Bayesian Topological Analysis of Functional Brain Networks
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911250512871424 |
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| author | Zhu, Xukun Lutz, Michael W Songdechakraiwut, Tananun |
| author_facet | Zhu, Xukun Lutz, Michael W Songdechakraiwut, Tananun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03605 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Topological Analysis of Functional Brain Networks Zhu, Xukun Lutz, Michael W Songdechakraiwut, Tananun Methodology 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. |
| title | Bayesian Topological Analysis of Functional Brain Networks |
| topic | Methodology |
| url | https://arxiv.org/abs/2511.03605 |