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Updated: Oct 22, 2025
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Published on: August 5, 2014
Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double
Iman Beheshti1, Daichi Sone2, Norihide Maikusa3
1Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, 7- 61-2, Yatsuyamada, Koriyama, 963-8052, Japan.
Objective:
The main objective of this study was to determine the ability of double inversion recovery (DIR) data coupled with machine-learning algorithms to distinguish normal individuals from epileptic subjects and to identify the laterality of the focus side in MRI-negative, PET-positive temporal lobe epilepsy (TLE) patients.
Materials And Methods:
We used whole-brain DIR data as the input features with which to train a linear support-vector machine model in 63 participants who underwent high-resolution structural MRI and DIR scans. The subjects included 20 left TLE patients, 19 right TLE patients, and 24 healthy controls (HCs).
Results:
Using the DIR data, we achieved a robust accuracy of 87.30% for discriminating among the left TLE, right TLE, and HC groups as well as 84.61%, 97.72%, and 93.02% prediction accuracies for distinguishing left TLE from right TLE, HC from right TLE, and HC from left TLE, respectively.
Interpretation:
Our experimental results suggest that DIR data coupled with machine-learning algorithms provide a promising approach to identifying MRI-negative TLE patients, especially when fluorodeoxyglucose-PET is not available.
