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hmm mar toolbox in matlab  (MathWorks Inc)


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    MathWorks Inc hmm mar toolbox in matlab
    Hmm Mar Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/hmm+mar+toolbox+in+matlab/Database+Toolbox/pm40610759-210-11-14
    Average 96 stars, based on 2340 article reviews
    hmm mar toolbox in matlab - by Bioz Stars, 2026-09
    96/100 stars

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    Related Articles

    other:

    Article Title: Exploratory GABAa-informed control network modulates hyperarousal brain dynamics in chronic insomnia
    Article Snippet: We employed the HMM-MAR toolbox in MATLAB ( https://github.com/OHBA-analysis/HMM-MAR ) to decode transient brain states from resting-state fMRI data, using variational Bayes (VB) inversion of an HMM with up to 500 iterations .

    Article Title: Exploratory GABAa-informed control network modulates hyperarousal brain dynamics in chronic insomnia.
    Article Snippet: Decoding brain dynamics utilizing a hidden Markov model We employed the HMM-MAR toolbox in MATLAB (https://github.com/ OHBA-analysis/HMM-MAR) todecode transient brain states fromrestingstate fMRI data, using variational Bayes (VB) inversion of anHMMwith up to 500 iterations17.We used the VB approach forHMM inference because it provides an efficient and scalable way to estimate the model’s posterior distribution, especially when analyzing large-scale, concatenated group data17.



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    ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) <t>Hidden</t> <t>Markov</t> <t>model</t> <t>(HMM)</t> was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.
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    ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) Hidden Markov model (HMM) was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) Hidden Markov model (HMM) was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques: Derivative Assay, Activation Assay, Functional Assay

    State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of an example run.

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of an example run.

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques:

    State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of a second example run.

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of a second example run.

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques:

    The error bars represent the standard error of the mean. Panel ( A ) free energy; Panel ( B ) maximum Occupancy (percentage); Panel ( C ) median Occupancy (percentage); Panel ( D ) Wilk’s Λ; Panel ( E ) mean Hidden Markov model (HMM) state Lifetime (TR, 3 s).

    Journal: eLife

    Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

    doi: 10.7554/eLife.98739

    Figure Lengend Snippet: The error bars represent the standard error of the mean. Panel ( A ) free energy; Panel ( B ) maximum Occupancy (percentage); Panel ( C ) median Occupancy (percentage); Panel ( D ) Wilk’s Λ; Panel ( E ) mean Hidden Markov model (HMM) state Lifetime (TR, 3 s).

    Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

    Techniques: