Metabolic heterogeneity of the normal human brain: multivariate analysis of ^sup 1^H MRS in vivo spectra acquired at 3T

• Published in: Metabolomics Vol. 13; no. 4; p. 1
• Main Authors: Skorupa, Agnieszka, Boguszewicz, Ukasz, Kijonka, Marek, Sokó, Maria
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Nature B.V 01-04-2017
• ISSN: 1573-3882; 1573-3890
• DOI: 10.1007/s11306-017-1171-5

Introduction In recent years multivariate projection techniques of data analysis (PCA, PLS-DA) have been increasingly used for detection of complex 1H MRS derived metabolic signatures in pathologic conditions. However, these techniques have not been applied in the studies of metabolic heterogeneity of the normal human brain. Objective In this work we extended current knowledge about regional distribution of metabolites by multivariate analysis of metabolite levels obtained from various cortical and subcortical regions. Methods The studied group consisted of 71 volunteers with no neurological disorders. The metabolite levels obtained from short echo time 1H MRS in vivo spectra were subjected to univariate and multivariate analysis. Results The major variance direction in the dataset was dominated by glutamine+glutamate, creatine, myo-inositol and was successful in differentiation of the cortical grey matter and cerebellar vermis from the cortical white matter, pons, basal ganglia, hippocampus and thalamus. The projection plane formed by the second and third variance directions was dominated by N-acetylaspartate+N-acetylaspartylglutamate, choline and glutamine+glutamate variation not explained by the first direction. This plane revealed a huge metabolic contrast between the pons and basal ganglia, differentiation between the cortical grey matter regions and cerebellar vermis as well as biochemical heterogeneity between the regions such as: thalamus, basal ganglia and hippocampus. Conclusion Multivariate approach to 1H MRS data analysis provides an insight into the normal brain biochemistry and is helpful in understanding the regional heterogeneity of the normal brain. Such knowledge is crucial for a proper interpretation of altered metabolic pathways in diseases.