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diffusion mri toolbox  (MathWorks Inc)


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    MathWorks Inc diffusion mri toolbox
    Diffusion Mri Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2341 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/diffusion+analysis+toolbox+(dmat/Database+Toolbox/pm31686219-91-10-16
    Average 96 stars, based on 2341 article reviews
    diffusion mri toolbox - by Bioz Stars, 2026-10
    96/100 stars

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    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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    Image Search Results


    DMAT model fits. We fit the diffusion model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .

    Journal: Scientific Reports

    Article Title: Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

    doi: 10.1038/s41598-020-79765-2

    Figure Lengend Snippet: DMAT model fits. We fit the diffusion model to the data using the software package DMAT by allowing only the boundary parameter to vary with SAT levels and the drift parameter to vary with contrast. The fits were very similar to the ones with HDDM (Fig. ). We observed good fits for the d′-RT curves except for the “extremely fast” condition (upper left panel), monotonically increasing functions for the RT difference between error and correct trials (upper right panel), monotonically increasing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{SD\left( {RT} \right)}}{{mean\left( {RT} \right)}}$$\end{document} S D RT m e a n RT curves (lower left panel), and inverted-U RT skewness curves (lower right panel). Again, none of the empirically observed U-shaped curves were reproduced even qualitatively. All notation is identical to Fig. .

    Article Snippet: We fit the diffusion model to the data using both the hierarchical drift diffusion model (HDDM) python package and the diffusion model analysis toolbox (DMAT) in MATLAB .

    Techniques: Diffusion-based Assay, Software