Evaluating structural brain networks based on their performance in predicting functional connectivity

Structural networks are described as graphs, which only summarize microstructural properties recovered via tractography. The edges of a brain graph may reflect the number of streamlines connecting each pair of regions or the average fractional anisotropy or average mean diffusivity and so on. Understanding the implication of these network properties is not straightforward. Here, we hypothesize that a more accurate reconstructed structural network would be able to predict functional connectivity better. We evaluate how well structural brain networks predict functional connectivity based on sparse canonical correlation analysis.

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