Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data

Abstract

Objective To compare the location of suspect lesions detected by computational analysis of multimodal magnetic resonance imaging data with areas of seizure onset, early propagation, and interictal epileptiform discharges (IEDs) identified with stereoelectroencephalography (SEEG) in a cohort of patients with medically refractory focal epilepsy and radiologically normal magnetic resonance imaging (MRI) scans. Methods We developed a method of lesion detection using computational analysis of multimodal MRI data in a cohort of 62 control subjects, and 42 patients with focal epilepsy and MRI‐visible lesions. We then applied it to detect covert lesions in 27 focal epilepsy patients with radiologically normal MRI scans, comparing our findings with the areas of seizure onset, early propagation, and IEDs identified at SEEG. Results Seizure‐onset zones (SoZs) were identified at SEEG in 18 of the 27 patients (67%) with radiologically normal MRI scans. In 11 of these 18 cases (61%), concordant abnormalities were detected by our method. In the remaining seven cases, either early seizure propagation or IEDs were observed within the abnormalities detected, or there were additional areas of imaging abnormalities found by our method that were not sampled at SEEG. In one of the nine patients (11%) in whom SEEG was inconclusive, an abnormality, which may have been involved in seizures, was identified by our method and was not sampled at SEEG. Significance Computational analysis of multimodal MRI data revealed covert abnormalities in the majority of patients with refractory focal epilepsy and radiologically normal MRI that co‐located with SEEG defined zones of seizure onset. The method could help identify areas that should be targeted with SEEG when considering epilepsy surgery.

The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy

“…Improving the detection of brain areas responsible for epileptic seizure generation in patients that do not respond to pharmacotherapy and lack macroscopic structural abnormalities at the same time is a challenging topic that many researchers are trying to resolve. Due to an advancement in computational possibilities, a growing number of studies fuse multimodal data in order to achieve more accurate results through complex analytical approaches 41 , 42 . The detection rate of such advanced computational algorithms of course strongly depends on the accuracy of each method that enters the multimodal fusion.…”

The Temporal Lobe as a Symptomatogenic Zone in Medial Parietal Lobe Epilepsy

“…Recently, computational analyses of multimodal MRI data have been utilized to increase the chances of detecting seizure-onset zones in non-lesional focal epilepsy ( 45 ). The computational analysis is developed in samples of patients with lesional epilepsy before application to non-lesional cases.…”

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

“…Structural MRI in particular plays a major role in the visual detection of focus lesions, and it has been widely used in clinical practice for epilepsy (Bernasconi et al, 2019). As seen in Table 3, there have been various applications of machine learning for lesion identification to improve the detection rate or to develop automated algorithms (Hong et al, 2014;Ahmed et al, 2015;Rudie et al, 2015;El Azami et al, 2016;Adler et al, 2017;Jin et al, 2018;Tan et al, 2018;Wang et al, 2018b;Mo et al, 2019;Alaverdyan et al, 2020;Lee et al, 2020a;Wagstyl et al, 2020;Snyder et al, 2021;Zhang et al, 2021), which would be concordant with the seizure onset zone detected by intracranial EEG (Kanber et al, 2021). Focal cortical dysplasia (FCD), which is a common cause of intractable epilepsy, is characterized by abnormal cortical thickness, blurring of the gray-white matter junction, and T2/FLAIR hyperintensity (Bernasconi et al, 2019).…”