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Abstract #3368

Bundle Analytics: a computational and statistical analyses framework for tractometric studies

Bramsh Qamar Chandio1, Jaroslaw Harezlak2, Serge Koudoro 1, David Reagan 3, and Eleftherios Garyfallidis1

1Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States, 2School of Public Health, Indiana University Bloomington, Bloomington, IN, United States, 3Pervasive Technology Institute, Indiana University Bloomington, Bloomington, IN, United States

Bundle Analytics promises fast, robust, and flexible computational and statistical analyses for tractometric studies on clinical data. It uses information from both tractometry, and anatomy to analyze the extracted fiber bundles from challenging clinical datasets. It uses streamline-based efficient algorithms to register and extract fiber bundles from a tractogram, and applies linear mixed models in the extracted bundles to find significant differences at specific locations of the bundles across groups. Finally, the method does not require training, an important advantage over deep learning methods.

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