  Functional connectivity fingerprints are among today's best choices to obtain
a faithful sampling of an individual's brain and cognition in health and
disease. Here we make a case for key advantages of analyzing such connectome
profiles using Bayesian analysis strategies. They (i) afford full probability
estimates of the studied neurocognitive phenomenon (ii) provide analytical
machinery to separate methodological uncertainty and biological variability in
a coherent manner (iii) usher towards avenues to go beyond classical
null-hypothesis significance testing and (iv) enable estimates of credibility
around all model parameters at play and thus enable predictions with
uncertainty intervals for single subject. We pick research questions about
autism spectrum disorder as a recurring theme to illustrate our methodological
arguments.
