Nonparametric Bayesian Clustering of Structural Whole Brain Connectivity in Full Image Resolution

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2014

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Diffusion magnetic resonance imaging enables measuring the structural connectivity of the human brain at a high spatial resolution. Local noisy connectivity estimates can be derived using tractography approaches and statistical models are necessary to quantify the brain’s salient structural organization. However, statistically modeling these massive structural connectivity datasets is a computational challenging task. We develop a high-performance inference procedure for the infinite relational model (a prominent non-parametric Bayesian model for clustering networks into structurally similar groups) that defines structural units at the resolution of statistical support. We apply the model to a network of structural brain connectivity in full image resolution with more than one hundred thousand regions (voxels in the gray-white matter boundary) and around one hundred million connections. The derived clustering identifies in the order of one thousand salient structural units and we find that the identified units provide better predictive performance than predicting using the full graph or two commonly used atlases. Extracting structural units of brain connectivity at the full image resolution can aid in understanding the underlying connectivity patterns, and the proposed method for large scale data driven generation of structural units provides a promising framework that can exploit the increasing spatial resolution of neuro-imaging technologies.
Original languageEnglish
Title of host publicationProceedings of the 4th International Workshop on Pattern Recognition in Neuroimaging (PRNI 2014)
Number of pages4
PublisherIEEE
Publication date2014
ISBN (print)978-1-4799-4149-0
StatePublished - 2014
Event4th International Workshop on Pattern Recognition in Neuroimaging - Tübingen, Germany

Workshop

Workshop4th International Workshop on Pattern Recognition in Neuroimaging
Number4
CountryGermany
CityTübingen
Period04/06/201406/06/2014
Internet address
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