Abstract
We aimed to determine the alterations in the subcortical structures of patients with idiopathic generalized epilepsy with tonic–clonic seizures (IGE-GTCS) via MRI volumetry and vertex-based shape analysis and to evaluate the relationships between MRI measures and drug responses. In a follow-up sample of 48 patients with IGE-GTCS and 48 matched normal controls (NCs), high-resolution 3D T1WI was performed at baseline. After 1 year of follow-up, 31 patients were classified as seizure free (SF) and 17 as drug resistant (DR). The volumes of subcortical structures were extracted, and vertex-based shape analysis was performed using FSL-Integrated Registration and Segmentation Toolbox (FSL-FIRST). Comparisons among groups were calculated adjusting for covariates [age, sex, and intracranial volume (ICV)]. Analysis of the relationships among imaging biomarkers along with frequency and duration was assessed using partial correlations. The differential imaging indicators were used as features in a linear support vector machine (LSVM). The DR group displayed significant regional atrophy in the volume of the left amygdala compared with NCs (p = 0.004, false discovery rate corrected) and SF patients (p = 0.029, uncorrected). Meanwhile, vertex-based shape analysis showed focal inward deformation in the basolateral subregion of the left amygdala in DR compared with the results for SF and NC (p < 0.05, FWE corrected). There were significant correlations between the volume changes and seizure frequency (r = −0.324, p = 0.030) and between shape (r = −0.438, p = 0.003) changes and seizure frequency. Moreover, the volume of the left thalamus in the DR group was significantly correlated with seizure frequency (r = −0.689, p = 0.006). The SVM results revealed areas under the receiver operating characteristic curve of 0.82, 0.68, and 0.88 for the classification between SF and DR, between SF and NC, and between DR and NC, respectively. This study indicates the presence of focal atrophy in the basolateral region of the left amygdala in patients with IGE drug resistance; this finding may help predict drug responses and suggests a potential therapeutic target.
Highlights
Idiopathic generalized epilepsy (IGE) with tonic–clonic seizures (GTCS), one of the main genetic generalized epilepsy syndromes, representing approximately 15–20% of all epilepsies (Jallon and Latour, 2005), typically responds well to antiepileptic drug (AED) treatment
They met the following inclusion and exclusion criteria: (1) those diagnosed with idiopathic/hereditary generalized epilepsy with only GTCS according to the current International League Against Epilepsy (ILAE) classification of seizure types (Fisher et al, 2017) based on electroclinical symptomatology were included; (2) subjects who had psychiatric disorders, brain structural lesions, systemic disease, or other magnetic resonance imaging (MRI) contraindications were excluded; (3) prospective clinical treatment follow-up of at least 1 year after an imaging investigation was required for inclusion; and (4) participation in a 3-T study MRI on the same scanner and 3D T1WI results were necessary for inclusion
Anatomical MRI data from 96 participants were included in the study, consisting of 31 seizure free (SF) patients, 17 drug resistant (DR) patients, and 48 normal controls (NCs)
Summary
Idiopathic generalized epilepsy (IGE) with tonic–clonic seizures (GTCS), one of the main genetic generalized epilepsy syndromes, representing approximately 15–20% of all epilepsies (Jallon and Latour, 2005), typically responds well to antiepileptic drug (AED) treatment. Approximately one-third of patients still develop drug resistance (Brodie et al, 2012) despite the availability of over 20 new AEDs in the past 30 years (Engel and Pitkanen, 2020). IGE appears normal on conventional magnetic resonance imaging (MRI) with a diffuse mechanism of seizure onset and no identifiable pathogenesis other than hereditary susceptibility. Identifying common biological disease pathways may help clarify diagnostic and prognostic biomarkers, which in turn helps optimize individual treatment (Pitkänen et al, 2016). We aimed to identify imaging biomarkers to predict prognosis in IGE patients
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