Crane Natania A, Jenkins Lisanne M, Bhaumik Runa, Dion Catherine, Gowins Jennifer R, Mickey Brian J, Zubieta Jon-Kar, Langenecker Scott A
The University of Illinois at Chicago, Department of Psychiatry and the Cognitive Neuroscience Center, Chicago, IL 60612, USA.
The University of Michigan Medical School, Department of Psychiatry, Molecular and Behavioral Neuroscience Institute, Ann Arbor, MI 48104, USA.
Brain. 2017 Feb;140(2):472-486. doi: 10.1093/brain/aww326. Epub 2017 Jan 24.
Predicting treatment response for major depressive disorder can provide a tremendous benefit for our overstretched health care system by reducing number of treatments and time to remission, thereby decreasing morbidity. The present study used neural and performance predictors during a cognitive control task to predict treatment response (% change in Hamilton Depression Rating Scale pre- to post-treatment). Forty-nine individuals diagnosed with major depressive disorder were enrolled with intent to treat in the open-label study; 36 completed treatment, had useable data, and were included in most data analyses. Participants included in the data analysis sample received treatment with escitalopram (n = 22) or duloxetine (n = 14) for 10 weeks. Functional MRI and performance during a Parametric Go/No-go test were used to predict per cent reduction in Hamilton Depression Rating Scale scores after treatment. Haemodynamic response function-based contrasts and task-related independent components analysis (subset of sample: n = 29) were predictors. Independent components analysis component beta weights and haemodynamic response function modelling activation during Commission errors in the rostral and dorsal anterior cingulate, mid-cingulate, dorsomedial prefrontal cortex, and lateral orbital frontal cortex predicted treatment response. In addition, more commission errors on the task predicted better treatment response. Together in a regression model, independent component analysis, haemodynamic response function-modelled, and performance measures predicted treatment response with 90% accuracy (compared to 74% accuracy with clinical features alone), with 84% accuracy in 5-fold, leave-one-out cross-validation. Convergence between performance markers and functional magnetic resonance imaging, including novel independent component analysis techniques, achieved high accuracy in prediction of treatment response for major depressive disorder. The strong link to a task paradigm provided by use of independent component analysis is a potential breakthrough that can inform ways in which prediction models can be integrated for use in clinical and experimental medicine studies.
预测重度抑郁症的治疗反应可为我们过度紧张的医疗保健系统带来巨大益处，方法是减少治疗次数和缓解时间，从而降低发病率。本研究在认知控制任务期间使用神经和行为预测指标来预测治疗反应（汉密尔顿抑郁量表治疗前后的变化百分比）。四十九名被诊断为重度抑郁症的个体被纳入开放标签研究并有意接受治疗；36人完成治疗，有可用数据，并被纳入大多数数据分析。纳入数据分析样本的参与者接受了10周的艾司西酞普兰（n = 22）或度洛西汀（n = 14）治疗。使用功能磁共振成像和参数化Go/No-go测试期间的行为表现来预测治疗后汉密尔顿抑郁量表评分的降低百分比。基于血流动力学反应函数的对比和与任务相关的独立成分分析（样本子集：n = 29）是预测指标。独立成分分析成分的β权重以及在喙部和背侧前扣带回、中扣带回、背内侧前额叶皮质和外侧眶额皮质的执行错误期间血流动力学反应函数建模的激活预测了治疗反应。此外，任务中更多的执行错误预测了更好的治疗反应。在回归模型中，独立成分分析、血流动力学反应函数建模和行为测量指标共同预测治疗反应的准确率为90%（相比仅使用临床特征时的准确率为74%），在5折留一法交叉验证中的准确率为84%。行为标记与功能磁共振成像之间的融合，包括新颖的独立成分分析技术，在预测重度抑郁症的治疗反应方面达到了高精度。使用独立成分分析提供的与任务范式的紧密联系是一个潜在的突破，可为预测模型在临床和实验医学研究中的整合方式提供参考。
