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conn functional connectivity toolbox v 18  (MathWorks Inc)


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    Structured Review

    MathWorks Inc conn functional connectivity toolbox v 18
    Conn Functional Connectivity Toolbox V 18, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 671 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/fmri+data+preprocessing/Mapping+Toolbox/pmc09694608-82-9-32
    Average 96 stars, based on 671 article reviews
    conn functional connectivity toolbox v 18 - by Bioz Stars, 2026-09
    96/100 stars

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    Related Articles

    other:

    Article Title: Sixty years of observations and future projections of nine declining North American glaciers.
    Article Snippet: Basemap imagery (Fig. 1) is from the MATLAB Mapping Toolbox MTRI is also acknowledged for any related publication costs.

    Article Title: Testosterone modulates multispectral oscillatory activity serving performance of motor sequences in typically developing youth
    Article Snippet: Separate whole-brain ANCOVAs with sex as a between-subjects factor, testosterone level as a covariate of interest, and age as a covariate of no interest were then performed per oscillatory response using the open-source Statistical Parametric Mapping (SPM 12) toolbox (https://www.fil.ion.ucl.ac.uk/spm/software/) in MATLAB (MathWorks,Natick,MA,USA).A full factorial design was used specifying testosterone interactions with sex.

    Article Title: Testosterone modulates multispectral oscillatory activity serving performance of motor sequences in typically developing youth
    Article Snippet: Separate whole‐brain ANCOVAs with sex as a between‐subjects factor, testosterone level as a covariate of interest, and age as a covariate of no interest were then performed per oscillatory response using the open‐source Statistical Parametric Mapping (SPM 12) toolbox ( https://www.fil.ion.ucl.ac.uk/spm/software/ ) in MATLAB (MathWorks, Natick, MA, USA).

    Article Title: Moral inconsistency is based on the vmPFC's insufficient representation across tasks and connectedness.
    Article Snippet: All images were preprocessed using MATLAB toolbox Statistical Parametric Mapping (SPM12; www.fil.ion.ucl.ac.uk/spm) and custom code.

    Article Title: Effects of 5-Minute Light-Intensity Physical Activity Breaks (Sit-Cycling and Stand-Twisting) on Cognitive Function, Sleepiness, and Back Pain in Physically Inactive University Students: Protocol for a Mixed-Design Laboratory Study.
    Article Snippet: A MATLAB-based toolbox was used to accurately estimate the spatial brain mapping of the electroencephalography (EEG) 10-20 system onto associated Brodmann areas, identifying the left and the right DLPFC as the regions of interest.

    Construct:

    Article Title: Source of Lateralization in Reverse Total Shoulder Arthroplasty Matters: A Comparison of Glenoid and Humeral Lateralization on Rotator Cuff Biomechanics.
    Article Snippet: Journal Pre-proof Source of Lateralization in Reverse Total Shoulder Arthroplasty Matters: A Comparison of Glenoid and Humeral Lateralization on Rotator Cuff Biomechanics Christopher M. Brusalis, MD, Jonathan Glenday, PhD, Michael C. Fu, MD, MHS, Joshua S. Dines, MD, Theodore A. Blaine, MD, David M. Dines, MD, Lawrence V. Gulotta, MD, Samuel A. Taylor, MD, Andreas Kontaxis, PhD PII: S1058-2746(26)00135-7 DOI: https://doi.org/10.1016/j.jse.2026.02.021 Reference: YMSE 7694 To appear in: Journal of Shoulder and Elbow Surgery Received Date: 13 November 2025 Revised Date: 21 February 2026 Accepted Date: 26 February 2026 Please cite this article as: Brusalis CM, Glenday J, Fu MC, Dines JS, Blaine TA, Dines DM, Gulotta LV, Taylor SA, Kontaxis A, Source of Lateralization in Reverse Total Shoulder Arthroplasty Matters: A Comparison of Glenoid and Humeral Lateralization on Rotator Cuff Biomechanics, Journal of Shoulder and Elbow Surgery (2026), doi: https://doi.org/10.1016/j.jse.2026.02.021.. This is a PDF of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability.. This version will undergo additional copyediting, typesetting and review before it is published in its final form.

    Imaging:

    Article Title: Neuronal populations across the cortex underlie discrete, categorical, and subjective representations of visual durations
    Article Snippet: We performed the entire procedure using custom functions in MATLAB. shows the fitting result for a representative vertex. .. We conducted our analyses within a set of ROIs identified using a mass-univariate GLM approach performed with the Statistical Parametric Mapping toolbox in MATLAB (SPM12, version 7219, Wellcome Department of Imaging Neuroscience, University College London). ..

    Clinical Proteomics:

    Article Title: Cognitive and brain reserve in bilingual speakers with clinical AD variants
    Article Snippet: .. MR images were bias-corrected, segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)[ ] and normalized to Montreal Neurological Institute (MNI) using an optimized geodesic shooting procedure[ ] in CAT12 toolbox via Statistical Parametric Mapping (SPM[ ] running in MATLAB version 2022b ( The MathWorks Inc ., 2022 ). ..



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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the <t>fMRI</t> <t>data</t> from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.
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    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the <t>fMRI</t> <t>data</t> from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.
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    Image Search Results


    Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the fMRI data from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.

    Journal: Cerebral cortex (New York, N.Y. : 1991)

    Article Title: Multiscale functional connectome abnormality predicts cognitive outcomes in subcortical ischemic vascular disease.

    doi: 10.1093/cercor/bhab507

    Figure Lengend Snippet: Figure 1. The schematic illustration of the main method. (A) The procedures to obtain multiscale FCNs. (i) the fMRI data from one individual is inputted and preprocessed. (ii) The application of the Schaefer’s multiscale atlases to the fMRI data. The black border lines indicate boundaries of ROIs and the colors encode the resting-state network (RSN) to which the ROI belongs. The RSNs include DMN, frontoparietal network (FP), limbic network (LIM), salience network (SAL), attention network (ATT), somatomotor network (SM), and visual network (VIS). (iii) The extraction of ROI-averaged signals. (iv) The construction of multiscale FCNs from individual fMRI data. (B) The architecture for multiscale atlas-based GCN (MAGCN). The multiscale FCNs are extracted via a series of GCNs connected by the APs. The nodal features h are integrated with skip connections and concatenations, based on which individualized diagnosis is generated.

    Article Snippet: The sagittal T1-weighted images covering the whole brain were acquired by a 3Dfast spoiled gradient recalled echo sequence: TR = 5.6 ms, TE = 1.8 ms, matrix = 256 × 256, inversion time = 450 ms, flip angle = 15◦, slice thickness/gap = 1/0 mm, number of slices = 156, gap = 0, and FOV = 256 × 256 mm2. fMRI Data Preprocessing We adopt the standardized pipeline from the public available toolbox Data Processing Assistant for Resting-State fMRI (Yan and Zang 2010) in Matlab (Mathworks.

    Techniques: Extraction, Biomarker Discovery, Generated