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matlab function lsim  (MathWorks Inc)


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    Structured Review

    MathWorks Inc matlab function lsim
    Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed <t>DCM</t> (used as reference, at the subject level); ( b ) the capability of generating data similar to <t>the</t> <t>empirical</t> recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.
    Matlab Function Lsim, 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/result/matlab function lsim/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab function lsim - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Macroscale coupling between structural and effective connectivity in the mouse brain"

    Article Title: Macroscale coupling between structural and effective connectivity in the mouse brain

    Journal: Scientific Reports

    doi: 10.1038/s41598-024-51613-7

    Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed DCM (used as reference, at the subject level); ( b ) the capability of generating data similar to the empirical recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.
    Figure Legend Snippet: Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed DCM (used as reference, at the subject level); ( b ) the capability of generating data similar to the empirical recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.

    Techniques Used: MANN-WHITNEY, Comparison



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    Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed <t>DCM</t> (used as reference, at the subject level); ( b ) the capability of generating data similar to <t>the</t> <t>empirical</t> recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.
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    Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed <t>DCM</t> (used as reference, at the subject level); ( b ) the capability of generating data similar to <t>the</t> <t>empirical</t> recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.
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    Image Search Results


    Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed DCM (used as reference, at the subject level); ( b ) the capability of generating data similar to the empirical recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.

    Journal: Scientific Reports

    Article Title: Macroscale coupling between structural and effective connectivity in the mouse brain

    doi: 10.1038/s41598-024-51613-7

    Figure Lengend Snippet: Model fit quantified as: ( a ) the log-likelihood difference between the structurally informed DCMs and the non-structurally informed DCM (used as reference, at the subject level); ( b ) the capability of generating data similar to the empirical recordings used to fit the DCM model, in terms of correlation between their FCs (left y-axis) and KS distance of their dynamic FCs (right y-axis), for non-structurally informed DCM and structurally informed DCM with different SC thresholds, i.e. 60, 40 and 20 (percentage of kept links), averaged over 100 realizations. *p < 0.05, **p < 0.01 and ***p < 0.001, Mann–Whitney U test with Benjamini/Hochberg multiple comparison correction.

    Article Snippet: After fitting a DCM model to each empirical single-subject rsfMRI recordings, we generated 100 realizations of synthetic rsfMRI signals (by using the MATLAB function lsim ).

    Techniques: MANN-WHITNEY, Comparison