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

TL;DR

This paper advocates for using Bayesian analysis in population neuroscience to improve the interpretation of functional connectivity fingerprints, offering probabilistic insights, uncertainty quantification, and methodological advantages over traditional approaches.

Contribution

It introduces Bayesian strategies for analyzing connectome profiles, emphasizing their benefits in uncertainty estimation and advancing beyond null-hypothesis testing.

Findings

Bayesian methods provide full probability estimates of neurocognitive phenomena.

They enable separation of methodological uncertainty from biological variability.

The approach allows credible interval estimation and improved predictions for individuals.

Abstract

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.