Statistical Approaches to Characterize Functional Connectivity in Brain and Physiologic Networks on a Single-Subject Basis.

The trend toward personalized medicine necessitates drawing conclusions from descriptive indexes of physiopathological states estimated from individual recordings of biomedical signals, using statistical analyses that focus on subject-specific differences between experimental conditions. In this context, the present work introduces an approach to assess functional connectivity in brain and physiologic networks by pairwise information-theoretic measures of coupling between signals, whose significance and variations between conditions are statistically validated on a single-subject basis through the use of surrogate and bootstrap data analyses. The approach is illustrated on single-subject recordings of (i) resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired in a pediatric patient with hepatic encephalography associated to a portosystemic shunt and undergoing liver vascular shunt correction, and of (ii) cardiovascular and cerebrovascular time series acquired at rest and during head-up tilt in a subject suffering from orthostatic intolerance.