More than six decades have passed since the discovery of monoaminergic antidepressants. Yet, it remains a mystery why these drugs take weeks to months to achieve therapeutic effects, although their monoaminergic actions are present rapidly after treatment. In an attempt to solve this mystery, rather than studying the acute neurochemical effects of antidepressants, here we propose focusing on the early changes in the brain functional connectome using traditional statistics and machine learning approaches. Capitalizing on three independent datasets (n = 1,261) and recent developments in data and network science, we identified a specific connectome fingerprint that predates and predicts response to monoaminergic antidepressants. The discovered fingerprint appears to generalize to antidepressants with differing mechanism of action. We also established a consensus whole-brain hierarchical connectivity architecture and provided a set of model-based features engineering approaches suitable for identifying connectomic signatures of brain function in health and disease.

自发现单胺能抗抑郁药以来已经过去了六十多年。然而，尽管这些药物的单胺能作用在治疗后迅速出现，但是为什么这些药物需要数周至数月才能达到治疗效果仍是个谜。为了解决这个谜团，而不是研究抗抑郁药的急性神经化学作用，在此我们建议使用传统的统计学方法和机器学习方法，着重研究脑功能连接体的早期变化。利用三个独立的数据集（n = 1,261）以及数据和网络科学的最新发展，我们确定了一个特定的连接组指纹，该指纹早于并预测对单胺类抗抑郁药的反应。发现的指纹似乎可以推广到具有不同作用机理的抗抑郁药。