文章基本信息

标题：A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants
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作者：Samaneh Nemati ; Teddy J. Akiki ; Jeremy Roscoe 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2020
卷号：23
期号：1
页码：1-30
DOI：10.1016/j.isci.2019.100800
语种：English
出版社：Elsevier
摘要：SummaryMore 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.Graphical AbstractDisplay OmittedHighlights•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 antidepressantsDrugs; Medical Imaging; Clinical Neuroscience