  Objective: New measures of human brain connectivity are needed to address
gaps in the existing measures and facilitate the study of brain function,
cognitive capacity, and identify early markers of human disease. Traditional
approaches to measure functional connectivity between pairs of brain regions in
functional MRI, such as correlation and partial correlation, fail to capture
nonlinear aspects in the regional associations. We propose a new machine
learning based measure of functional connectivity which efficiently captures
linear and nonlinear aspects. Approach: We propose two new EC measures. The
first, a machine learning based measure of effective connectivity, measures
nonlinear aspects across the entire brain. The second, Structurally Projected
Granger Causality adapts Granger Causal connectivity to efficiently
characterize and regularize the whole brain EC connectome to respect underlying
biological structural connectivity. The proposed measures are compared to
traditional measures in terms of reproducibility and the ability to predict
individual traits in order to demonstrate these measures internal validity. We
use four repeat scans of the same individuals from the Human Connectome Project
and measure the ability of the measures to predict individual subject
physiologic and cognitive traits. Main results: The proposed new FC measure of
ML.FC attains high reproducibility with an R squared of 0.44, while the
proposed EC measure of SP.GC attains the highest predictive power with an R
squared of 0.66. Significance: The proposed methods are highly suitable for
achieving high reproducibility and predictiveness.
