Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R₁ and R₂* in healthy young adults

Abstract

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R 1 ), and effective transverse relaxation rate (R 2 *). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R 1 , and R 2 * differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R 1 and R 2 * showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R 1 and R 2 * might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.

Grey-matter structure in cortical and limbic regions correlates with general cognitive ability in old age

“…Neuroimaging techniques such as T1-weighted structural imaging and diffusion tensor imaging (DTI) approximate macrostructural properties of brain tissues at various levels of granularity. Despite recent advances [51] , [52] , the mapping of these properties onto physiologically interpretable constructs remains a challenge. Recently, attempts have been made to triangulate the degree of grey-matter structural integrity by combining different imaging modalities into a latent construct [20] , [21] .…”