A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants. 2020

Samaneh Nemati, and Teddy J Akiki, and Jeremy Roscoe, and Yumeng Ju, and Christopher L Averill, and Samar Fouda, and Arpan Dutta, and Shane McKie, and John H Krystal, and J F William Deakin, and Lynnette A Averill, and Chadi G Abdallah
National Center for PTSD - Clinical Neuroscience Division, US Department of Veterans Affairs, 950 Campbell Avenue 151 E, West Haven, CT 06516, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.

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.

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