Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Abstract

Proton (¹H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T₂ relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Keywords: Database; Human brain; In vivo; Meta-analysis; Proton MRS; Simulation1.

Figure 1:

PRISMA flow charts that show the database selection and inclusion process of the (A) concentration and (B) T₂ relaxation publications. Records “Removed Before Screening” were duplicates (identified from more than one database), reviews, meta-analyses, textbooks, or re-analyses. Conference abstracts were generally excluded, with exceptions made when information for a given metabolite/disease was scarce. “Records Excluded” were those identified as the wrong field of study, non-¹H MRS, non-spectroscopy MR methods, non-brain regions, or animal studies (for Concentration study only). “Reports Excluded” during the “Assessed for Eligibility” did not include metabolite concentrations nor relaxation values.

Figure 2:

Brain metabolite concentrations in younger (18–45 years, in blue) and older (>50 years, in white) healthy adults from studies that reported results as: (A) Molar, molal, and Institutional Units; (B) Creatine-referenced. An * indicates the use of a Fixed Effects Model rather than a Random Effects Model. A † indicates a combined effects model was not defined.

Figure 3:

The six most commonly investigated metabolite concentrations modeled in diseased populations. Data from metabolite and metabolite complexes are combined (e.g., Cr and tCr, Glu and Glx). An * by the group classification indicates the use of a Fixed Effects Model rather than a Random Effects Model. A † indicates a combined effects model was not defined. PC = perinatal complications; Aut = autism; ADHD = attention-deficit/hyper activity; MCI = mild cognitive impairment; E4 apolipoprotein 4 carriers; Dem = dementia; Etrm = essential tremor; PD = Parkinson’s disease; MS = multiple sclerosis; Bip = bipolar; Pers = personality disorder; Psy = psychosis; Schz = schizophrenia; Adc = addiction; Depr = depression; OCD = obsessive compulsive disorder; PTSD = post-traumatic stress disorder; Fib = fibromyalgia; Mgrn = migraine; Pain = chronic pain; Canc = cancer; D1 = type 1 diabetes; TBI = traumatic brain injury; Str = stroke; Seiz = seizure disorder.

Figure 4:

Transverse relaxation time meta-analysis. Only results for NAA, Cho, and Cr are shown for ease of visualization, but a total of 629 values for 14 metabolites were included in the database and modeled. Metabolite, field strength, localization, T₂ filter, species, and tissue type were included as factors in the model. Database entries are sorted here by these factors in that order. Each study is represented by a square of size reflecting the modeling weight (based on the inverse of variance). The red line shows the model.