Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

Abstract

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Reproducibility of quantitative structural and physiological MRI measurements

“…However, these conditions are unlikely to be maintained throughout the life of longer longitudinal or cross‐sectional studies where scanner upgrades, significant hardware changes such as changes head coil, and other methodological changes may be expected. To address these aspects of longitudinal studies, our group and others used two strategies to accommodate for methodological changes: collections of calibration data and use of meta‐ and mega‐analyses (Jahanshad et al., 2013; Kochunov et al., 2015; McGuire et al., 2014a). In the first approach, calibration data are collected before and after change to derive cross‐calibration parameters.…”

“…Alternatively, cross‐sectional and longitudinal studies with short interimaging periods can use statistical aggregation approaches that treat samples collected on different hardware as independent datasets. ENIGMA consortium has demonstrated the utility in meta‐ and mega‐analysis of quantitative neuroimaging data (Jahanshad et al., 2013; Kochunov et al., 2015). …”

“…Briefly, DTI data were collected using a single‐shot echo‐planar, single refocusing spin‐echo, T2‐weighted sequence with a spatial resolution of 1.7 × 1.7 × 3.0 mm with sequence parameters of TE/TR = 87/8,000 ms, field of view (FOV) = 200 mm, axial slice orientation with 50 slices and no gaps, 64 isotropically distributed diffusion‐weighted directions, two diffusion weighting values ( b  =   0 and 700 s/mm 2 ), and five b  =   0 images. HARDI data for both groups were processed using the ENIGMA‐DTI (http://enigma.ini.usc.edu/protocols/dti-protocols/) pipeline (Jahanshad et al., 2013). ENIGMA‐DTI analysis pipeline is based on the tract‐based spatial statistics (TBSS) method, distributed as a part of FSL package (Smith et al., 2006).…”

What is a representative brain? Neuroscience meets population science

“…Substantially more work is needed to broaden this understanding and to more fully integrate what is known about large-scale social phenomena with the individual level processes that yield these largerscale phenomena. (68,69), scanning larger and larger samples of individuals (6,70,71), using data sharing and open access data (68,(72)(73)(74), consortium models (75)(76)(77), and neuroimaging meta-analysis (78,79). These models emphasize that neuroimaging approaches can be done on a larger scale and across research teams.…”

Copy number deletion burden is associated with cognitive, structural, and resting-state network differences in patients with schizophrenia

“…Fractional anisotropy (FA) differences are heritable [20] and mutations, especially rare deletions, may influence the connectivity between distinct brain regions rendering the brain less efficient, resulting in risk for psychosis.…”