2025/11/05 by Zhu, Xukun, Michael W. Lutz, Lutz, Michael W +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Methodology (stat.ME) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2511.03605
openalex publication_date 2025/11/05 · openalex created_date 2025/11/07 · openalex updated_date 2026/07/28
Subtle alterations in brain network topology often evade detection by traditional statistical methods. To address this limitation, we introduce a Bayesian inference framework for topological comparison of brain networks that probabilistically models within- and between-group dissimilarities. The framework employs Markov chain Monte Carlo sampling to estimate posterior distributions of test statistics and Bayes factors, enabling graded evidence assessment beyond binary significance testing. Simulations confirmed statistical consistency to permutation testing. Applied to fMRI data from the Duke-UNC Alzheimer's Disease Research Center, the framework detected topology-based network differences that conventional permutation tests failed to reveal, highlighting its enhanced sensitivity to early or subtle brain network alterations in clinical neuroimaging.