A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants

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. [Display omitted] •Machine learning methods were used to fully investigate the brain connectome•Network-informed features engineering approaches were proposed•A cortical-subcortical hierarchical brain atlas was established•A specific connectome signature was found to predict response to antidepressants Drugs; Medical Imaging; Clinical Neuroscience