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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