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Abstract #1260

Nested support vector machine applied to structural and diffusion MR features for Alzheimer's disease prediction

Giovanni Giulietti1, Mara Cercignani2, and Marco Bozzali1

1Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy, 2Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom

The current study is an application of nested support vector machine (SVM) to distinguish healthy subjects and patients with Alzheimer’s disease using very few features coming from structural (T1) and diffusion (DWI) MR. After having segmented the T1 images in GM, WM and CSF, mean values of fractional_anisotropy, mean_diffusivity, radial_diffusivity and axial_diffusivity were computed in GM and WM; volume of GM and WM as percentage of total_intracranial_volume were also assessed. Therefore we computed 1023 different SVMs, one for each possible combination of the 10 features. Surprisingly, the WM diffusion measures resulted to be the most specific of dementia status.

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