Classification of temporal lobe epilepsy based on neuropsychological tests and exploration of its underlying neurobiology.

ABSTRACT:

Objective

To assist improving long-term postoperative seizure-free rate, we aimed to use machine learning algorithms based on neuropsychological data to differentiate temporal lobe epilepsy (TLE) from extratemporal lobe epilepsy (extraTLE), as well as explore the relationship between magnetic resonance imaging (MRI) and neuropsychological tests.

Methods

Twenty-three patients with TLE and 23 patients with extraTLE underwent neuropsychological tests and MRI scans before surgery. The least absolute shrinkage and selection operator were firstly employed for feature selection, and a machine learning approach with neuropsychological tests was employed to classify TLE using leave-one-out cross-validation. A generalized linear model was used to analyze the relationship between brain

SUBMITTER: Meng X 

PROVIDER: S-EPMC10307531 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature