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Abstract #2979

A machine learning approach to identify structural connections affected in diffuse axonal injury

J. Mitra 1 , S. Ghose 1 , K-K. Shen 1 , K. Pannek 2 , P. Bourgeat 1 , J. Fripp 1 , O. Salvado 1 , J. L. Mathias 3 , D. J. Taylor 4 , and S. Rose 1

1 Australian e-Health & Research Centre, CSIRO Digital Productivity Flagship, Herston, QLD, Australia, 2 Imperial College London, London, United Kingdom, 3 School of Psychology, University of Adelaide, Adelaide, SA, Australia, 4 Dept. of Radiology, The Royal Adelaide Hospital, Adelaide, SA, Australia

Patients with mild TBI sustain diffuse axonal injury (DAI) which is microscopic in nature and difficult to detect using conventional MRI. Diffusion MRI, along with probabilistic tractography, is ideally suited to detect DAI within specific white matter (WM) pathways. These approaches, based on measures of structural connectivity, can be used to identify damaged neural pathways in group-wise analyses of TBI and healthy control cohorts. We present a new method to identify significantly different and discriminative structural connections between the TBI and healthy control groups by integrating a statistical GLM-based (generalized linear model) network clustering and random forest classifier.

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