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tapas rdcm toolbox in matlab  (MathWorks Inc)


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    Structured Review

    MathWorks Inc tapas rdcm toolbox in matlab
    Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → <t>rDCM.</t> Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.
    Tapas Rdcm Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 447 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/tapas rdcm toolbox in matlab/product/MathWorks Inc
    Average 96 stars, based on 447 article reviews
    tapas rdcm toolbox in matlab - by Bioz Stars, 2026-05
    96/100 stars

    Images

    1) Product Images from "The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI"

    Article Title: The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI

    Journal: bioRxiv

    doi: 10.1101/2025.07.13.664614

    Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM. Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.
    Figure Legend Snippet: Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM. Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.

    Techniques Used: Control

    QC-FC distance-dependence correlations across datasets. Bars show the distance dependence value for each connectivity metric for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM.
    Figure Legend Snippet: QC-FC distance-dependence correlations across datasets. Bars show the distance dependence value for each connectivity metric for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM.

    Techniques Used: Control

    KRR prediction accuracies across datasets. The boxplots show the median and interquartile ranges for accuracies averaged over cross validation folds, repetitions, and behaviours. Mean predictive accuracy for each connectivity measure is shown with a white triangle. Prediction accuracies are shown for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM
    Figure Legend Snippet: KRR prediction accuracies across datasets. The boxplots show the median and interquartile ranges for accuracies averaged over cross validation folds, repetitions, and behaviours. Mean predictive accuracy for each connectivity measure is shown with a white triangle. Prediction accuracies are shown for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM

    Techniques Used: Biomarker Discovery, Control



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    Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → <t>rDCM.</t> Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.
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    Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → <t>rDCM.</t> Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.
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    Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → <t>rDCM.</t> Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.
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    Image Search Results


    Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM. Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.

    Journal: bioRxiv

    Article Title: The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI

    doi: 10.1101/2025.07.13.664614

    Figure Lengend Snippet: Distribution of QC-FC correlations across all methods for quantifying FC/EC for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM. Distribution means (μ) are presented in orange text, while population variance (02) is presented in purple text.

    Article Snippet: All analyses were implemented using the TAPAS rDCM toolbox in MATLAB ( a; b).

    Techniques: Control

    QC-FC distance-dependence correlations across datasets. Bars show the distance dependence value for each connectivity metric for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM.

    Journal: bioRxiv

    Article Title: The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI

    doi: 10.1101/2025.07.13.664614

    Figure Lengend Snippet: QC-FC distance-dependence correlations across datasets. Bars show the distance dependence value for each connectivity metric for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM.

    Article Snippet: All analyses were implemented using the TAPAS rDCM toolbox in MATLAB ( a; b).

    Techniques: Control

    KRR prediction accuracies across datasets. The boxplots show the median and interquartile ranges for accuracies averaged over cross validation folds, repetitions, and behaviours. Mean predictive accuracy for each connectivity measure is shown with a white triangle. Prediction accuracies are shown for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM

    Journal: bioRxiv

    Article Title: The Motion Sensitivity and Predictive Utility of Different Estimates of Inter-regional Functional Coupling in Resting-state Functional MRI

    doi: 10.1101/2025.07.13.664614

    Figure Lengend Snippet: KRR prediction accuracies across datasets. The boxplots show the median and interquartile ranges for accuracies averaged over cross validation folds, repetitions, and behaviours. Mean predictive accuracy for each connectivity measure is shown with a white triangle. Prediction accuracies are shown for ( A ) HCP participants meeting stringent motion criteria, ( B ) ABCD participants passing standard quality control inclusion criteria, and ( C ) ABCD participants meeting stringent motion exclusion thresholds. Measures are ordered as: Correlation → Partial correlation → Mutual Information → Coherence → PID → rDCM

    Article Snippet: All analyses were implemented using the TAPAS rDCM toolbox in MATLAB ( a; b).

    Techniques: Biomarker Discovery, Control