Purpose of review Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments.
Recent findings The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.
Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy.
Summary Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.