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e isc toolbox  (MathWorks Inc)


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    MathWorks Inc e isc toolbox
    E Isc Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1226 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/isc+toolbox/Control+System+Toolbox/bio_rxiv__2025__11__06__686932-67-9-11
    Average 96 stars, based on 1226 article reviews
    e isc toolbox - by Bioz Stars, 2026-09
    96/100 stars

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    Related Articles

    Software:

    Article Title: A versatile software package for inter-subject correlation based analyses of fMRI
    Article Snippet: .. In this paper, we present a graphical user interface (GUI) based software package, ISC Toolbox, implemented in Matlab for computing various ISC based analyses. ..

    Article Title: A versatile software package for inter-subject correlation based analyses of fMRI
    Article Snippet: .. We have presented a software package, named ISC Toolbox, implemented in Matlab for computing various ISC based analyses. ..

    Comparison:

    Article Title: Untangling the Relatedness among Correlations, Part I: Nonparametric Approaches to Inter-Subject Correlation Analysis at the Group Level
    Article Snippet: .. For direct comparisons, we applied the permutation approach implemented in the ISC Toolbox (version 2.1, using the recommended default with 100 million randomizations; Kauppi et al., 2014 ) in Matlab (version R2015b) to the male group of 24 subjects (two group comparison is currently not available in the toolbox). ..

    other:

    Article Title: Brain and behavioral alterations in subjects with social anxiety dominated by empathic embarrassment
    Article Snippet: We performed data-driven FuSeISC ( ) of brain regions using the ISC toolbox ( ) implemented in Matlab.



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    Pipeline for the analysis. ( Upper ) The analysis steps of associations between TKS scores and overall brain connectivity during the empathic embarrassment task: construction of group-level brain template <t>by</t> <t>FuSeISC</t> (see Lower for explanation of <t>ISC</t> mean and variability) (i); estimation of connectivity networks for EMBAR and PRIDE (ii); computation of overall connectivity strength for each subject for multiple choices of connectivity parameters ( SI Appendix ) (iii); correlations between overall connectivity strengths and TKS scores, separately for each parameter combination (iv); and statistical evaluation of the average correlations across parameter combinations (v). ( Lower ) Concepts of ISC mean and ISC variability, used as inherent features to divide brain areas into functional segments. High mean ISC corresponds to similar fMRI time courses across multiple subject pairs (mainly positive ISCs). Meanwhile, high ISC variability corresponds to varying fMRI time courses across subject pairs, that is, similar (positive ISC), dissimilar (no ISC), and opposite (negative ISC). Some brain regions, such as early sensory areas, are typically characterized by high ISC means, whereas, for example, certain higher-order brain areas are potentially characterized by relatively high ISC variability .
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    Image Search Results


    Pipeline for the analysis. ( Upper ) The analysis steps of associations between TKS scores and overall brain connectivity during the empathic embarrassment task: construction of group-level brain template by FuSeISC (see Lower for explanation of ISC mean and variability) (i); estimation of connectivity networks for EMBAR and PRIDE (ii); computation of overall connectivity strength for each subject for multiple choices of connectivity parameters ( SI Appendix ) (iii); correlations between overall connectivity strengths and TKS scores, separately for each parameter combination (iv); and statistical evaluation of the average correlations across parameter combinations (v). ( Lower ) Concepts of ISC mean and ISC variability, used as inherent features to divide brain areas into functional segments. High mean ISC corresponds to similar fMRI time courses across multiple subject pairs (mainly positive ISCs). Meanwhile, high ISC variability corresponds to varying fMRI time courses across subject pairs, that is, similar (positive ISC), dissimilar (no ISC), and opposite (negative ISC). Some brain regions, such as early sensory areas, are typically characterized by high ISC means, whereas, for example, certain higher-order brain areas are potentially characterized by relatively high ISC variability .

    Journal: Proceedings of the National Academy of Sciences of the United States of America

    Article Title: Brain and behavioral alterations in subjects with social anxiety dominated by empathic embarrassment

    doi: 10.1073/pnas.1918081117

    Figure Lengend Snippet: Pipeline for the analysis. ( Upper ) The analysis steps of associations between TKS scores and overall brain connectivity during the empathic embarrassment task: construction of group-level brain template by FuSeISC (see Lower for explanation of ISC mean and variability) (i); estimation of connectivity networks for EMBAR and PRIDE (ii); computation of overall connectivity strength for each subject for multiple choices of connectivity parameters ( SI Appendix ) (iii); correlations between overall connectivity strengths and TKS scores, separately for each parameter combination (iv); and statistical evaluation of the average correlations across parameter combinations (v). ( Lower ) Concepts of ISC mean and ISC variability, used as inherent features to divide brain areas into functional segments. High mean ISC corresponds to similar fMRI time courses across multiple subject pairs (mainly positive ISCs). Meanwhile, high ISC variability corresponds to varying fMRI time courses across subject pairs, that is, similar (positive ISC), dissimilar (no ISC), and opposite (negative ISC). Some brain regions, such as early sensory areas, are typically characterized by high ISC means, whereas, for example, certain higher-order brain areas are potentially characterized by relatively high ISC variability .

    Article Snippet: We performed data-driven FuSeISC ( ) of brain regions using the ISC toolbox ( ) implemented in Matlab.

    Techniques: Functional Assay

    ISC-based whole-brain functional segmentation. Axial FuSeISC maps are shown for the observed segments in the affEMP ( Upper ) and cogEMP ( Lower ) contrasts ( q < 0.05, FDR-corrected), with the different segments indicated with different colors. Note that the visible (labeled) brain areas are not necessarily the only ones included in a certain segment because spatial constraints were not used in FuSeISC. MNI z coordinates (in millimeters) are indicated for each slice (see SI Appendix , Table S1 for a comprehensive list of brain regions for each segment).

    Journal: Proceedings of the National Academy of Sciences of the United States of America

    Article Title: Brain and behavioral alterations in subjects with social anxiety dominated by empathic embarrassment

    doi: 10.1073/pnas.1918081117

    Figure Lengend Snippet: ISC-based whole-brain functional segmentation. Axial FuSeISC maps are shown for the observed segments in the affEMP ( Upper ) and cogEMP ( Lower ) contrasts ( q < 0.05, FDR-corrected), with the different segments indicated with different colors. Note that the visible (labeled) brain areas are not necessarily the only ones included in a certain segment because spatial constraints were not used in FuSeISC. MNI z coordinates (in millimeters) are indicated for each slice (see SI Appendix , Table S1 for a comprehensive list of brain regions for each segment).

    Article Snippet: We performed data-driven FuSeISC ( ) of brain regions using the ISC toolbox ( ) implemented in Matlab.

    Techniques: Functional Assay, Labeling