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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.