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statistical nonparametric mapping snpm13 toolbox  (MathWorks Inc)


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    MathWorks Inc statistical nonparametric mapping snpm13 toolbox
    The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using <t>SnPM13,</t> with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.
    Statistical Nonparametric Mapping Snpm13 Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 649 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/statistical+nonparametric+mapping+snpm13+toolbox/med_rxiv__2025__10__24__25338751-110-17-26?v=MathWorks+Inc
    Average 96 stars, based on 649 article reviews
    statistical nonparametric mapping snpm13 toolbox - by Bioz Stars, 2026-06
    96/100 stars

    Images

    1) Product Images from "Speaking in Tones: The role of lexical tones in Chinese-speaking Primary Progressive Aphasia"

    Article Title: Speaking in Tones: The role of lexical tones in Chinese-speaking Primary Progressive Aphasia

    Journal: medRxiv

    doi: 10.1101/2025.10.24.25338751

    The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using SnPM13, with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.
    Figure Legend Snippet: The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using SnPM13, with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.

    Techniques Used: Labeling



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    The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using <t>SnPM13,</t> with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.
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    The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using <t>SnPM13,</t> with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.
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    Image Search Results


    The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using SnPM13, with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.

    Journal: medRxiv

    Article Title: Speaking in Tones: The role of lexical tones in Chinese-speaking Primary Progressive Aphasia

    doi: 10.1101/2025.10.24.25338751

    Figure Lengend Snippet: The figure shows the voxel-based morphometry analyses of (A) Sensical homophonous tonal phrases; (B) Nonsensical homophonous tonal phrases; (C) Isotonous syllabically varied phrases; with (D) indicating overlapping regions across the three production tasks. For (A)∼(C), statistical parametric maps show brain regions where greater gray matter volume was significantly associated with higher task performance. Analyses were conducted using SnPM13, with age, sex, total gray matter volume, education, and testing language as covariates. Nonparametric inference (5,000 permutations) used a cluster-forming threshold of t = 3.09 (uncorrected p < 0.001), with family-wise error correction at the cluster level (α = 0.05) and an extent threshold of k > 100. Slices are labeled with MNI coordinates (L = left, R = right); the color bar represents t-values. For (D), red, green, and blue indicate task-specific associations, and yellow highlights overlapping regions.

    Article Snippet: To examine the neural correlates of lexical tone performance, we conducted voxel-based morphometry (VBM) analysis using the Statistical NonParametric Mapping (SnPM13) toolbox implemented in SPM12 and MATLAB , .

    Techniques: Labeling