Fig. 1: Molecular and connectomic cortical profiles.
From: Local molecular and global connectomic contributions to cross-disorder cortical abnormalities
a, b Brain surfaces show the z-scored molecular (a) and connectomic (b) predictors used in the multilinear regression models. Heatmaps on the right show Pearson's correlation coefficients between pairs of features. See Methods for details on how each feature was derived. Molecular predictors: gene PC1 = first component of 11 560 genes' expression; receptor PC1 = first component of 18 PET-derived receptor/transporter density; E:I ratio = excitatory:inhibitory receptor density ratio; glycolytic index = amount of aerobic glycolysis; glucose metabolism \(={\left[\right.}^{18}\left.{{{{{{{\rm{F}}}}}}}}\right]\)-labelled fluorodeoxyglucose (FDG) PET image; synapse density = synaptic vesicle glycoprotein 2A (SV2A)-binding \({\left[\right.}^{11}\left.{{{{{{{\rm{C}}}}}}}}\right]\)UCB-J PET tracer; myelination = T1w/T2w ratio. Connectivity predictors: strength = sum of weighted connections; betweenness = fraction of all shortest paths traversing region i; closeness = mean shortest path length between region i and all other regions; Euclidean distance = mean Euclidean distance between region i and all other regions; participation coefficient = diversity of connections from region i to the seven Yeo-Krienen resting-state networks164; clustering = fraction of triangles including region i; mean first passage time = average time for a random walker to travel from region i to any other region.
