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Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders with neurobiological and clinical heterogeneity could benefit from the identification of disease subtypes on the basis of data acquired with established neuroimaging technologies. Here, we report the identification of two clinically relevant s...

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Autores principales: Zhang, Yu, Wu, Wei, Toll, Russell T., Naparstek, Sharon, Maron-Katz, Adi, Watts, Mallissa, Gordon, Joseph, Jeong, Jisoo, Astolfi, Laura, Shpigel, Emmanuel, Longwell, Parker, Sarhadi, Kamron, El-Said, Dawlat, Li, Yuanqing, Cooper, Crystal, Chin-Fatt, Cherise, Arns, Martijn, Goodkind, Madeleine S., Trivedi, Madhukar H., Marmar, Charles R., Etkin, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053667/
https://www.ncbi.nlm.nih.gov/pubmed/33077939
http://dx.doi.org/10.1038/s41551-020-00614-8
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author Zhang, Yu
Wu, Wei
Toll, Russell T.
Naparstek, Sharon
Maron-Katz, Adi
Watts, Mallissa
Gordon, Joseph
Jeong, Jisoo
Astolfi, Laura
Shpigel, Emmanuel
Longwell, Parker
Sarhadi, Kamron
El-Said, Dawlat
Li, Yuanqing
Cooper, Crystal
Chin-Fatt, Cherise
Arns, Martijn
Goodkind, Madeleine S.
Trivedi, Madhukar H.
Marmar, Charles R.
Etkin, Amit
author_facet Zhang, Yu
Wu, Wei
Toll, Russell T.
Naparstek, Sharon
Maron-Katz, Adi
Watts, Mallissa
Gordon, Joseph
Jeong, Jisoo
Astolfi, Laura
Shpigel, Emmanuel
Longwell, Parker
Sarhadi, Kamron
El-Said, Dawlat
Li, Yuanqing
Cooper, Crystal
Chin-Fatt, Cherise
Arns, Martijn
Goodkind, Madeleine S.
Trivedi, Madhukar H.
Marmar, Charles R.
Etkin, Amit
author_sort Zhang, Yu
collection PubMed
description The understanding and treatment of psychiatric disorders with neurobiological and clinical heterogeneity could benefit from the identification of disease subtypes on the basis of data acquired with established neuroimaging technologies. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional-connectivity patterns, prominently within the frontoparietal-control and default-mode networks. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets from patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.
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spelling pubmed-80536672021-04-19 Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography Zhang, Yu Wu, Wei Toll, Russell T. Naparstek, Sharon Maron-Katz, Adi Watts, Mallissa Gordon, Joseph Jeong, Jisoo Astolfi, Laura Shpigel, Emmanuel Longwell, Parker Sarhadi, Kamron El-Said, Dawlat Li, Yuanqing Cooper, Crystal Chin-Fatt, Cherise Arns, Martijn Goodkind, Madeleine S. Trivedi, Madhukar H. Marmar, Charles R. Etkin, Amit Nat Biomed Eng Article The understanding and treatment of psychiatric disorders with neurobiological and clinical heterogeneity could benefit from the identification of disease subtypes on the basis of data acquired with established neuroimaging technologies. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional-connectivity patterns, prominently within the frontoparietal-control and default-mode networks. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets from patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis. 2020-10-19 /pmc/articles/PMC8053667/ /pubmed/33077939 http://dx.doi.org/10.1038/s41551-020-00614-8 Text en http://www.nature.com/authors/editorial_policies/license.html#termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms http://www.nature.com/reprintsReprints and permissions information is available at www.nature.com/reprints (http://www.nature.com/reprints) .
spellingShingle Article
Zhang, Yu
Wu, Wei
Toll, Russell T.
Naparstek, Sharon
Maron-Katz, Adi
Watts, Mallissa
Gordon, Joseph
Jeong, Jisoo
Astolfi, Laura
Shpigel, Emmanuel
Longwell, Parker
Sarhadi, Kamron
El-Said, Dawlat
Li, Yuanqing
Cooper, Crystal
Chin-Fatt, Cherise
Arns, Martijn
Goodkind, Madeleine S.
Trivedi, Madhukar H.
Marmar, Charles R.
Etkin, Amit
Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
title Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
title_full Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
title_fullStr Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
title_full_unstemmed Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
title_short Identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
title_sort identification of psychiatric-disorder subtypes from functional-connectivity patterns in resting-state electroencephalography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053667/
https://www.ncbi.nlm.nih.gov/pubmed/33077939
http://dx.doi.org/10.1038/s41551-020-00614-8
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