Abstract found on Wiley Online Library
Objective: Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for non-invasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional MRI (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients.
Methods: 2132 healthy controls and 32 pre-operative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data was used to generate training samples for a 3D convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure-onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions.
Results: The 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, while the resting state networks which colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with post-surgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel class > 1 compared to Engel class 1.
Significance: Non-invasive techniques capable of localizing the seizure-onset zone could improve pre-surgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning.