A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants

Samaneh Nemati, Teddy J Akiki, Jeremy Roscoe, Yumeng Ju, Christopher L Averill, Samar Fouda, Arpan Dutta, Shane McKie, John H Krystal, J F William Deakin, Lynnette A Averill, Chadi G Abdallah

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)100800
Issue number1
Early online date23 Dec 2019
Publication statusPublished - 24 Jan 2020


Dive into the research topics of 'A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants'. Together they form a unique fingerprint.

Cite this