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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Human Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2022.774921/full |
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| author | Pingting Lin Pingting Lin Pingting Lin Shiyi Zang Shiyi Zang Shiyi Zang Yi Bai Yi Bai Yi Bai Haixian Wang Haixian Wang Haixian Wang |
| author_facet | Pingting Lin Pingting Lin Pingting Lin Shiyi Zang Shiyi Zang Shiyi Zang Yi Bai Yi Bai Yi Bai Haixian Wang Haixian Wang Haixian Wang |
| author_sort | Pingting Lin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-24T02:59:56Z |
| format | Article |
| id | doaj.art-87134151ff7f4e27853cdc590d477d79 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-24T02:59:56Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-87134151ff7f4e27853cdc590d477d792022-12-21T17:18:14ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612022-02-011610.3389/fnhum.2022.774921774921Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov ModelPingting Lin0Pingting Lin1Pingting Lin2Shiyi Zang3Shiyi Zang4Shiyi Zang5Yi Bai6Yi Bai7Yi Bai8Haixian Wang9Haixian Wang10Haixian Wang11School of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, ChinaResearch Center for Learning Science, Southeast University, Nanjing, ChinaSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, ChinaResearch Center for Learning Science, Southeast University, Nanjing, ChinaSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, ChinaResearch Center for Learning Science, Southeast University, Nanjing, ChinaSchool of Biological Science and Medical Engineering, Southeast University, Nanjing, ChinaKey Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, ChinaResearch Center for Learning Science, Southeast University, Nanjing, ChinaAutism 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.https://www.frontiersin.org/articles/10.3389/fnhum.2022.774921/fullautism spectrum disorderHidden Markov Modelslarge-scale whole-brain networkglobal temporal dynamicsmodularity analysis |
| spellingShingle | Pingting Lin Pingting Lin Pingting Lin Shiyi Zang Shiyi Zang Shiyi Zang Yi Bai Yi Bai Yi Bai Haixian Wang Haixian Wang Haixian Wang Reconfiguration of Brain Network Dynamics in Autism Spectrum Disorder Based on Hidden Markov Model Frontiers in Human Neuroscience autism spectrum disorder Hidden Markov Models large-scale whole-brain network global temporal dynamics modularity analysis |
| 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 | autism spectrum disorder Hidden Markov Models large-scale whole-brain network global temporal dynamics modularity analysis |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2022.774921/full |
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