Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study

Abstract

Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site’s MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys was higher than that of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.

Structural neuroimaging correlates of alcohol and cannabis use in adolescents and adults

“…Among adolescent populations, however, associations between WM measures and alcohol use are less conclusive. Greater number of lifetime drinks was associated with smaller subcortical WM volume [61], but another study using the same sample found no differences in WM integrity measures between adolescent drinkers and non- or low-drinkers [66]. Several cross-sectional studies of adolescent alcohol use and WM measures have found lower FA in corpus callosum, corona radiata, inferior and superior longitudinal fasciculi [14, 22–23, 67].…”

A review of diffusion MRI of typical white matter development from early childhood to young adulthood

“…Several studies have found no differences in age-related diffusion changes between males and females. [25][26][27]82 Some studies report higher FA and lower MD in girls, 62 and more protracted maturation in males, 77,79,86,87 overall suggesting a pattern of earlier diffusion changes in females. Clearly more investigation of sex differences in development rates is warranted; however, studies will need to have very large sample sizes to accurately model non-linear changes separately in males and females.…”

“…Findings for AD have been mixed, with some studies reporting little to no change, † others reporting slight decreases (see, e.g., Figure 3D), 25,79,84,87 and still others reporting increases of AD with age. 83,85 A rat model combining MRI and electron microscopy attributed increases of AD to reduced tortuosity (straightening) of axons, 151 which is known to occur during development 152 ; however, these changes are likely occurring at time points much earlier than the typical age range measured in these studies.…”

A multisample study of longitudinal changes in brain network architecture in 4–13‐year‐old children

“…Prior research showed that childhood is characterized by widespread changes in FA and white matter volume [Brouwer et al, 2012;Giedd et al, 2015;Hagmann et al, 2010;Muetzel et al 2015]. In addition, different maturation rates across various brain regions have been observed [Lebel et al, 2008;Tamnes et al, 2010;Brouwer et al, 2012;Simmonds et al, 2014;Krogsrud et al, 2016;Pohl et al, 2016]. However, it is currently not well understood if these white matter changes in childhood reflect substantial reorganization of network topology, or whether they merely contribute to a global increase in network efficiency.…”