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spm 12 matlab toolbox  (MathWorks Inc)


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    MathWorks Inc spm 12 matlab toolbox
    Spm 12 Matlab Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 454 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/spm+matlab+toolbox/Instrument+Control+Toolbox/pmc12602468-59-10-11
    Average 96 stars, based on 454 article reviews
    spm 12 matlab toolbox - by Bioz Stars, 2026-10
    96/100 stars

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    Related Articles

    other:

    Article Title: Source localization of epileptic spikes using Multiple Sparse Priors
    Article Snippet: A step-by-step function evaluation was accomplished to ensure the code performed equal to implementation of the MSP method in the SPM Matlab toolbox ( ).

    Article Title: Topological dissimilarities of hierarchical resting networks in type 2 diabetes mellitus and obesity.
    Article Snippet: The green boxes specify the effective connectivity analysis using methods from the DCM framework implemented in the SPM Matlab toolbox.

    Article Title: Methods and tools for analyzing brain images
    Article Snippet: In the right panel, the predicted patterns are compared with measured atrophy of dementia patients (AD, bvFTD and age-matched normal subjects), obtained via a completely separate processing pipeline available in the SPM Matlab toolbox.

    Article Title: Anatomically Veridical On-Scalp Sensor Topographies.
    Article Snippet: Data Availability Statement The anatomical projection method has been added to the SPM MATLAB toolbox as a single function spm_get_anatomical_layout, available in the SPM 25.01 release (Tierney et al. 2025).

    Significance Assay:


    Comparison:

    Article Title: Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models.
    Article Snippet: .. Eigenmodes and comparison with measured/published findings Eigenmodes were derived from the healthy network, and a com- parison of predicted patterns with atrophy of AD patients was obtained by different pipelines using SPM MATLAB toolbox. ..

    Derivative Assay:

    Article Title: Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models.
    Article Snippet: .. Eigenmodes and comparison with measured/published findings Eigenmodes were derived from the healthy network, and a com- parison of predicted patterns with atrophy of AD patients was obtained by different pipelines using SPM MATLAB toolbox. ..



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    Fig. 1. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different shoes (H: 50 mm, M: 35 mm and L: 27 mm) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of two running speed conditions are represented for each shoe condition. Lower row shows the corresponding <t>SPM</t> <t>rmANOVA</t> results for the main factor shoe. F* values indicate the corresponding thresholds for α = 0.05.
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    Fig. 1. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different shoes (H: 50 mm, M: 35 mm and L: 27 mm) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of two running speed conditions are represented for each shoe condition. Lower row shows the corresponding <t>SPM</t> <t>rmANOVA</t> results for the main factor shoe. F* values indicate the corresponding thresholds for α = 0.05.
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    Image Search Results


    A: Group-averaged t values of the contrast right-hand ME > left-hand ME ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). B: Group-averaged t values of the contrast left-hand ME > right-hand ME ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). C: Group-averaged t values of the contrast right-hand MI > left-hand MI ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). D: Group-averaged t values of the contrast left-hand MI > right-hand MI ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). Results are displayed using the BrainNet toolbox in MATLAB.

    Journal: NeuroImage

    Article Title: Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis

    doi: 10.1016/j.neuroimage.2025.121311

    Figure Lengend Snippet: A: Group-averaged t values of the contrast right-hand ME > left-hand ME ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). B: Group-averaged t values of the contrast left-hand ME > right-hand ME ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). C: Group-averaged t values of the contrast right-hand MI > left-hand MI ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). D: Group-averaged t values of the contrast left-hand MI > right-hand MI ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 17). Results are displayed using the BrainNet toolbox in MATLAB.

    Article Snippet: fMRI analyses were performed using the Statistical Parametric Mapping (SPM) MATLAB toolbox ( http://www.fil.ion.ucl.ac.uk/spm/ , version 12) and custom MATLAB scripts.

    Techniques:

    A: Group-averaged t values showing BOLD correlates of alpha rhythms in channel C3 during ME tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). B: group-averaged t values showing neural correlates of alpha rhythms in channel C4 during ME tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). C: Group-averaged t values showing neural correlates of alpha rhythms in channel C3 during MI tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). Results are displayed using the BrainNet toolbox in MATLAB.

    Journal: NeuroImage

    Article Title: Investigating the neurovascular coupling across multiple motor execution and imagery conditions: a whole-brain EEG-informed fMRI analysis

    doi: 10.1016/j.neuroimage.2025.121311

    Figure Lengend Snippet: A: Group-averaged t values showing BOLD correlates of alpha rhythms in channel C3 during ME tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). B: group-averaged t values showing neural correlates of alpha rhythms in channel C4 during ME tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). C: Group-averaged t values showing neural correlates of alpha rhythms in channel C3 during MI tasks ( p < 0.001, uncorrected with k higher than the expected number of voxels) ( n = 10). Results are displayed using the BrainNet toolbox in MATLAB.

    Article Snippet: fMRI analyses were performed using the Statistical Parametric Mapping (SPM) MATLAB toolbox ( http://www.fil.ion.ucl.ac.uk/spm/ , version 12) and custom MATLAB scripts.

    Techniques:

    Fig. 1. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different shoes (H: 50 mm, M: 35 mm and L: 27 mm) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of two running speed conditions are represented for each shoe condition. Lower row shows the corresponding SPM rmANOVA results for the main factor shoe. F* values indicate the corresponding thresholds for α = 0.05.

    Journal: Journal of biomechanics

    Article Title: The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

    doi: 10.1016/j.jbiomech.2025.112615

    Figure Lengend Snippet: Fig. 1. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different shoes (H: 50 mm, M: 35 mm and L: 27 mm) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of two running speed conditions are represented for each shoe condition. Lower row shows the corresponding SPM rmANOVA results for the main factor shoe. F* values indicate the corresponding thresholds for α = 0.05.

    Article Snippet: The time series of the three UCM components (UCM‖, UCM�, UCMratio) were analyzed with statistical parametric mapping (SPM) repeated measures analysis of variance (rmANOVA) by using the SPM toolbox in MATLAB (spm1d toolbox; (Pataky et al., 2019).

    Techniques:

    Fig. 2. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different running speeds (10 km/h and 15 km/h) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of three shoe conditions are represented for each running speed. The significant speed effects in SPM rmANOVA are shown as the gray areas. Lower row shows the corresponding SPM rmANOVA results for the main factor speed with clustered p- values. F* values indicate the corresponding thresholds for α = 0.05.

    Journal: Journal of biomechanics

    Article Title: The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

    doi: 10.1016/j.jbiomech.2025.112615

    Figure Lengend Snippet: Fig. 2. Upper row shows the results for the three UCM components (UCM‖, UCM and UCMratio) for different running speeds (10 km/h and 15 km/h) as mean (thicker lines) ± standard deviations (upper and lower thinner lines). The average of three shoe conditions are represented for each running speed. The significant speed effects in SPM rmANOVA are shown as the gray areas. Lower row shows the corresponding SPM rmANOVA results for the main factor speed with clustered p- values. F* values indicate the corresponding thresholds for α = 0.05.

    Article Snippet: The time series of the three UCM components (UCM‖, UCM�, UCMratio) were analyzed with statistical parametric mapping (SPM) repeated measures analysis of variance (rmANOVA) by using the SPM toolbox in MATLAB (spm1d toolbox; (Pataky et al., 2019).

    Techniques:

    Fig. 3. SPM rmANOVA results for the interaction effects between shoe and speed conditions. F* values indicate the corresponding thresholds for α = 0.05.

    Journal: Journal of biomechanics

    Article Title: The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

    doi: 10.1016/j.jbiomech.2025.112615

    Figure Lengend Snippet: Fig. 3. SPM rmANOVA results for the interaction effects between shoe and speed conditions. F* values indicate the corresponding thresholds for α = 0.05.

    Article Snippet: The time series of the three UCM components (UCM‖, UCM�, UCMratio) were analyzed with statistical parametric mapping (SPM) repeated measures analysis of variance (rmANOVA) by using the SPM toolbox in MATLAB (spm1d toolbox; (Pataky et al., 2019).

    Techniques: