Department of Cognitive Psychology II, Goethe University Frankfurt am Main, Frankfurt am Main, Germany.
Department of Psychiatry and Psychotherapy, Philipps University of Marburg, Marburg, Germany.
JAMA Psychiatry. 2015 Jan;72(1):68-74. doi: 10.1001/jamapsychiatry.2014.1741.
Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research.
To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG).
DESIGN, SETTING, AND PARTICIPANTS: We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010.
Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure.
Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT.
Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable accuracy rates (acquisition phase, 70%; extinction phase, 71%; combined, 79%).
Predicting treatment response to CBT based on functional neuroimaging data in PD/AG is possible with high accuracy on an individual-patient level. This novel machine-learning approach brings personalized medicine within reach, directly supporting clinical decisions for the selection of treatment options, thus helping to improve response rates.
尽管神经影像学研究在识别焦虑障碍的大规模神经基础方面取得了重大进展，但它在临床应用方面的价值却落后于预期。机器学习方法在预测个体患者预后方面具有预测潜力，因此可能有助于精神科研究的转化努力。
基于伴有广场恐怖症的惊恐障碍（PD/AG）患者的功能磁共振成像数据，预测个体患者对认知行为疗法（CBT）的治疗反应。
设计、设置和参与者：我们纳入了 49 名至少 4 周未服用药物且主要诊断为 PD/AG 的患者，这些患者参与了在德国 8 家临床研究机构和门诊中心进行的纵向研究。功能磁共振成像研究于 2007 年 7 月至 2010 年 3 月进行。
每周进行 2 次共 12 次的 CBT，重点是行为暴露。
治疗反应定义为汉密尔顿焦虑量表评分降低 50%以上。在差异恐惧条件反射任务期间测量血氧水平依赖信号。使用嵌套的留一法交叉验证的区域和全脑高斯过程分类器，从 CBT 前采集的数据预测治疗反应。
虽然没有单个脑区具有预测治疗反应的能力，但整合基于恐惧条件反射任务采集和消退阶段数据的全脑区域分类器可获得良好的预测性能（准确性为 82%；敏感性为 92%；特异性为 72%；P <.001）。采集阶段的数据可实现 73%的正确个体患者分类（敏感性为 80%；特异性为 67%；P <.001），而消退阶段的数据则导致准确性为 74%（敏感性为 64%；特异性为 83%；P <.001）。考虑到潜在混杂因素的保守重新分析产生了名义上较低但可比的准确率（采集阶段，70%；消退阶段，71%；综合阶段，79%）。
在伴有广场恐怖症的惊恐障碍患者中，基于功能神经影像学数据预测对 CBT 的治疗反应可以达到很高的个体患者水平的准确性。这种新的机器学习方法使个性化医疗成为可能，直接支持治疗选择的临床决策，从而有助于提高反应率。
