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