ArticleBrain topography2015
Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models.
Article in Brain topography, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Characteristic spatiotemporal features of large-scale functional network architecture in posttraumatic stress disorder.Translational psychiatry · 2026Article
- A comprehensive framework for statistical testing of brain dynamics.Nature protocols · 2026Review
- When Can Brain Connectivity Track the Working Mind? A Large-Scale Benchmark of Dynamic Functional Connectivity Across Cognitive Paradigms.bioRxiv : the preprint server for biology · 2026Article
- Resting-state fMRI using hidden Markov models reveals abnormal dynamic brain functional states in asthma.Scientific reports · 2026Article
- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Revealing brain network dynamics during the emotional state of suspense using TDA.Network neuroscience (Cambridge, Mass.) · 2025Article
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- Article
- Generative dynamical models for classification of rsfMRI data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.Brain connectivity · 2023Article
- Subcortical-cortical dynamical states of the human brain and their breakdown in stroke.Nature communications · 2022Article
- Article
- Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks.Human brain mapping · 2022Article
- Multi-timepoint pattern analysis: Influence of personality and behavior on decoding context-dependent brain connectivity dynamics.Human brain mapping · 2022Article
- Multiframe Evolving Dynamic Functional ConnectivityFrontiers in neuroscience · 2022Article
- Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning.Computational intelligence and neuroscience · 2021Review
- Intrinsic Network Brain Dysfunction Correlates With Temporal Complexity in Generalized Anxiety Disorder and Panic Disorder.Frontiers in human neuroscience · 2021Article
- Questions and controversies in the study of time-varying functional connectivity in resting fMRI.Network neuroscience (Cambridge, Mass.) · 2020Review
- Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network.Human brain mapping · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain's functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain's functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84% of PTSD patients and 86% of NC subjects are successfully classified via multiple HMMs using majority voting.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.