Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.

ABSTRACT: About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE from healthy individuals. This study aimed to explore the use of machine learning methods to diagnose MRI-negative TLE patients based on single and combined resting-state fMRI (rs-fMRI) metrics. This study investigates the diagnostic implications of using both singular and composite resting-state fMRI (rs-fMRI) indices in patients with MRI-negative TLE. We carried out a retrospective analysis of the clinical data and rs-fMRI data of 90 patients with MRI-negative TLE and 90 healthy controls (HCs). Next, the participants were divided into a trai

SUBMITTER: Yan L 

PROVIDER: S-EPMC12627538 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature