Seizure-induced neuronal injury: Human data

Article abstractEvidence that recurrent epileptic seizures may cause neuronal injury in some patients has been inferred from clinical observation, neuropsychological assessments, and neuroimaging studies. Cross-sectional investigations have yielded conflicting results and it is not possible to draw conclusions regarding causation, rather than merely association, from such designs. However, there is also evidence from in vivo biochemical studies that seizures may cause neuron injury. The heterogeneity of the epilepsies, epileptic seizures, co-morbidities, treatment regimens, and individual patient susceptibility all complicate the picture and inhibit the drawing of conclusions that are uniformly applicable. Longitudinal neuroimaging studies have the potential to objectively identify structural changes in the brain that are markers of neuronal injury. Such studies are a major undertaking, requiring age-matched control groups and consistent image acquisition and analysis techniques. One needs to analyze not only changes in group means but also the number of patients who show significant changes in imaging parameters that exceed the limits of test–retest reliability and changes in age-matched controls. Quantitative analysis of MRI T1-weighted volumetric datasets can reliably identify changes in cerebral and hippocampal volumes of 1–3% in individual subjects. The sensitivity of such quantitative analysis of structural data to identify functionally significant changes is not yet certain. Functional imaging techniques such as MR spectroscopy, PET, and SPECT may be more sensitive for detecting cerebral abnormalities, but their test–retest reliability is inferior. Other MRI tools, such as diffusion tensor imaging, may be useful for evaluating secondary cerebral damage after seizures, both acutely and chronically. Present evidence suggests that, to detect significant treatment effects, longitudinal studies of putative neuroprotective agents, using neuroimaging methods as a surrogate end point, would require at least a 3-year observation period, include large numbers of patients, and provide stratification for important clinical variables.