AUTHOR=Meng Xianghong , Deng Kan , Huang Bingsheng , Lin Xiaoyi , Wu Yingtong , Tao Wei , Lin Chuxuan , Yang Yang , Chen Fuyong 

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

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1100683

DOI=10.3389/fnhum.2023.1100683

ISSN=1662-5161

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 machine learning approaches with neuropsychological tests were employed to classify TLE using leave-one-out cross-validation. A generalised linear model was used to analyse the relationship between brain alterations and neuropsychological tests.
Results: We found that logistic regression with the selected neuropsychological tests generated classification accuracies of 87.0%, with an area under the receiver operating characteristic curve (AUC) of 0.89. Three neuropsychological tests were acquired as significant neuropsychological signatures for the diagnosis of TLE. We also found that the right-left orientation test difference was related to the superiortemporal and the banks of the superior temporal sulcus (bankssts). The conditional association learning test (CALT) was associated with the cortical thickness difference in the lateral orbitofrontal area between the two groups, and the component verbal fluency test was associated with the cortical thickness difference in the lateral occipital cortex between the two groups.
Conclusions: These results showed that machine learning-based classification with the selected neuropsychological data can successfully classify TLE with high accuracy compared to previous studies, which could provide kind of warning sign for surgery candidate of TLE patients. In addition, understanding the mechanism of cognitive behaviour using neuroimaging information could assist doctors in the presurgical evaluation of TLE.