Predicting individual clinical trajectories of depression with generative embedding
Predicting individual clinical trajectories of depression with generative embedding
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DOI：
10.1016/j.nicl.2020.102213
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发表时间：
2020-01-01
影响因子：
4.2
通讯作者：
Stephan, Klaas E.
中科院分区：
文献类型：
--
作者：
Fraessle, Stefan;Marquand, Andre F.;Stephan, Klaas E.
重性抑郁症（MDD）患者表现出异质性的治疗反应和高度可变的临床轨迹：虽然一些患者经历了迅速恢复，但其他患者表现出复发缓解或慢性病程。在早期阶段预测个人的临床轨迹是精神病学的一个关键挑战，并可能促进个性化的干预措施。然而，到目前为止，在单个患者水平上缺乏可靠的预测因素。在这里，我们评估了机器学习策略-生成嵌入（GE）-的实用性，它将可解释的生成模型与判别式分类器相结合。具体来说，我们使用功能性磁共振成像（fMRI）数据的情绪面孔知觉的85例抑郁症患者从荷兰抑郁症和焦虑症的研究（NESDA）已随访超过两年，并分为三个亚组不同的临床轨迹。将有效（定向）连接的生成模型与支持向量机（SVM）相结合，我们可以预测给定患者是否会经历慢性抑郁症与快速缓解，平衡准确率为79%。逐渐改善与快速缓解仍然可以预测以上的机会，但不太令人信服，与平衡的准确性为61%。生成嵌入优于基于传统（描述性）特征的分类，例如功能连接或局部激活估计，这些特征是从相同的数据中获得的，并且不允许高于机会的分类准确性。此外，GE的预测性能可以被分配到一个特定的网络属性：情感内容对连接的逐个尝试调制。鉴于我们研究的样本量有限，目前的结果是初步的，但可以作为概念验证，说明GE获得临床预测的潜力是可解释的网络机制。我们的研究结果表明，参与情绪面孔处理的连接的异常动态变化可能与发展不太有利的临床过程的风险较高。
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy - generative embedding (GE) - which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.