ArticleBrain sciences2024
Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study.
Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Autism Spectrum Disorder (ASD) Through the Lens of Hidden Markov Models (HMMs) Applied to Resting-State fMRI (rs-fMRI): A Systematic Review and Meta-analysis.Journal of autism and developmental disorders · 2026Article
- Dissociable, species-specific impact of Aβ on static and dynamic functional connectomes.bioRxiv : the preprint server for biology · 2026Article
- A review of decomposition methods for brain states estimation.Biomedical engineering online · 2026Review
- Multi-Site Classification of Autism Spectrum Disorder Using Spatially Constrained ICA on Resting-State fMRI Networks.Brain sciences · 2026Article
- Multi-view united transformer block of graph attention network based autism spectrum disorder recognition.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Registered trials
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