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data preprocessing assistant for resting-state fmri (dparsf) software in matlab 2017b  (MathWorks Inc)


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    MathWorks Inc data preprocessing assistant for resting-state fmri (dparsf) software in matlab 2017b
    Data Preprocessing Assistant For Resting State Fmri (Dparsf) Software In Matlab 2017b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/fmri+data+preprocessing/pm40047998-83-27-27
    Average 90 stars, based on 1 article reviews
    data preprocessing assistant for resting-state fmri (dparsf) software in matlab 2017b - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice.
    Article Snippet: Statistical analyses and data presentation Statistical analysis was performed with Matlab 2017b and Matlab 2022a (Mathworks).

    Article Title: Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice
    Article Snippet: Statistical analysis was performed with Matlab 2017b and Matlab 2022a (Mathworks).

    Article Title: Decoding basic emotional states through integration of an fNIRS-based brain-computer interface with supervised learning algorithms.
    Article Snippet: The extracted features were 1) maximum, 2) range (i.e., amplitude difference between maximum and minimum signal values), 3) signal to noise ratio, 4) peak time, 5) skewness, 6) kurtosis, 7) variance, 8) median, 9) root mean square, 10) area under the curve and step response characteristics obtained with ‘stepinfo’ function implemented in MATLAB 2017B (The Mathworks Inc., Natick, MA, USA) which included 11) rise time, 12) settling time, 13) settling min, 14) settling max and 15–20) coefficients of a fifth-degree polynomial which was fitted to each channel and stimulus specific truncated time series HbO data.

    Article Title: Interoception vs. Exteroception: Cardiac interoception competes with tactile perception, yet also facilitates self-relevance encoding
    Article Snippet: The task was presented with Matlab 2017b ( ) using the Psychtoolbox , running on a MacBook Air stimulation laptop.

    Article Title: Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice.
    Article Snippet: All analysis of calcium data was performed on deconvolved traces (variable C_dec) extracted through CNMF and performed using Matlab 2017b (Mathworks, code available on Github).

    Article Title: Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice
    Article Snippet: All analysis of calcium data was performed on deconvolved traces (variable C_dec ) extracted through CNMF and performed using Matlab 2017b (Mathworks, code available on Github).

    Article Title: A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.
    Article Snippet: Recently, SSVEP-based brain–computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training.. Therefore, various methods have been proposed to recognize the frequency of SSVEPs.. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs.

    Article Title: Distinct neural bases of subcomponents of the attentional blink
    Article Snippet: Stimulus presentation and data acquisition were programmed with Psychtoolbox ( ) and MATLAB 2017b MathWorks Inc (2017), (Natick, MA.).



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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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    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