Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
Department of Radiology & Biomedical Imaging, University of California, San Francisco, 1700 4th Street, BH102, San Francisco, CA 94143, USA.
Neuroimage Clin. 2019;23:101914. doi: 10.1016/j.nicl.2019.101914. Epub 2019 Jul 2.
Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. "Who will improve with CBT?" is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT.
We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation.
The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus.
Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression.
青少年重度抑郁症（MDD）是一种高发、致残和高成本的疾病。许多患有抑郁症的青少年并没有因认知行为疗法（CBT）而得到改善，CBT 是青少年 MDD 的一线治疗方法，他们面临着自杀风险增加和许多负面健康后果的毁灭性后果。“谁会因 CBT 而改善？”是一个至关重要的问题，但仍未得到解答，青少年抑郁症的治疗计划仍然没有生物学指导。本研究的目的是利用机器学习应用于患者的脑影像数据，以帮助预测 CBT 对抑郁症状的缓解。
我们将监督机器学习应用于基于弥散磁共振成像的结构连接组学数据，以预测 30 名接受 CBT 治疗三个月后的抑郁青少年的症状缓解情况。选择了一组 21 个属性，包括基线抑郁评分、年龄、性别、两个全局网络属性以及先前与抑郁相关的大脑区域的节点强度。使用 10 折交叉验证，采用实用且稳健的 J48 修剪树分类器。
分类结果预测抑郁症状缓解的准确率为 83%。大小为 7 且只有 3 个属性的树突出了右侧丘脑在预测 CBT 对抑郁症状缓解中的作用。进一步的分析显示，抑郁症状的变化与右侧丘脑的节点强度之间存在显著的负相关。
我们的结果表明，仅使用结构连接组学数据和基线抑郁评分的机器学习算法可以高度准确地预测青少年 MDD 患者接受 CBT 后的抑郁症状缓解情况。这一知识可以帮助改善青少年抑郁症的治疗计划。
