Figure Materials for publication: Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

This work explored whether a combination of structural and functional connectivity can improve models of cognitive performance, and whether this differs by cognitive domain. Principal Component Analysis (PCA) was applied to cognitive data from the Human Connectome Project. Four components were obtained, reflecting Retention and Retrieval, Processing Speed, Self-regulation, and Encoding. The PCA-Regression approach was applied to predict cognitive performance using structural, functional and joint structural-functional components.

The .edge files have been used to project the regression models in cognitive connectome space. .nv file was used as the selected glass brain. .node file contains center of mass coordinates used to place Shen et al (2012) 278 nodes in the glass brain. .m files were used to generate the figures and files used.

This file contains the supplementary material of this work.

A1. Describes behavioural measures used to obtain the 4 cognitive components.

A2. Contains regression model statistics including model evidence, explained variance, error and cross-validation error.

A3. Presents projections of models into space of cognitive clusters generated with Neurosynth.

A4. Presents projection of models in resting-network space (Yeo et al. 2011).