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louvain’s algorithm function from the brain connectivity toolbox  (MathWorks Inc)


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    MathWorks Inc louvain’s algorithm function from the brain connectivity toolbox
    a Resting-state functional MRI pre-processing pipeline and time-series extraction from functional atlases. b Pearson correlation matrices. c Optimal local threshold estimation; the network edge density at which Q−Q rand is maximum. d Thresholded matrices by optimal density using local and global threshold network construction methods. e <t>Louvain’s</t> community and modular dissociation (MD) estimation, see also Supplementary Fig . f Modular variability (MV) using consensus community. g Group means MD; subcortical regions and cerebellum showed in all groups high MD while motor-sensory, frontal, temporal pole and occipital cortex show low MD. h Group mean MV; patterns of high and low MV were consistent across all groups. Motor-sensory, occipital, and temporal pole showed low MV while parietal, ventral frontal and insulo-opercular cortices showed high MV.
    Louvain’s Algorithm Function From The Brain Connectivity Toolbox, supplied by MathWorks Inc, 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/louvain%E2%80%99s+algorithm+function+from+the+brain+connectivity+toolbox/pmc08367990-309-27-37
    Average 90 stars, based on 1 article reviews
    louvain’s algorithm function from the brain connectivity toolbox - by Bioz Stars, 2026-08
    90/100 stars

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    1) Product Images from "The functional brain favours segregated modular connectivity at old age unless affected by neurodegeneration"

    Article Title: The functional brain favours segregated modular connectivity at old age unless affected by neurodegeneration

    Journal: Communications Biology

    doi: 10.1038/s42003-021-02497-0

    a Resting-state functional MRI pre-processing pipeline and time-series extraction from functional atlases. b Pearson correlation matrices. c Optimal local threshold estimation; the network edge density at which Q−Q rand is maximum. d Thresholded matrices by optimal density using local and global threshold network construction methods. e Louvain’s community and modular dissociation (MD) estimation, see also Supplementary Fig . f Modular variability (MV) using consensus community. g Group means MD; subcortical regions and cerebellum showed in all groups high MD while motor-sensory, frontal, temporal pole and occipital cortex show low MD. h Group mean MV; patterns of high and low MV were consistent across all groups. Motor-sensory, occipital, and temporal pole showed low MV while parietal, ventral frontal and insulo-opercular cortices showed high MV.
    Figure Legend Snippet: a Resting-state functional MRI pre-processing pipeline and time-series extraction from functional atlases. b Pearson correlation matrices. c Optimal local threshold estimation; the network edge density at which Q−Q rand is maximum. d Thresholded matrices by optimal density using local and global threshold network construction methods. e Louvain’s community and modular dissociation (MD) estimation, see also Supplementary Fig . f Modular variability (MV) using consensus community. g Group means MD; subcortical regions and cerebellum showed in all groups high MD while motor-sensory, frontal, temporal pole and occipital cortex show low MD. h Group mean MV; patterns of high and low MV were consistent across all groups. Motor-sensory, occipital, and temporal pole showed low MV while parietal, ventral frontal and insulo-opercular cortices showed high MV.

    Techniques Used: Functional Assay, Extraction



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    louvain’s algorithm function from the brain connectivity toolbox  (MathWorks Inc)
    90
    MathWorks Inc louvain’s algorithm function from the brain connectivity toolbox
    a Resting-state functional MRI pre-processing pipeline and time-series extraction from functional atlases. b Pearson correlation matrices. c Optimal local threshold estimation; the network edge density at which Q−Q rand is maximum. d Thresholded matrices by optimal density using local and global threshold network construction methods. e <t>Louvain’s</t> community and modular dissociation (MD) estimation, see also Supplementary Fig . f Modular variability (MV) using consensus community. g Group means MD; subcortical regions and cerebellum showed in all groups high MD while motor-sensory, frontal, temporal pole and occipital cortex show low MD. h Group mean MV; patterns of high and low MV were consistent across all groups. Motor-sensory, occipital, and temporal pole showed low MV while parietal, ventral frontal and insulo-opercular cortices showed high MV.
    Louvain’s Algorithm Function From The Brain Connectivity Toolbox, supplied by MathWorks Inc, 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/louvain%E2%80%99s+algorithm+function+from+the+brain+connectivity+toolbox/pmc08367990-309-27-37
    Average 90 stars, based on 1 article reviews
    louvain’s algorithm function from the brain connectivity toolbox - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    a Resting-state functional MRI pre-processing pipeline and time-series extraction from functional atlases. b Pearson correlation matrices. c Optimal local threshold estimation; the network edge density at which Q−Q rand is maximum. d Thresholded matrices by optimal density using local and global threshold network construction methods. e Louvain’s community and modular dissociation (MD) estimation, see also Supplementary Fig . f Modular variability (MV) using consensus community. g Group means MD; subcortical regions and cerebellum showed in all groups high MD while motor-sensory, frontal, temporal pole and occipital cortex show low MD. h Group mean MV; patterns of high and low MV were consistent across all groups. Motor-sensory, occipital, and temporal pole showed low MV while parietal, ventral frontal and insulo-opercular cortices showed high MV.

    Journal: Communications Biology

    Article Title: The functional brain favours segregated modular connectivity at old age unless affected by neurodegeneration

    doi: 10.1038/s42003-021-02497-0

    Figure Lengend Snippet: a Resting-state functional MRI pre-processing pipeline and time-series extraction from functional atlases. b Pearson correlation matrices. c Optimal local threshold estimation; the network edge density at which Q−Q rand is maximum. d Thresholded matrices by optimal density using local and global threshold network construction methods. e Louvain’s community and modular dissociation (MD) estimation, see also Supplementary Fig . f Modular variability (MV) using consensus community. g Group means MD; subcortical regions and cerebellum showed in all groups high MD while motor-sensory, frontal, temporal pole and occipital cortex show low MD. h Group mean MV; patterns of high and low MV were consistent across all groups. Motor-sensory, occipital, and temporal pole showed low MV while parietal, ventral frontal and insulo-opercular cortices showed high MV.

    Article Snippet: For this, the Hadamard product between the binarised matrix and the original weighted matrix was computed, and used for community structure and modularity statistic estimation using the Louvain’s algorithm function from the Brain Connectivity Toolbox (BCT) in Matlab (Mathworks Inc, R2017a).

    Techniques: Functional Assay, Extraction