Bridging the gap between metabolic profile determination and visualization in neurometabolic disorders: a multivariate analysis of proton magnetic resonance in vivo spectra. (21st March 2013) - Skorupa, Agnieszka; Jamroz, Ewa; Paprocka, Justyna; Sokół, Maria; Wicher, Magdalena; Kiełtyka, Aleksandra
Abstract : The purpose of this work was to check the degree of overlap between rare inborn errors of metabolism and other neurological disorders using principal component analysis of proton magnetic resonance spectroscopy ( 1 H MRS) in vivo data. We examined 60 patients (median age of 22 months). Fourteen of them were diagnosed with neurometabolic disorders (three cases of metachromatic leukodystrophy, two cases of Canavan disease, two cases of megalencephalic leukoencephalopathy with subcortical cysts, three cases of mitochondrial cytopathy, one case of nonketotic hyperglycinemia, one case of globoid leukodystrophy, one case of congenital disorders of glycosylation, and one case of ethylmalonic encephalopathy). The remaining 46 patients were diagnosed with epilepsy, cerebral palsy, and developmental delay. Results obtained from principal component analysis of complete unresolved 1 H MRS in vivo spectra were interpreted parallelly with LCModel‐derived metabolite levels. The main attention was paid to the following metabolites: N ‐acetylaspartate, glutamate + glutamine, creatine, choline, myo‐inositol signal with an uncertain contribution of glycine, and glucose. 1 H MRS in vivo coupled with multivariate analysis is an efficient tool in visualization of metabolic abnormalities in several inborn errors of metabolism (metachromatic leukodystrophy, globoid leukodystrophy, megalencephalic leukoencephalopathy with subcortical cysts, and Canavan disease). Copyright © 2013 John Wiley & Sons, Ltd. Abstract : Principal component analysis was coupled with proton magnetic resonance spectroscopy. The overlap between neurometabolic and other neurological disorders has to be checked. Separation of various groups of spectra was analyzed after dimensionality reduction. Several neurometabolic disorders (metachromatic leukodystrophy, globoid leukodystrophy, Canavan disease, megalencephalic leukoencephalopathy with subcortical cysts) were separated from the main bulk of data. The presented technique is an efficient tool in exploration of heterogeneous datasets.