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Connectomic disturbances underlying insomnia disorder and predictors of treatment response
OBJECTIVE: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimula...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399490/ https://www.ncbi.nlm.nih.gov/pubmed/36034119 http://dx.doi.org/10.3389/fnhum.2022.960350 |
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author | Lu, Qian Zhang, Wentong Yan, Hailang Mansouri, Negar Tanglay, Onur Osipowicz, Karol Joyce, Angus W. Young, Isabella M. Zhang, Xia Doyen, Stephane Sughrue, Michael E. He, Chuan |
author_facet | Lu, Qian Zhang, Wentong Yan, Hailang Mansouri, Negar Tanglay, Onur Osipowicz, Karol Joyce, Angus W. Young, Isabella M. Zhang, Xia Doyen, Stephane Sughrue, Michael E. He, Chuan |
author_sort | Lu, Qian |
collection | PubMed |
description | OBJECTIVE: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy. MATERIALS AND METHODS: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up. RESULTS: Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change. CONCLUSION: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets. |
format | Online Article Text |
id | pubmed-9399490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93994902022-08-25 Connectomic disturbances underlying insomnia disorder and predictors of treatment response Lu, Qian Zhang, Wentong Yan, Hailang Mansouri, Negar Tanglay, Onur Osipowicz, Karol Joyce, Angus W. Young, Isabella M. Zhang, Xia Doyen, Stephane Sughrue, Michael E. He, Chuan Front Hum Neurosci Human Neuroscience OBJECTIVE: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy. MATERIALS AND METHODS: 51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up. RESULTS: Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change. CONCLUSION: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399490/ /pubmed/36034119 http://dx.doi.org/10.3389/fnhum.2022.960350 Text en Copyright © 2022 Lu, Zhang, Yan, Mansouri, Tanglay, Osipowicz, Joyce, Young, Zhang, Doyen, Sughrue and He. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Human Neuroscience Lu, Qian Zhang, Wentong Yan, Hailang Mansouri, Negar Tanglay, Onur Osipowicz, Karol Joyce, Angus W. Young, Isabella M. Zhang, Xia Doyen, Stephane Sughrue, Michael E. He, Chuan Connectomic disturbances underlying insomnia disorder and predictors of treatment response |
title | Connectomic disturbances underlying insomnia disorder and predictors of treatment response |
title_full | Connectomic disturbances underlying insomnia disorder and predictors of treatment response |
title_fullStr | Connectomic disturbances underlying insomnia disorder and predictors of treatment response |
title_full_unstemmed | Connectomic disturbances underlying insomnia disorder and predictors of treatment response |
title_short | Connectomic disturbances underlying insomnia disorder and predictors of treatment response |
title_sort | connectomic disturbances underlying insomnia disorder and predictors of treatment response |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399490/ https://www.ncbi.nlm.nih.gov/pubmed/36034119 http://dx.doi.org/10.3389/fnhum.2022.960350 |
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