
影响因子：
6.9
通讯作者：
Harrison, Ben J.
中科院分区：
文献类型：
--
作者：
Davey, Christopher G.;Cearns, Micah;Jamieson, Alec;Harrison, Ben J.
抑制喙前扣带皮层（rACC）已显示出作为抑郁症的预后生物标志物的前景。我们的目标是使用机器学习来验证其预测抑郁症缓解的能力。数据来自81名15至25岁的重度抑郁症患者，他们参加了YoDA-C试验，在该试验中，他们被随机分配接受认知行为治疗加氟西汀或安慰剂。在开始治疗之前，患者进行功能性磁共振成像（fMRI）任务，以评估rACC抑制。使用嵌套交叉验证在fMRI数据上训练支持向量机，并在临床数据上进行类似的训练。我们进一步在YoDA-A试验的数据上测试了我们的fMRI模型，在该试验中，参与者完成了相同的fMRI范式。在YoDA-C试验中，81名参与者中有36名（44%）获得缓解。我们的fMRI模型能够预测缓解状态（AUC = 0.777 [95%置信区间（CI）0.638-0.916]，平衡准确度= 67%，阴性预测值= 74%，p < 0.0001）。临床模型未能预测缓解状态优于机会水平。在替代YoDA-A数据集上测试该模型证实了其预测缓解的能力（AUC = 0.776，平衡准确度= 64%，阴性预测值= 70%，p < 0.0001）。我们证实，rACC活性作为抑郁症的预后生物标志物。机器学习模型可以识别可能患有难以治疗的抑郁症的患者，这可能会指导早期提供增强的支持和更强化的治疗。
Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission. Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm. Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638–0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001). We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies.