mvar model Search Results


voltage tunable attenuator  (Mini-Circuits)
90
Mini-Circuits voltage tunable attenuator
Voltage Tunable Attenuator, supplied by Mini-Circuits, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/10__1364_slash_oe__22__006287-190-20-23?v=Mini-Circuits
Average 90 stars, based on 1 article reviews
voltage tunable attenuator - by Bioz Stars, 2026-07
90/100 stars
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hidden markov model multivariate autoregressive  (MathWorks Inc)
96
MathWorks Inc hidden markov model multivariate autoregressive
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
Hidden Markov Model Multivariate Autoregressive, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/pm37460460-120-3-13?v=MathWorks+Inc
Average 96 stars, based on 1 article reviews
hidden markov model multivariate autoregressive - by Bioz Stars, 2026-07
96/100 stars
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17-inch wide (1280×768 dots) tft liquid crystal panel  (JSR Corporation)
90
JSR Corporation 17-inch wide (1280×768 dots) tft liquid crystal panel
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
17 Inch Wide (1280×768 Dots) Tft Liquid Crystal Panel, supplied by JSR Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/us08508702-238-14-5?v=JSR+Corporation
Average 90 stars, based on 1 article reviews
17-inch wide (1280×768 dots) tft liquid crystal panel - by Bioz Stars, 2026-07
90/100 stars
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(trade name: “kdl20j3000”  (Sony)
90
Sony (trade name: “kdl20j3000”
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
(Trade Name: “Kdl20j3000”, supplied by Sony, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/us09261731-609-32-39?v=Sony
Average 90 stars, based on 1 article reviews
(trade name: “kdl20j3000” - by Bioz Stars, 2026-07
90/100 stars
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autoregressive multivariate linear models  (Sgarlato Labs)
90
Sgarlato Labs autoregressive multivariate linear models
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
Autoregressive Multivariate Linear Models, supplied by Sgarlato Labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/10__47852_slash_bonviewaia42021821-45-15-0?v=Sgarlato+Labs
Average 90 stars, based on 1 article reviews
autoregressive multivariate linear models - by Bioz Stars, 2026-07
90/100 stars
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micro-amylo-graph universal (mvag) 803222  (Anton Paar)
90
Anton Paar micro-amylo-graph universal (mvag) 803222
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
Micro Amylo Graph Universal (Mvag) 803222, supplied by Anton Paar, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/us10316107-92-20-28?v=Anton+Paar
Average 90 stars, based on 1 article reviews
micro-amylo-graph universal (mvag) 803222 - by Bioz Stars, 2026-07
90/100 stars
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multivariate autoregressive model  (TSUMURA)
90
TSUMURA multivariate autoregressive model
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
Multivariate Autoregressive Model, supplied by TSUMURA, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/pm16722069-20-62-45?v=TSUMURA
Average 90 stars, based on 1 article reviews
multivariate autoregressive model - by Bioz Stars, 2026-07
90/100 stars
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multivariate autoregressive model  (SAS institute)
90
SAS institute multivariate autoregressive model
Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden <t>Markov</t> Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.
Multivariate Autoregressive Model, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/mvar+model/10__1080_slash_10705511__2012__634712-15-2-24?v=SAS+institute
Average 90 stars, based on 1 article reviews
multivariate autoregressive model - by Bioz Stars, 2026-07
90/100 stars
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Image Search Results


Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden Markov Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.

Journal: Translational psychiatry

Article Title: Altered brain dynamic in major depressive disorder: state and trait features.

doi: 10.1038/s41398-023-02540-0

Figure Lengend Snippet: Fig. 1 Schematic overview of the study. A Resting-state data from one participant representing 232 imaging volumes). B Schaefer et. al (2018) parcellation with 100 cortical regions and Tian et. al. (2020) parcellation with 16 subcortical regions. C Part of the time-series extracted from the parcellation schemes in Fig. 1B. D Hidden Markov Model (HMM) to calculate the probability of latent states being active at each timepoint of the observed time-series, concatenated from the whole study population. Depicted is the probability of occurrence of any state and each time-point of a part of time-series. The states do not occur sequentially and any of them might occur at any time-point. E Probability of transitioning from one state to any other state across groups.

Article Snippet: We used the Hidden Markov Model - Multivariate Autoregressive (HMM-MAR) toolbox implemented in MATLAB (https:// github.com/OHBA-analysis/HMM-MAR) to perform variational Bayes inversion of HMM with 500 cycles to define states by multivariate Gaussian distribution [49], the finite number of states should be set as a prior in the model.

Techniques: Imaging

Fig. 2 Group comparisons of the temporal features. Applying the hidden Markov model (HMM) resulted in six spatial states, with the brain map of averaged functional activity represented for each state (blue to red is indicating the negative to positive averaged functional activity, range −0.15 to 0.15). This figure contains the finding of fractional occupancy and averaged lifetime of state #1, #4 and #6 and the findings related to states #2, #3 and #5 can be found in Supplementary Fig. 4. The range of −0.15 and 0.15 for the averaged functional activity represents the level of functional activity observed during a particular state in the current dataset. In general, the magnitude and direction of the values can indicate the degree and type of neural activity occurring during a particular state. The positive values may indicate increased neural activity, while negative values may indicate decreased activity. Functional activity is averaged blood-oxygen-level-dependent (BOLD) time-series at that state for each region. The violin plots represent the group comparisons (HC vs. all MDD-diagnosed patients and HC vs. asymptomatic or symptomatic patients) of the temporal features (fractional occupancy and averaged lifetime). The value on the top of each comparison is an uncorrected p-value and the p-values that are significant also after the Bonferroni correction are indicated by red color and asterisks.

Journal: Translational psychiatry

Article Title: Altered brain dynamic in major depressive disorder: state and trait features.

doi: 10.1038/s41398-023-02540-0

Figure Lengend Snippet: Fig. 2 Group comparisons of the temporal features. Applying the hidden Markov model (HMM) resulted in six spatial states, with the brain map of averaged functional activity represented for each state (blue to red is indicating the negative to positive averaged functional activity, range −0.15 to 0.15). This figure contains the finding of fractional occupancy and averaged lifetime of state #1, #4 and #6 and the findings related to states #2, #3 and #5 can be found in Supplementary Fig. 4. The range of −0.15 and 0.15 for the averaged functional activity represents the level of functional activity observed during a particular state in the current dataset. In general, the magnitude and direction of the values can indicate the degree and type of neural activity occurring during a particular state. The positive values may indicate increased neural activity, while negative values may indicate decreased activity. Functional activity is averaged blood-oxygen-level-dependent (BOLD) time-series at that state for each region. The violin plots represent the group comparisons (HC vs. all MDD-diagnosed patients and HC vs. asymptomatic or symptomatic patients) of the temporal features (fractional occupancy and averaged lifetime). The value on the top of each comparison is an uncorrected p-value and the p-values that are significant also after the Bonferroni correction are indicated by red color and asterisks.

Article Snippet: We used the Hidden Markov Model - Multivariate Autoregressive (HMM-MAR) toolbox implemented in MATLAB (https:// github.com/OHBA-analysis/HMM-MAR) to perform variational Bayes inversion of HMM with 500 cycles to define states by multivariate Gaussian distribution [49], the finite number of states should be set as a prior in the model.

Techniques: Functional Assay, Activity Assay, Comparison