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

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

doi:10.3389/fnhum.2023.1100683

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

Front Hum Neurosci. 2023 Jun 14:17:1100683. doi: 10.3389/fnhum.2023.1100683. eCollection 2023.

Authors

Xianghong Meng^#¹, Kan Deng^#^{2

3}, Bingsheng Huang², Xiaoyi Lin², Yingtong Wu², Wei Tao¹, Chuxuan Lin², Yang Yang⁴, Fuyong Chen¹

Affiliations

¹ Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen, China.
² Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
³ MSC Clinical and Technical Solutions, Philips Healthcare, Guangzhou, China.
⁴ Department of Radiology, Suining Central Hospital, Suining, China.

^# Contributed equally.

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 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 superior temporal 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.

Conclusion: 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 behavior by neuroimaging information could assist doctors in the presurgical evaluation of TLE.

Keywords: cognitive impairment; machine learning; neuropsychology; structural magnetic resonance imaging; temporal lobe epilepsy.

Grants and funding

This work was supported by the Guangdong Basic and Applied Basic Research Foundation (No. 2020A1515010571), the Shenzhen Science and Technology Project (JCYJ20200109114014533), the Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (No. 2021SHIBS0003), SZU Top Ranking Project, Shenzhen University (860/000002100108), the Discipline Layout of Fundamental Research of Shenzhen Science and Technology Innovation Commission (No. JCYJ20170412111316339), the Sanming Project of Medicine in Shenzhen “Multidisciplinary epilepsy diagnosis and treatment team of Prof. Wang Yuping from Xuanwu Hospital Capital Medical University” (SZSM2020006), and Research Fund Program of Suining Central Hospital (2022ypj11).