Access Type
Open Access Dissertation
Date of Award
January 2016
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Psychiatry and Behavioral Neurosciences
First Advisor
Jeffrey A. Loeb
Second Advisor
Jeffrey A. Stanley
Abstract
Epilepsy is a serious neurological disorder that affects 1% percent of the global population. Despite its status as one of the oldest neurological disorders known to man, its mechanisms remain poorly understood. Available medications are not curative but provide symptomatic management and do not work for well for more than 30 percent of patients. Because it is nearly impossible to predict on an individual level who will eventually develop epilepsy, it is also a disorder that can only be diagnosed after the patient has experienced established seizure activity, eliminating any possibility of stopping the disorder in its prodromal phase, before the patients are symptomatic. Availability of a reliable and non-invasive biomarker tool that can predict and identify the development of epilepsy would dramatically change how the disorder is detected, monitored, managed, and treated. In this project, we tested the potential of 1H MRS to provide metabolite biomarkers of epilepsy and epileptogenesis, both in human neocortical tissue obtained from intractable epilepsy patients who underwent resective surgery and also in a longitudinal rat model of epileptogenesis, using interictal epileptiform discharges as a surrogate indicator of disease progression. Using 1H MRS, we found unique metabolite differences that are highly predictive of epileptic and non-epileptic neocortex in humans that also partially overlaps with findings from our rat model. These findings provide evidence that 1H MRS is capable of identifying metabolite changes specific to epilepsy and may lead to reliable and non-invasive biomarkers of epilepsy and epileptogenesis in the future.
Recommended Citation
Wu, Helen, "Identification Of Metabolite Biomarkers In Epilepsy Using 1h Mrs" (2016). Wayne State University Dissertations. 1672.
https://digitalcommons.wayne.edu/oa_dissertations/1672