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Abstract #2471

Multivariate asymmetry analysis (MVAA): applications in temporal lobe epilepsy

Diego Cantor-Rivera 1 , Terry M. Peters 2 , and Ali R. Khan 2

1 Biomedical Engineering Graduate Program, Western University, London, ON, Canada, 2 Medical Biophysics, Western University, London, ON, Canada

This work presents a novel multivariate asymmetry analysis for investigating focal structural abnormalities. The novel method uses multi-parametric imaging data non-rigidly registered to a symmetric template to estimate asymmetry measures using locally-sampled cumulative distribution functions (Kolmogorov-Smirnov test). We applied it to investigate structural abnormalities in temporal lobe epilepsy using quantitative relaxometry, diffusion tensor imaging, and voxel-based morphometry. Whole brain Mahalanobis distance maps were employed in a support vector machine classification to show that the use of asymmetry significantly improves discrimination between temporal lobe epilepsy patients and healthy controls.

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