ArticleIEEE transactions on medical imaging2020
Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model.
Article in IEEE transactions on medical imaging, 2020. 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.
- Neurophysiological effects of transcranial alternating current stimulation combined with multidisciplinary rehabilitation in Parkinson's disease.Frontiers in psychiatry · 2026Trial
- Aberrant recovery of timescale-aligned amplitude balance links to symptoms and cognition in schizophrenia.Translational psychiatry · 2026Article
- Dual-attention temporal graph neural network on resting-state fMRI dynamic functional connectivity identifies risk-related patterns of migraine chronification.BMC medical imaging · 2026Article
- Unveiling state-specific neural dynamics in migraine with and without depressive symptom: a hidden Markov model and interpretable machine learning approach.The journal of headache and pain · 2026Article
- Aberrant Recovery of Timescale-Aligned Amplitude Balance Links to Symptoms and Cognition in Schizophrenia.Research square · 2026Article
- Studying Time-Resolved Functional Connectivity via Communication Theory: On the Complementary Nature of Phase Synchronization and Sliding Window Pearson Correlation.Brain connectivity · 2025Article
- Timescale-normalized fMRI reveals disrupted dynamic signal-energy balance in schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.bioRxiv : the preprint server for biology · 2025Article
- An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis.IEEE transactions on medical imaging · 2025Article
- Time-varying brain state dynamics in trait impulsivity and anxiety: An HSMM analysis of resting-state fMRI.Social cognitive and affective neuroscience · 2025Article
- A window into the brain: multimodal MRI assessment of vascular cognitive impairment.Frontiers in neuroscience · 2025Review
- Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models.Journal of translational medicine · 2024Article
- Study of dynamic brain function in irritable bowel syndrome via Hidden Markov Modeling.Frontiers in neuroscience · 2024Article
- Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex.PLoS computational biology · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Improved dynamic functional connectivity estimation with an alternating hidden Markov model.Cognitive neurodynamics · 2023Article
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
- An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development.Neural networks : the official journal of the International Neural Network Society · 2023Article
- Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing.Human brain mapping · 2023Article
- Human Activity Recognition with an HMM-Based Generative Model.Sensors (Basel, Switzerland) · 2023Article
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7 authors.
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Abstract
Estimating dynamic functional network connectivity (dFNC) of the brain from functional magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization and can be applied to track the developmental trajectory of brain maturity as well as to study mental illness. Resting state fMRI (rs-fMRI) is regarded as a promising task since it reflects the spontaneous brain activity without an external stimulus. The sliding window method has been successfully used to extract dFNC but typically assumes a fixed window size. The hidden Markov model (HMM) based method is an alternative approach for estimating time-varying connectivity. In this paper, we propose a sparse HMM based on Gaussian HMM and Gaussian graphical model (GGM). In this model, the time-varying neural processes are represented as discrete brain states which are described with functional connectivity networks. By enforcing the sparsity on the precision matrix, we can get interpretable connectivity between different functional regions. The optimization of our model can be realized with the expectation maximization (EM) and graphical least absolute shrinkage and selection operator (glasso) algorithms. The proposed model is validated on both simulated blood oxygenation-level dependent (BOLD) time series and rs-fMRI data. Results indicate that the proposed model can capture both stationary and abrupt brain activity fluctuations. We also compare dFNC patterns between children and young adults from the Philadelphia Neurodevelopmental Cohort (PNC) study. Both spatial and temporal behavior of the dFNC are analyzed and compared. The results provide insight into the developmental trajectory across childhood and motivate further research on brain connectivity.
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