multidimensional scaling matlab function mdscale  (MathWorks Inc)

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