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Abstract #3053

Highly accelerated graph theory implementations show benefits of finer cortical parcellations for group connectomic analyses

Greg D Parker1, Mark Drakesmith1,2, and Derek K Jones1,2

1CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom, 2Neuroscience and Mental Health Research Institute (NMHRI), School of Medicine, Cardiff University, Cardiff, United Kingdom

Graph theoretical connectome analysis1 is an increasingly important research area. There is, however, high computational overhead required to: (a) produce whole or partial brain tractographies; (b) convert tractographies into binary or weighted graphs; and (c) analyse those graphs according to multiple, often complex graph metrics. We have developed GP-GPU accelerated implementations of each step. Exploiting the resultant increase in computational power, we examined the effects of increasing streamline sampling densities and number of cortical parcellations on separability of connectomes between first episode psychosis patients and controls. We show finer cortical parcellation increases separability (while increasing streamline density reduces it).

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