Grzenda Adrienne, Speier William, Siddarth Prabha, Pant Anurag, Krause-Sorio Beatrix, Narr Katherine, Lavretsky Helen
Department of Psychiatry and Biobehavioral Science, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
Medical Imaging and Informatics Group, Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States.
Front Psychiatry. 2021 Oct 20;12:738494. doi: 10.3389/fpsyt.2021.738494. eCollection 2021.
Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram ( = 32, NCT01902004) and Tai Chi ( = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.
近期证据表明，多模态数据的整合可提高机器学习对抑郁症治疗结果预测的性能。在此，我们比较了三种机器学习分类器的预测性能，这些分类器使用社会人口统计学特征、基线临床自我报告、认知测试以及结构磁共振成像（MRI）特征的不同组合来预测老年抑郁症（LLD）的治疗结果。数据来自两项针对60岁及以上抑郁症成年人进行的临床试验，包括对艾司西酞普兰（ = 32，NCT01902004）和太极拳（ = 35，NCT02460666）的反应。缓解定义为治疗24周结束时，24项汉密尔顿抑郁量表（HAMD）评分≤6分。特征子集由基线社会人口统计学和临床特征、灰质体积（GMV）或两者构建而成。比较了三种分类算法：（1）支持向量机 - 径向基函数（SVMRBF），（2）随机森林（RF），以及（3）逻辑回归（LR）。采用基于包装器的特征选择方法的重复5折交叉验证方法进行模型拟合。模型性能指标包括ROC曲线下面积（AUC）和马修斯相关系数（MCC）。通过置换分析检验交叉验证性能的显著性。使用带有Bonferroni校正的McNemar卡方检验，通过Cochran's Q和两两比较对分类器进行比较。对于RF和SVMRBF算法，组合特征集优于临床和GMV特征集，最终交叉验证的AUC分别为0.83±0.11和0.80±0.11。两个分类器均通过置换分析。LR算法单独使用GMV特征时表现最佳（AUC 0.79±0.14），但使用任何特征集时均未通过置换分析。对于所有三个特征集，三种分类器的性能差异显著。治疗反应的重要预测特征包括前扣带回和后扣带回体积、抑郁特征以及自我报告的健康相关质量评分。对使用机器学习和多模态数据来识别LLD中总体治疗反应预测因素的初步探索表明，临床和结构MRI特征的整合显著提高了预测能力。所识别的特征与先前在老年抑郁症中涉及的特征相同，这为该领域的未来研究提供了支持。
