An MRspec database query and visualization engine with applications as a clinical diagnostic and research tool

- Largest collection of quantitative brain MRS; 4,430 pediatric patients, systematically ascertained, complemented with clinical information.
- Development and application of a robust and flexible computational tool for facilitating the exploration of vast metabolite datasets.
- Proven value in discovering neurochemical trends for clinical diagnosis, treatment monitoring, and research.
- Free access fostering widespread use of tool and data base.
- Increasing diagnostic yield and understanding of neuro-metabolic processes and conditions.

PurposeProton magnetic resonance spectroscopy (MRspec), one of the very few techniques for in vivo assessment of neuro-metabolic profiles, is often complicated by lack of standard population norms and paucity of computational tools.Methods7035 scans and clinical information from 4430 pediatric patients were collected from 2008 to 2014. Scans were conducted using a 1.5 T (n = 3664) or 3 T scanner (n = 3371), and with either a long (144 ms, n = 5559) or short echo time (35 ms, n = 1476). 3055 of these scans were localized in the basal ganglia (BG), 1211 in parieto-occipital white matter (WM). 34 metabolites were quantified using LCModel. A web application using MySQL, Python and Flask was developed to facilitate the exploration of the data set.ResultsAlready piloting the application revealed numerous insights. (1), N-acetylaspartate (NAA) increased throughout all ages. During early infancy, total choline was highly varied and myo-inositol demonstrated a downward trend. (2), Total creatine (tCr) and creatine increased throughout childhood and adolescence, though phosphocreatine (PCr) remained constant beyond 200 days. (3), tCr was higher in BG than WM. (4), No obvious gender-related differences were observed. (5), Field strength affects quantification using LCModel for some metabolites, most prominently for tCr and total NAA. (6), Outlier analysis identified patients treated with vigabatrin through elevated γ-aminobutyrate, and patients with Klippel-Feil syndrome, Leigh disease and L2-hydroxyglutaric aciduria through low choline in BG.ConclusionsWe have established the largest MRSpec database and developed a robust and flexible computational tool for facilitating the exploration of vast metabolite datasets that proved its value for discovering neurochemical trends for clinical diagnosis, treatment monitoring, and research. Open access will lead to its widespread use, improving the diagnostic yield and contributing to better understanding of metabolic processes and conditions in the brain.