Fig. 2. Explaining regional FC patterns with dynamic, topological, and geometric factors.
a Distributions of variance explained by different factors. Points represent brain regions and the mean variance explained across the entire cohort. Each point represents a subject (Nsub = 70 subjects following exclusion for motion and data quality; FC and SC averaged over two scan sessions). In each boxplot, the “box” denotes interquartile range (IQR), the horizontal bar indicates the median value, and the whiskers include points that are within 1.5 × IQR of upper and lower bounds of the IQR (25th and 7th percentiles). Any points that fall beyond the whiskers are, by convention, considered outliers. b Spatial embedding of predictors. The coordinates were determined using the following procedure: 1) calculating the similarity of regional correlations for every pair of predictors, 2) thresholding this matrix to retain the k = 4 nearest neighbors for each predictor, and 3) performing a principal component analysis of the thresholded and symmetrized matrix. Coordinates represent the first two principal components, PC1 and PC2. Broadly, predictors that are near/distant to one another in principal component space exhibit similar/dissimilar patterns of regional correlations. In this plot, edges exist between predictors if they are nearest neighbors. Note that here the principal component analysis was carried out without z-scoring or centering the columns of the correlation matrix. The size of points is proportional to the mean variance explained across all brain regions. Source data are provided as a Source Data file.