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data visualization intermediate toolbox users  (MathWorks Inc)


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    MathWorks Inc data visualization intermediate toolbox users
    Figure 4. Demonstrations of voxel-wise <t>visualization.</t> This figure shows SPM graphics windows resulting from voxel-wise visualization tools included in the MACS <t>toolbox.</t> (A) Using the module “inspect goodness of fit”, measured and predicted fMRI signal are shown alongside goodness-of-fit measures (see Section 2.1.1) which can be browsed for every in-mask voxel included in a particular first-level analysis (see Ashburner et al., 2016, sec. 31.2). The lower panel highlights voxels with the top 5% (yellow, good fit) and the bottom 5% (red, bad fit) in terms of coefficient of determination (R2) for this GLM. (B) Using the module “visualize high-dimensional <t>data”,</t> several data points in each brain region can be displayed and browsed in a voxel-wise fashion. The lower panel highlights voxels in which the R2 of a GLM (the same as in A) is larger than 0.¯3. The upper panel bar plots beta estimates from the four experimental condition regressors of this model, indicating a negative effect of both two-level factors in this voxel (see Ashburner et al., 2016, fig. 31.1). Note that this feature may also be used to display voxel-wise posterior probabilities or model frequencies in large model spaces for comparing model preferences in different regions of interest.
    Data Visualization Intermediate Toolbox Users, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2340 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/data+visualization+intermediate+toolbox+users/Database+Toolbox/pm29842901-148-34-53
    Average 96 stars, based on 2340 article reviews
    data visualization intermediate toolbox users - by Bioz Stars, 2026-09
    96/100 stars

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    1) Product Images from "MACS - a new SPM toolbox for model assessment, comparison and selection."

    Article Title: MACS - a new SPM toolbox for model assessment, comparison and selection.

    Journal: Journal of neuroscience methods

    doi: 10.1016/j.jneumeth.2018.05.017

    Figure 4. Demonstrations of voxel-wise visualization. This figure shows SPM graphics windows resulting from voxel-wise visualization tools included in the MACS toolbox. (A) Using the module “inspect goodness of fit”, measured and predicted fMRI signal are shown alongside goodness-of-fit measures (see Section 2.1.1) which can be browsed for every in-mask voxel included in a particular first-level analysis (see Ashburner et al., 2016, sec. 31.2). The lower panel highlights voxels with the top 5% (yellow, good fit) and the bottom 5% (red, bad fit) in terms of coefficient of determination (R2) for this GLM. (B) Using the module “visualize high-dimensional data”, several data points in each brain region can be displayed and browsed in a voxel-wise fashion. The lower panel highlights voxels in which the R2 of a GLM (the same as in A) is larger than 0.¯3. The upper panel bar plots beta estimates from the four experimental condition regressors of this model, indicating a negative effect of both two-level factors in this voxel (see Ashburner et al., 2016, fig. 31.1). Note that this feature may also be used to display voxel-wise posterior probabilities or model frequencies in large model spaces for comparing model preferences in different regions of interest.
    Figure Legend Snippet: Figure 4. Demonstrations of voxel-wise visualization. This figure shows SPM graphics windows resulting from voxel-wise visualization tools included in the MACS toolbox. (A) Using the module “inspect goodness of fit”, measured and predicted fMRI signal are shown alongside goodness-of-fit measures (see Section 2.1.1) which can be browsed for every in-mask voxel included in a particular first-level analysis (see Ashburner et al., 2016, sec. 31.2). The lower panel highlights voxels with the top 5% (yellow, good fit) and the bottom 5% (red, bad fit) in terms of coefficient of determination (R2) for this GLM. (B) Using the module “visualize high-dimensional data”, several data points in each brain region can be displayed and browsed in a voxel-wise fashion. The lower panel highlights voxels in which the R2 of a GLM (the same as in A) is larger than 0.¯3. The upper panel bar plots beta estimates from the four experimental condition regressors of this model, indicating a negative effect of both two-level factors in this voxel (see Ashburner et al., 2016, fig. 31.1). Note that this feature may also be used to display voxel-wise posterior probabilities or model frequencies in large model spaces for comparing model preferences in different regions of interest.

    Techniques Used:

    Related Articles

    other:

    Article Title: During natural vision, semantic novelty modulates fixation-related processing in primate cortex
    Article Snippet: In the human intracranial dataset, the electrode arrays each contained multiple contacts, which were identified using the iELVis MATLAB toolbox ( ).

    Article Title: From statistics to deep learning in single-molecule fluorescence resonance energy transfer analysis.
    Article Snippet: MASH-FRET [9,26], a MATLAB-based toolbox, uses the BOBA-FRET’s analysis and simulation framework, while adding workflows for molecule localisation and dynamic characterisation. iSMS [27] and SMART [28] offer graphical interfaces for data analysis and visualisation, and enhance standard HMM by incorporating VB and the Bayesian Information Criterion (BIC) while estimating the number of states.

    Software:

    Article Title: Alpha‐adrenergic mediated changes in blood pressure variability after hypoxia‐ischaemia in preterm fetal sheep
    Article Snippet: .. For beat-to-beat BPV analysis, systolic arterial blood pressure peak amplitudes were extracted using LabVIEW software and imported into a customised physiological data analysis toolbox using MATLAB (MATLAB 2023A; The MathWorks, Inc., Natick, MA, USA) to calculate features of beat-to-beat variability. ..

    Clinical Proteomics:

    Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
    Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4)58 Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

    Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
    Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4) Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

    Gentle:

    Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
    Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4)58 Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

    Article Title: Parvalbumin-positive neurons in the medial septum participate in the formation of hippocampal-dependent spatial memory.
    Article Snippet: .. The EDF files were exported to MATLAB and then analysis sleep state using an open source MATLAB toolbox (https://data.mendeley.com/datasets/c872f83pdz/4) Plasma corticosterone measures Plasma samples were collected approximately 12-15 hours after SD, including from the gentle handling SD group. ..

    Generated:

    Article Title: Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.
    Article Snippet: .. Topoplots were generated using the MATLAB toolbox from Víctor Martínez-Cagigal (2025): Topographic EEG/MEG plot (https://www.mathworks.com/matlabcentral/fileexchange/72729-topographic-eeg-meg-plot). https://doi.org/10.1371/journal.pcbi.1014112.g003 PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1014112 April 1, 2026 8 / 22 showing a positive association with posterior alpha frequency acceleration with effects ranging from negligible to moderate depending on dataset. ..

    Functional Assay:

    Article Title: Fronto-Cerebellar Connectivity Disruptions and Functional Reorganization in Friedreich's Ataxia: A Structural and Resting-State fMRI Study.
    Article Snippet: .. For functional data, grouplevel analyses were performed using the CONN toolbox in MATLAB (v.R2022a) (Nieto-Castanon and Whitfield-Gabrieli, 2022). ..



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    data visualization intermediate toolbox users  (MathWorks Inc)
    96
    MathWorks Inc data visualization intermediate toolbox users
    Figure 4. Demonstrations of voxel-wise <t>visualization.</t> This figure shows SPM graphics windows resulting from voxel-wise visualization tools included in the MACS <t>toolbox.</t> (A) Using the module “inspect goodness of fit”, measured and predicted fMRI signal are shown alongside goodness-of-fit measures (see Section 2.1.1) which can be browsed for every in-mask voxel included in a particular first-level analysis (see Ashburner et al., 2016, sec. 31.2). The lower panel highlights voxels with the top 5% (yellow, good fit) and the bottom 5% (red, bad fit) in terms of coefficient of determination (R2) for this GLM. (B) Using the module “visualize high-dimensional <t>data”,</t> several data points in each brain region can be displayed and browsed in a voxel-wise fashion. The lower panel highlights voxels in which the R2 of a GLM (the same as in A) is larger than 0.¯3. The upper panel bar plots beta estimates from the four experimental condition regressors of this model, indicating a negative effect of both two-level factors in this voxel (see Ashburner et al., 2016, fig. 31.1). Note that this feature may also be used to display voxel-wise posterior probabilities or model frequencies in large model spaces for comparing model preferences in different regions of interest.
    Data Visualization Intermediate Toolbox Users, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/data+visualization+intermediate+toolbox+users/Database+Toolbox/pm29842901-148-34-53
    Average 96 stars, based on 1 article reviews
    data visualization intermediate toolbox users - by Bioz Stars, 2026-09
    96/100 stars
    		  Buy from Supplier

    Image Search Results


    Figure 4. Demonstrations of voxel-wise visualization. This figure shows SPM graphics windows resulting from voxel-wise visualization tools included in the MACS toolbox. (A) Using the module “inspect goodness of fit”, measured and predicted fMRI signal are shown alongside goodness-of-fit measures (see Section 2.1.1) which can be browsed for every in-mask voxel included in a particular first-level analysis (see Ashburner et al., 2016, sec. 31.2). The lower panel highlights voxels with the top 5% (yellow, good fit) and the bottom 5% (red, bad fit) in terms of coefficient of determination (R2) for this GLM. (B) Using the module “visualize high-dimensional data”, several data points in each brain region can be displayed and browsed in a voxel-wise fashion. The lower panel highlights voxels in which the R2 of a GLM (the same as in A) is larger than 0.¯3. The upper panel bar plots beta estimates from the four experimental condition regressors of this model, indicating a negative effect of both two-level factors in this voxel (see Ashburner et al., 2016, fig. 31.1). Note that this feature may also be used to display voxel-wise posterior probabilities or model frequencies in large model spaces for comparing model preferences in different regions of interest.

    Journal: Journal of neuroscience methods

    Article Title: MACS - a new SPM toolbox for model assessment, comparison and selection.

    doi: 10.1016/j.jneumeth.2018.05.017

    Figure Lengend Snippet: Figure 4. Demonstrations of voxel-wise visualization. This figure shows SPM graphics windows resulting from voxel-wise visualization tools included in the MACS toolbox. (A) Using the module “inspect goodness of fit”, measured and predicted fMRI signal are shown alongside goodness-of-fit measures (see Section 2.1.1) which can be browsed for every in-mask voxel included in a particular first-level analysis (see Ashburner et al., 2016, sec. 31.2). The lower panel highlights voxels with the top 5% (yellow, good fit) and the bottom 5% (red, bad fit) in terms of coefficient of determination (R2) for this GLM. (B) Using the module “visualize high-dimensional data”, several data points in each brain region can be displayed and browsed in a voxel-wise fashion. The lower panel highlights voxels in which the R2 of a GLM (the same as in A) is larger than 0.¯3. The upper panel bar plots beta estimates from the four experimental condition regressors of this model, indicating a negative effect of both two-level factors in this voxel (see Ashburner et al., 2016, fig. 31.1). Note that this feature may also be used to display voxel-wise posterior probabilities or model frequencies in large model spaces for comparing model preferences in different regions of interest.

    Article Snippet: Examples are given in Figures 3 and 4: - Figure 3 : batch for cross-validated Bayesian model selection - Figure 4A: output from goodness-of-fit inspection for a GLM - Figure 4B: output from high-dimensional data visualization Intermediate toolbox users may also use the batch editor, but will additionally call the interface functions via MATLAB’s command-line interface.

    Techniques: