Analyzing Brain Networks in Population Neuroscience: A Case for the Bayesian Philosophy

Danilo Bzdok (1, 2, 3) , Dorothea Floris (4) , Andre Marquand (5)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 MILA - Montreal Institute for Learning Algorithms [Montréal]
3 Montreal Neurological Hospital
4 Donders Institute for Brain, Cognition and Behaviour
5 Radboud University Medical Center [Nijmegen]

Network connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition. Widely available MRI scanners can provide rich information tapping into network recruitment and reconfiguration that now scales to hundreds and thousands of humans. Here we contemplate advantages of analyzing such connectome profiles using Bayesian strategies. These analysis techniques afford full probability estimates of the studied network coupling phenomena; provide analytical machinery to separate epistemological uncertainty and biological variability in a coherent manner; usher towards avenues to go beyond binary statements on existence vs. non-existence of an effect; and afford credibility estimates around all model parameters at play, which thus enable single-subject subjects predictions with rigorous uncertainty intervals. We illustrate the brittle boundary between healthy and diseased brain circuits by autism spectrum disorder as a recurring theme where, we argue, network-based approaches in neuroscience will require careful probabilistic answers.

Mots clés: connectome-based prediction; uncertainty; confounding influences; statistical learning

Domaines: Bio-informatique [q-bio.QM]

Danilo Bzdok, Dorothea Floris, Andre Marquand. Analyzing Brain Networks in Population Neuroscience: A Case for the Bayesian Philosophy. Philosophical Transactions of the Royal Society of London. B (1887–1895), 2020. ⟨hal-02447507⟩