Single-Subject Anxiety Treatment Outcome Prediction using Functional Neuroimaging
Single-Subject Anxiety Treatment Outcome Prediction using Functional Neuroimaging
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DOI：
10.1038/npp.2013.328
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发表时间：
2014-04-01
影响因子：
7.6
通讯作者：
Paulus, Martin P.
中科院分区：
文献类型：
--
作者：
Ball, Tali M.;Stein, Murray B.;Paulus, Martin P.
个体化治疗预测的可能性对焦虑症患者个性化干预的发展具有深远的意义。在这里，我们利用随机森林分类和治疗前的功能性磁共振成像（fMRI）的数据，从个人广泛性焦虑症（GAD）和惊恐障碍（PD），以产生个别受试者的治疗结果预测。在认知行为治疗（CBT）之前，48名成年人（25名GAD和23名PD）在fMRI扫描期间减少（通过认知重新评估）或维持对负面图像的情绪反应。使用70个解剖学定义的区域的激活预测CBT应答状态。最终的随机森林模型包括10个对分类准确性贡献最大的预测因子。使用临床和人口统计学变量进行了类似的分析。维持期间海马的激活和重新评估期间前额叶、上级颞叶、缘上回和上级额回的激活是最佳预测因子，应答者的激活程度高于无应答者。最终基于fMRI的模型产生了79%的准确性，具有良好的灵敏度（0.86），特异性（0.68）以及阳性和阴性似然比（2.73，0.20）。临床和人口统计学变量的准确性（69%），敏感性（0.79），特异性（0.53）和似然比（1.67，0.39）较差。这是第一次使用随机森林模型来预测精神病学治疗前神经影像学数据的治疗结果。随机森林模型和功能磁共振成像可以提供具有良好测试特性的单一受试者预测。此外，激活模式与皮质边缘回路的更大激活预示着GAD和PD中更好的CBT反应的概念一致。
The possibility of individualized treatment prediction has profound implications for the development of personalized interventions for patients with anxiety disorders. Here we utilize random forest classification and pre-treatment functional magnetic resonance imaging (fMRI) data from individuals with generalized anxiety disorder (GAD) and panic disorder (PD) to generate individual subject treatment outcome predictions. Before cognitive behavioral therapy (CBT), 48 adults (25 GAD and 23 PD) reduced (via cognitive reappraisal) or maintained their emotional responses to negative images during fMRI scanning. CBT responder status was predicted using activations from 70 anatomically defined regions. The final random forest model included 10 predictors contributing most to classification accuracy. A similar analysis was conducted using the clinical and demographic Variables. Activations in the hippocampus during maintenance and anterior insula, superior temporal, supramarginal, and superior frontal gyri during reappraisal were among the best predictors, with greater activation in responders than non-responders. The final fMRI-based model yielded 79% accuracy, with good sensitivity (0.86), specificity (0.68), and positive and negative likelihood ratios (2.73, 0.20). Clinical and demographic variables yielded poorer accuracy (69%), sensitivity (0.79), specificity (0.53), and likelihood ratios (1.67, 0.39). This is the first use of random forest models to predict treatment outcome from pre-treatment neuroimaging data in psychiatry. Together, random forest models and fMRI can provide single-subject predictions with good test characteristics. Moreover, activation patterns are consistent with the notion that greater activation in cortico-limbic circuitry predicts better CBT response in GAD and PD.