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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992944/ https://www.ncbi.nlm.nih.gov/pubmed/31918047 http://dx.doi.org/10.1016/j.isci.2019.100800 |
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author | Nemati, Samaneh Akiki, Teddy J. Roscoe, Jeremy Ju, Yumeng Averill, Christopher L. Fouda, Samar Dutta, Arpan McKie, Shane Krystal, John H. Deakin, J.F. William Averill, Lynnette A. Abdallah, Chadi G. |
author_facet | Nemati, Samaneh Akiki, Teddy J. Roscoe, Jeremy Ju, Yumeng Averill, Christopher L. Fouda, Samar Dutta, Arpan McKie, Shane Krystal, John H. Deakin, J.F. William Averill, Lynnette A. Abdallah, Chadi G. |
author_sort | Nemati, Samaneh |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6992944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-69929442020-02-03 A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants Nemati, Samaneh Akiki, Teddy J. Roscoe, Jeremy Ju, Yumeng Averill, Christopher L. Fouda, Samar Dutta, Arpan McKie, Shane Krystal, John H. Deakin, J.F. William Averill, Lynnette A. Abdallah, Chadi G. iScience Article 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. Elsevier 2019-12-23 /pmc/articles/PMC6992944/ /pubmed/31918047 http://dx.doi.org/10.1016/j.isci.2019.100800 Text en http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Nemati, Samaneh Akiki, Teddy J. Roscoe, Jeremy Ju, Yumeng Averill, Christopher L. Fouda, Samar Dutta, Arpan McKie, Shane Krystal, John H. Deakin, J.F. William Averill, Lynnette A. Abdallah, Chadi G. A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants |
title | A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants |
title_full | A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants |
title_fullStr | A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants |
title_full_unstemmed | A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants |
title_short | A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants |
title_sort | unique brain connectome fingerprint predates and predicts response to antidepressants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6992944/ https://www.ncbi.nlm.nih.gov/pubmed/31918047 http://dx.doi.org/10.1016/j.isci.2019.100800 |
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