Objective: The purpose of this study was to investigate functional connectivity (FC) changes in epileptogenic networks in intractable partial epilepsy obtained from resting-state fMRI by using intrinsic connectivity contrast (ICC), a voxel-based network measure of degree that reflects the number of connections to each voxel. Methods: We measured differences between intrahemispheric- and interhemispheric-ICC (ICC_{intra−inter}) that could reveal localized connectivity abnormalities in epileptogenic zones while more global network changes would be eliminated when subtracting these values. The ICC_{intra−inter} map was compared with the seizure onset zone (SOZ) based on intracranial EEG (icEEG) recordings in 29 patients with at least 1 year of postsurgical follow-up. Two independent reviewers blindly interpreted the icEEG and fMRI data, and the concordance rates were compared for various clinical factors. Results: Concordance between the icEEG SOZ and ICC_{intra−inter} map was observed in 72.4% (21/29) of the patients, which was higher in patients with good surgical outcome, especially in those patients with temporal lobe epilepsy (TLE) or lateral temporal seizure localization. Concordance was also better in the extratemporal lobe epilepsy than the TLE group. In 85.7% (18/21) of the cases, the ICC_{intra−inter} values were negative in the SOZ, indicating decreased FC within the epileptic hemisphere relative to between hemispheres. Conclusions: Assessing alterations in FC using fMRI-ICC map can help localize the SOZ, which has potential as a noninvasive presurgical diagnostic tool to improve surgical outcome. In addition, the method reveals that, in focal epilepsy, both intrahemispheric- and interhemispheric-FC may be altered, in the presence of both regional as well as global network abnormalities.

The number of patients suffering post-stroke seizure after ischemic stroke (PSSi) is quite considerable, especially because ischemic stroke is more prevalent than hemorrhage in the general population. This study aimed to determine the predicting factors for seizure recurrence in ischemic stroke survivors and develop a clinical scoring system for the prediction of risks for seizure recurrence after the first PSSi. We reviewed 3792 ischemic stroke patients from the Ewha Stroke Registry. A total of 124 (3.3 %) patients who experienced PSSi were recruited (mean follow-up for 44.4 months). Medical records concerning the etiology, functional disability, seizure onset latency from stroke, type of seizure, electroencephalography (EEG), and neuroimaging findings were statistically analyzed to derive a seizure recurrence risk scoring system. Seizures recurred in 35.4 % (17/48) of early PSSi patients (≤1 week since stroke onset) and 48.7 % (37/76) of late PSSi (>1 week) patients. Atrial fibrillation, large sized, and cortical stroke lesion were more common in late onset PSSi compared to those in early onset PSSi (p < 0.05). Seizure recurrence tended to be more prevalent in early PSSi patients with male gender, atrial fibrillation or cortical stroke lesion, severe functional disability, and partial seizures. Seizure recurrence in late PSSi group was more common in patients of young age (≤65 years old), male gender, large lesion size, and partial seizure type. The validity of seizure recurrence risk score in the early PSSi group was better when evaluating based on gender, atrial fibrillation, cortical lesion, functional disability, and partial seizure type, with sensitivity of 70.6 % and specificity of 71.0 %. Our study characterized the high risk group for seizure recurrence in patients with the first PSSi. PSSi patients with high risk score of seizure recurrence had a greater chance of developing epilepsy later. Therefore, they should be considered for further treatment such as antiepileptic drug medication in clinical practice.

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient’s data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.