New Statistical Tool Unlocks Brain Connectivity Patterns from Brain Scans
Researchers have developed a faster, more accurate method to analyze how different brain regions communicate during brain scans, potentially accelerating diagnosis of neurological disorders. The technique allows doctors to extract individual patient insights rather than relying on group averages, opening new pathways for personalized brain health assessment and treatment.
Originaltitel: Bayesian modelling of effective and functional brain connectivity using hierarchical vector autoregressions
<p>Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression hierarchical model for analysing brain connectivity within resting-state functional magnetic resonance imaging, and apply it to simulated data and a real data set with subjects in different groups. Our approach models functional and effective connectivity simultaneously and allows for both group- and single-subject inference. We combine analytical marginalization with Hamiltonian Monte Carlo to obtain highly efficient posterior sampling. We show that our model gives similar inference for effective connectivity compared to models with a common covariance matrix to all subjects, but more accurate inference for functional connectivity between regions compared to models with more restrictive covariance structures. A Stan implementation of our model is available on GitHub.</p>