Using machine learning to classify temporal lobe epilepsy based on diffusion MRI.

ABSTRACT:

Background

It is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)-based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).

Methods

Thirty-two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross-validation (CV) was repeated for 1000 iterations.

SUBMITTER: Del Gaizo J 

PROVIDER: S-EPMC5651385 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature