Department of Psychology, University of California, Los Angeles, CA 90095;
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles, CA 90095.
Proc Natl Acad Sci U S A. 2018 Feb 27;115(9):2222-2227. doi: 10.1073/pnas.1716686115. Epub 2018 Feb 12.
Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive-compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual's potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity. Pretreatment FC patterns within the default mode network and visual network significantly predicted posttreatment OCD severity, explaining up to 67% of the variance. These networks were stronger predictors than pretreatment clinical scores. Results have clinical implications for developing personalized medicine approaches to identifying individual OCD patients who will maximally benefit from intensive CBT.
认知行为疗法（CBT）是治疗强迫症（OCD）的有效方法。然而，个体之间的反应差异很大。如果有一种方法可以预测一个人的潜在反应，那么临床医生就可以更谨慎地为这种经常带来压力和耗时的治疗分配资源。我们在接受 4 周密集每日 CBT 治疗前后，从 OCD 成人患者中收集了静息状态功能磁共振成像数据。我们利用机器学习和交叉验证来评估功能连接（FC）模式预测个体治疗后 OCD 症状严重程度的能力。默认模式网络和视觉网络中的治疗前 FC 模式可显著预测治疗后的 OCD 严重程度，解释了高达 67%的方差。这些网络比治疗前的临床评分更具预测性。这些结果对开发个性化医疗方法具有临床意义，可以识别出将从密集 CBT 中受益最大的 OCD 患者。
