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multidimensional scaling matlab mdscale function  (MathWorks Inc)


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    MathWorks Inc multidimensional scaling matlab mdscale function
    Multidimensional Scaling Matlab Mdscale Function, 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/function+for+multidimensional+scaling/pmc09018846-114-9-10
    Average 90 stars, based on 1 article reviews
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    Article Title: Sense of agency for intracortical brain-machine interfaces.
    Article Snippet: To graphically represent the spatio-temporal patterns of neural activity, we performed a multidimensional scaling (MATLAB function ‘mds’) on correlation distances computed between spatio-temporal patterns of neural activity.

    Article Title: Instrumental aversion coding in the basolateral amygdala and its reversion by a benzodiazepine
    Article Snippet: Kernel coordinates in 2D space were obtained via multidimensional scaling (MATLAB mdscale function, criterion = metricstress), using fit distances as input.

    Article Title: Stimulus vignetting and orientation selectivity in human visual cortex
    Article Snippet: We then averaged across subjects and used multidimensional scaling (Matlab function ‘mdscale’) to reduce the dimensionality to 2.

    Article Title: Validating a double Gaussian source model for small proton fields in a commercial Monte-Carlo dose calculation engine
    Article Snippet: A post-processing routine was implemented in MATLAB (R2019a, MathWorks, Natick, US), scaling and merging the single measurements to a composite 2D image with signals down to 0.01 % of the maximum.

    Article Title: Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations
    Article Snippet: We solve for B ( t ) numerically by calculating the matrix exponential of A using a scaling and squaring algorithm implemented in MATLAB (MathWorks, Newton, MA) .

    Article Title: Grandmaternal caregiving is associated with a distinct multi-voxel neural representation of grandchildren in the parental motivation circuit.
    Article Snippet: The group-level compromise matrix was then submitted to multidimensional scaling and hierarchical clustering analyses in MATLAB via “midscale” and “linkage” functions in MATLAB.

    Software:

    Article Title: Computer-intensive simulation of solid-state NMR experiments using SIMPSON.
    Article Snippet: http://dx.doi.org/10.1016/j.jmr.2014.07.002 1090-7807/ 2014 Elsevier Inc. All rights reserved.. ⇑ Corresponding authors.. Address: NMR Laboratory, Department of Chemistry, Faculty of Science, Charles University in Prague, Hlavova 8, CZ-128 43, Czech Republic (Z. Tošner).



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    Analysis of background functional connectivity reveals changes over the time course of epileptogenesis. A , E , I , Individual connectivity matrices represented as dots in the first two principal dimensions of the <t>multidimensional</t> <t>scaling</t> of Frobenius distances between the individual connectivity matrices. Each dot represents a single matrix (green, Day 0; yellow, Day 7; red, Day 28; gray, Sham control; empty symbols: circle, diamond, and square represent the median of the connectivity matrices). The first three principal multidimensional scaling dimensions represent ∼70% of the relations encoded in the raw Frobenius distances ( R 2 ABS =0.66, R 2 MAX =0.72, R 2 MIN =0.7; R is Pearson’s correlation coefficient between the Frobenius distances in the matrix space and the Euclidian distances in the reconstructed space); for clarity only the first two coordinates are plotted. B – D , F – H , J – L , Median functional connectivity matrices (indicated with empty symbols in A , E , I ) resulting from the three different measures at different days with color-coded connection weights (Day 0 over 11 matrices, Day 7 over 6 matrices, Day 28 over 8 matrices; different numbers of matrices for individual days because of quality of recordings; see Materials and Methods).
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    Analysis of background functional connectivity reveals changes over the time course of epileptogenesis. A , E , I , Individual connectivity matrices represented as dots in the first two principal dimensions of the <t>multidimensional</t> <t>scaling</t> of Frobenius distances between the individual connectivity matrices. Each dot represents a single matrix (green, Day 0; yellow, Day 7; red, Day 28; gray, Sham control; empty symbols: circle, diamond, and square represent the median of the connectivity matrices). The first three principal multidimensional scaling dimensions represent ∼70% of the relations encoded in the raw Frobenius distances ( R 2 ABS =0.66, R 2 MAX =0.72, R 2 MIN =0.7; R is Pearson’s correlation coefficient between the Frobenius distances in the matrix space and the Euclidian distances in the reconstructed space); for clarity only the first two coordinates are plotted. B – D , F – H , J – L , Median functional connectivity matrices (indicated with empty symbols in A , E , I ) resulting from the three different measures at different days with color-coded connection weights (Day 0 over 11 matrices, Day 7 over 6 matrices, Day 28 over 8 matrices; different numbers of matrices for individual days because of quality of recordings; see Materials and Methods).
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    Analysis of background functional connectivity reveals changes over the time course of epileptogenesis. A , E , I , Individual connectivity matrices represented as dots in the first two principal dimensions of the <t>multidimensional</t> <t>scaling</t> of Frobenius distances between the individual connectivity matrices. Each dot represents a single matrix (green, Day 0; yellow, Day 7; red, Day 28; gray, Sham control; empty symbols: circle, diamond, and square represent the median of the connectivity matrices). The first three principal multidimensional scaling dimensions represent ∼70% of the relations encoded in the raw Frobenius distances ( R 2 ABS =0.66, R 2 MAX =0.72, R 2 MIN =0.7; R is Pearson’s correlation coefficient between the Frobenius distances in the matrix space and the Euclidian distances in the reconstructed space); for clarity only the first two coordinates are plotted. B – D , F – H , J – L , Median functional connectivity matrices (indicated with empty symbols in A , E , I ) resulting from the three different measures at different days with color-coded connection weights (Day 0 over 11 matrices, Day 7 over 6 matrices, Day 28 over 8 matrices; different numbers of matrices for individual days because of quality of recordings; see Materials and Methods).
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    Analysis of background functional connectivity reveals changes over the time course of epileptogenesis. A , E , I , Individual connectivity matrices represented as dots in the first two principal dimensions of the multidimensional scaling of Frobenius distances between the individual connectivity matrices. Each dot represents a single matrix (green, Day 0; yellow, Day 7; red, Day 28; gray, Sham control; empty symbols: circle, diamond, and square represent the median of the connectivity matrices). The first three principal multidimensional scaling dimensions represent ∼70% of the relations encoded in the raw Frobenius distances ( R 2 ABS =0.66, R 2 MAX =0.72, R 2 MIN =0.7; R is Pearson’s correlation coefficient between the Frobenius distances in the matrix space and the Euclidian distances in the reconstructed space); for clarity only the first two coordinates are plotted. B – D , F – H , J – L , Median functional connectivity matrices (indicated with empty symbols in A , E , I ) resulting from the three different measures at different days with color-coded connection weights (Day 0 over 11 matrices, Day 7 over 6 matrices, Day 28 over 8 matrices; different numbers of matrices for individual days because of quality of recordings; see Materials and Methods).

    Journal: eNeuro

    Article Title: Background EEG Connectivity Captures the Time-Course of Epileptogenesis in a Mouse Model of Epilepsy

    doi: 10.1523/ENEURO.0059-19.2019

    Figure Lengend Snippet: Analysis of background functional connectivity reveals changes over the time course of epileptogenesis. A , E , I , Individual connectivity matrices represented as dots in the first two principal dimensions of the multidimensional scaling of Frobenius distances between the individual connectivity matrices. Each dot represents a single matrix (green, Day 0; yellow, Day 7; red, Day 28; gray, Sham control; empty symbols: circle, diamond, and square represent the median of the connectivity matrices). The first three principal multidimensional scaling dimensions represent ∼70% of the relations encoded in the raw Frobenius distances ( R 2 ABS =0.66, R 2 MAX =0.72, R 2 MIN =0.7; R is Pearson’s correlation coefficient between the Frobenius distances in the matrix space and the Euclidian distances in the reconstructed space); for clarity only the first two coordinates are plotted. B – D , F – H , J – L , Median functional connectivity matrices (indicated with empty symbols in A , E , I ) resulting from the three different measures at different days with color-coded connection weights (Day 0 over 11 matrices, Day 7 over 6 matrices, Day 28 over 8 matrices; different numbers of matrices for individual days because of quality of recordings; see Materials and Methods).

    Article Snippet: Next, we used classical multidimensional scaling (MDS) to visualize relations captured by the similarity matrix , using MATLAB (Release 2018b, MathWorks) function cmdscale.

    Techniques: Functional Assay, Control