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Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model
Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper ins...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861306/ https://www.ncbi.nlm.nih.gov/pubmed/35211000 http://dx.doi.org/10.3389/fnhum.2022.774921 |
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author | Lin, Pingting Zang, Shiyi Bai, Yi Wang, Haixian |
author_facet | Lin, Pingting Zang, Shiyi Bai, Yi Wang, Haixian |
author_sort | Lin, Pingting |
collection | PubMed |
description | Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD. |
format | Online Article Text |
id | pubmed-8861306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88613062022-02-23 Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model Lin, Pingting Zang, Shiyi Bai, Yi Wang, Haixian Front Hum Neurosci Neuroscience Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model (HMM) analysis for resting-state functional MRI (fMRI) from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange (ABIDE). Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls (HCs). Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states (including fractional occupancies and lifetimes) were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network (DMN), sensory processing networks [including visual network, auditory network, and sensory and motor network (SMN)]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD. Frontiers Media S.A. 2022-02-08 /pmc/articles/PMC8861306/ /pubmed/35211000 http://dx.doi.org/10.3389/fnhum.2022.774921 Text en Copyright © 2022 Lin, Zang, Bai and Wang. 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 | Neuroscience Lin, Pingting Zang, Shiyi Bai, Yi Wang, Haixian Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model |
title | Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model |
title_full | Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model |
title_fullStr | Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model |
title_full_unstemmed | Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model |
title_short | Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model |
title_sort | reconfiguration of brain network dynamics in autism spectrum disorder based on hidden markov model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861306/ https://www.ncbi.nlm.nih.gov/pubmed/35211000 http://dx.doi.org/10.3389/fnhum.2022.774921 |
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