基于数据驱动的皮质下形状和皮质厚度聚类选择可预测抑郁症状的恢复情况。
DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS.
作者信息
Wade Benjamin S C, Sui Jing, Njau Stephanie, Leaver Amber M, Vasvada Megha, Gutman Boris A, Thompson Paul M, Espinoza Randal, Woods Roger P, Abbott Christopher C, Narr Katherine L, Joshi Shantanu H
机构信息
Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, UCLA.
Imaging Genetics Center, USC.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:502-506. doi: 10.1109/ISBI.2017.7950570. Epub 2017 Jun 19.
Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.
重度抑郁症（MDD）患者在接受抗抑郁治疗后若未实现症状完全缓解，则复发风险更高。与药物治疗相比，电休克疗法（ECT）能在重度抑郁症患者中更迅速地产生更大程度的反应。然而，预测哪些患者最有可能在ECT治疗后改善仍然具有挑战性。利用42名在ECT治疗前接受扫描的ECT患者的结构MRI数据，我们基于数据驱动的形状聚类选择和皮质厚度特征开发了一种随机森林分类器来预测缓解情况。右侧海马体形状和右侧颞下回皮质厚度对缓解的预测性最强，当这些区域在治疗前较厚时，恢复的预测概率会降低。缓解的预测平均平衡准确率为73%。MRI数据分类可能有助于识别对治疗有反应的患者，并辅助临床决策。我们的结果为个性化治疗策略的开发带来了希望。
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