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svr lesion symptom mapping toolbox  (MathWorks Inc)


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    MathWorks Inc svr lesion symptom mapping toolbox
    Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom <t>mapping</t> <t>(SVR-LSM).</t> Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].
    Svr Lesion Symptom Mapping 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/result/svr lesion symptom mapping toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    svr lesion symptom mapping toolbox - by Bioz Stars, 2026-03
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    1) Product Images from "Double dissociation of dynamic and static face perception provides causal evidence for a third visual pathway"

    Article Title: Double dissociation of dynamic and static face perception provides causal evidence for a third visual pathway

    Journal: Nature Communications

    doi: 10.1038/s41467-025-61395-9

    Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom mapping (SVR-LSM). Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].
    Figure Legend Snippet: Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom mapping (SVR-LSM). Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].

    Techniques Used: Transformation Assay, Plasmid Preparation, Two Tailed Test



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    Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom <t>mapping</t> <t>(SVR-LSM).</t> Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].
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    Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom <t>mapping</t> <t>(SVR-LSM).</t> Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].
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    Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom <t>mapping</t> <t>(SVR-LSM).</t> Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].
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    Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom mapping (SVR-LSM). Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].

    Journal: Nature Communications

    Article Title: Double dissociation of dynamic and static face perception provides causal evidence for a third visual pathway

    doi: 10.1038/s41467-025-61395-9

    Figure Lengend Snippet: Performance on static and dynamic emotion recognition tasks and brain lesion mapping in patients with focal brain lesions ( A ) 31 Patients with focal brain lesions involving the right posterior superior temporal sulcus (pSTS) but sparing the right fusiform face area (FFA) and occipital face area (OFA), showed normal performance in the static emotion recognition task but poor performance for dynamic emotion recognition. B 12 patients with focal brain lesions involving the right FFA or OFA, but sparing the right pSTS, showed normal performance in dynamic but poor performance in static emotion recognition. In ( A , B ), the rendered MNI152 template brain shows the extent of the associated lesions in green. The boxplots show the z-transformed performance accuracy of the static and dynamic emotion recognition tasks. The boxes denote the interquartile range, the whiskers extend to 1.5 × IQR from each quartile and the horizontal lines denote the median. The source data and code used for generating the plots are available at https://osf.io/ez9dp/ . C Rendered MNI152 template brain and orthogonal brain slices show results of the multivariate support vector regression lesion symptom mapping (SVR-LSM). Clusters show significant associations with behavioral performance (FWE-corrected p < 0.001) for dynamic (blue) and static (red) emotion recognition, identified using a two-tailed, permutation-based maximum statistic approach. A disconnectome analysis for the white matter revealed tracts associated with dynamic (in purple) and static (in yellow) emotion recognition. [AF Arcuate fasciculus, MDLF Middle longitudinal fasciculus, ILF Inferior longitudinal fasciculus, IFOF Inferior fronto-occipital fasciculus].

    Article Snippet: Initial SVR-LSM was conducted using the SVR lesion symptom mapping toolbox ( https://github.com/atdemarco/svrlsmgui ) with functionalities of the Statistics and Machine Learning Toolbox within MATLAB (MATLAB 2022a, The MathWorks, Inc., Natick, Massachusetts, United States) , .

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