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Functional connectivity fingerprints are among today's best choices to obtaina faithful sampling of an individual's brain and cognition in health anddisease. Here we make a case for key advantages of analyzing such connectomeprofiles using Bayesian analysis strategies. They (i) afford full probabilityestimates of the studied neurocognitive phenomenon (ii) provide analyticalmachinery to separate methodological uncertainty and biological variability ina coherent manner (iii) usher towards avenues to go beyond classicalnull-hypothesis significance testing and (iv) enable estimates of credibilityaround all model parameters at play and thus enable predictions withuncertainty intervals for single subject. We pick research questions aboutautism spectrum disorder as a recurring theme to illustrate our methodologicalarguments.

Analyzing Brain Circuits in Population Neuroscience: A Case to Be a Bayesian