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


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    Structured Review

    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/function+from+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, 2026-10
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    Images

    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

    Related Articles

    Functional Assay:

    Article Title: Brain region networks for the assimilation of new associative memory into a schema
    Article Snippet: Module detection was carried out using the community_louvain function in the Brain Connectivity Toolbox within MATLAB [ ].

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

    Article Title: A multilayer network model of neuron-astrocyte populations in vitro reveals mGluR 5 inhibition is protective following traumatic injury
    Article Snippet: 12), constructed with a Newman-Girvan null model ( ) (Fig. S13), using a Louvain-like locally greedy algorithm implemented in MATLAB ( ).

    Article Title: Modularity maximization as a flexible and generic framework for brain network exploratory analysis.
    Article Snippet: A generalized Louvain method for community detection implemented in MATLAB.

    Article Title: Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum
    Article Snippet: Using a modularity maximization method, we identified brain network modules with a Louvain-like greedy algorithm implemented in MATLAB.

    Article Title: Developmental differences in functional organization of multispectral networks.
    Article Snippet: The community structure of each participant’s 200 × 200 connectivity matrix was calculated using a Louvain-like locally greedy algorithm implemented in MATLAB (Jeub et al. 2011), which is a data-driven approach that assigns each node to a constituent module (i.e. a community) based on the density of interconnectivity with other nodes.

    Article Title: Individualized event structure drives individual differences in whole-brain functional connectivity.
    Article Snippet: A generalized louvain method for community de- tection implemented in MATLAB.

    Extraction:

    Article Title: Brain region networks for the assimilation of new associative memory into a schema
    Article Snippet: Module detection was carried out using the community_louvain function in the Brain Connectivity Toolbox within MATLAB [ ].

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

    Article Title: A multilayer network model of neuron-astrocyte populations in vitro reveals mGluR 5 inhibition is protective following traumatic injury
    Article Snippet: 12), constructed with a Newman-Girvan null model ( ) (Fig. S13), using a Louvain-like locally greedy algorithm implemented in MATLAB ( ).

    Article Title: Modularity maximization as a flexible and generic framework for brain network exploratory analysis.
    Article Snippet: A generalized Louvain method for community detection implemented in MATLAB.

    Article Title: Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum
    Article Snippet: Using a modularity maximization method, we identified brain network modules with a Louvain-like greedy algorithm implemented in MATLAB.

    Article Title: Developmental differences in functional organization of multispectral networks.
    Article Snippet: The community structure of each participant’s 200 × 200 connectivity matrix was calculated using a Louvain-like locally greedy algorithm implemented in MATLAB (Jeub et al. 2011), which is a data-driven approach that assigns each node to a constituent module (i.e. a community) based on the density of interconnectivity with other nodes.

    Article Title: Individualized event structure drives individual differences in whole-brain functional connectivity.
    Article Snippet: A generalized louvain method for community de- tection implemented in MATLAB.



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    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