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Abstract #2843

Megatrack: A fast and effective strategy for group comparison and supervised analysis of large-scale tractography datasets

Flavio Dell'Acqua 1 , Luis Lacerda 1 , Rachel Barrett 1 , Lucio D'Anna 2 , Stella Tsermentseli 3 , Laura Goldstein 4 , and Marco Catani 2

1 Dept of Neuroimaging, King's College London, London, United Kingdom, 2 Dept of Forensic and Neurodevelopmental Sciences, King's College London, London, United Kingdom, 3 Dept of Psychology, University of Greenwich, London, United Kingdom, 4 Dept of Psychology, King's College London, United Kingdom

While manual dissections of tractography datasets may offer the best results in terms of anatomical accuracy, they are also extremely time consuming making difficult to extend them to large-scale datasets. On the contrary, automatic dissections or clustering approaches allow researcher to efficiently dissect large numbers of datasets but at the expense of decreased accuracy in the final dissection, leaving often little to no user interaction to control for artifactual components. In this study we propose a novel approach that drastically reduces the time required for manual dissections while preserving the ability to extract automatically tract specific measures from large datasets

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