Yale University School of Medicine, New Haven, CT, USA; Yale University Department of Psychiatry, New Haven, CT, USA.
Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA.
Neuroimage Clin. 2018 Aug 11;20:407-414. doi: 10.1016/j.nicl.2018.08.016. eCollection 2018.
Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n = 306) exploring the effect of antidepressant administration on emotional face processing.
We created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies.
We found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50 to 87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another.
We found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development.
临床认可的抗抑郁药调节大脑的情绪效价回路，这表明这些回路的反应可以作为筛选候选抗抑郁药物的生物标志物。然而，有必要可靠地检测到这些调制。在这里，我们应用经过交叉验证的预测模型，对 11 个基于任务的 fMRI 数据集（n=306）进行分类，以探索抗抑郁药给药对情绪面孔处理的影响。
我们创建了情绪面孔任务的参数估计的个体水平对比，并使用 Shen 全脑分割方案来定义 268 个个体水平的特征，这些特征经过交叉验证的梯度提升机协议进行训练，以在个体内和个体间的研究中分类情绪效价（恐惧与快乐面孔视觉条件）和药理学效应（药物与安慰剂给药）。
我们发现了可以以统计学显著的准确度（所有受试者的 70%；跨研究的范围为 50-87%）分类情绪效价的大脑活动模式。我们的分类器未能一致地区分药物与安慰剂。受试者人群（健康或不健康）、治疗组（药物或安慰剂）和药物给药方案（剂量和持续时间）以类似的人群更好地预测彼此的方式影响了这一准确性。
我们发现有限的证据表明抗抑郁药以一致的方式调节大脑反应，但发现了情绪效价的一致特征。跨研究的可变功能模式表明，预测模型可以为精神健康和药物治疗开发中的生物标志物开发提供信息。我们的结果表明，需要进行病例对照设计和更标准化的方案，以便功能成像为药物开发提供稳健的生物标志物。
