Imaging Connectomics and the Understanding of Brain Diseases.

Abstract

Neuroimaging-based personalized medicine is emerging to characterize brain disorders and their evolution at the patient level. In this chapter, we present the most classic methods used to infer large-scale brain connectivity based on functional MRI. We adopt a modeling perspective where every connectivity measure is linked to a specific model that allows to interpret the connectivity estimate. This perspective allows to analyze the quality of retrieved connectivity profiles in terms of modeling error and estimation error. In the first part of the chapter, we present undirected functional connectivity (Pearson's correlation and MI) and effective connectivity (partial correlation), as well as directed effective connectivity (VAR, MOU, Granger causality, DCM). In addition, some of these measures correspond to fully connected graphs (Pearson's correlation) while others to sparse ones (MOU, DCM), where the sparsity can come from the integration of functional and structural data. In the second part, we claim that machine learning tools are better suited than null-hypothesis testing to link the estimated connectomes with diagnosis and prognosis of neuropsychiatric diseases. Finally, we propose that linear models and features selection are preferable to more complex and nonlinear tools (when prediction performance is on a par) for building interpretable algorithms to predict clinical variables.