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A conceptual framework for the use of neuroimaging to study and predict pharmacoresistance in epilepsy
Author(s) -
PohlmannEden Bernd,
Crocker Candice E.,
Schmidt Matthias H.
Publication year - 2013
Publication title -
epilepsia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.687
H-Index - 191
eISSN - 1528-1167
pISSN - 0013-9580
DOI - 10.1111/epi.12190
Subject(s) - epilepsy , hippocampal sclerosis , magnetic resonance imaging , cortical dysplasia , neuroimaging , medicine , temporal lobe , population , epileptogenesis , neuroscience , diffusion mri , disease , pediatrics , psychology , psychiatry , pathology , radiology , environmental health
Summary Twenty percent to 49% of newly treated patients with epilepsy will develop pharmacoresistance ( PR ). The mechanisms leading to PR are unclear. There is currently no unifying theory to explain the variety of presentations of PR and the diversity of potential contributing factors. Etiology of seizures seems to play a critical role in at least a subset of PR . Many magnetic resonance imaging ( MRI ) studies in the advanced stages of epilepsy suggest a strong association between lesions such as hippocampal sclerosis and focal cortical dysplasia and PR . Unfortunately, almost all of these studies are cross‐sectional and retrospective. There is a need for a new perspective on the role of preexisting lesions in the evolution of epilepsy and PR . We propose in this article to study a unique population of drug‐naive patients with either first seizure or new‐onset epilepsy longitudinally with advanced MRI imaging techniques, including magnetic resonance spectroscopy and diffusion tensor imaging. We hope to be able to monitor imaging findings and the development of PR early in the course of the disease in a subset of these patients with temporal lobe epilepsy ( TLE ). Our goal is to understand the pathogenesis of PR , to dissect changes associated with the development of PR from changes associated with chronic seizures and medication, and ultimately to predict PR at the onset of disease.