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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.
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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.
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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.
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functions from brain connectivity toolbox  (MathWorks Inc)
90
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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.
Functions From 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/pmc04554838-96-11-8
Average 90 stars, based on 1 article reviews
functions from brain connectivity toolbox - by Bioz Stars, 2026-09
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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