Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline

Abstract

We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the genetics of brain structure (GOBS) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age = 47.9 ± 13.2 years) from 29 extended pedigrees (average family size = 9 people; range 5-32). The GOBS rsfMRI data was collected using a 7.5-min acquisition sequence (spatial resolution = 1.72 × 1.72 × 3 mm³ ). The HCP cohort consisted of 518 twins and family members (240M/278F; average age = 28.7 ± 3.7 years). rsfMRI data was collected using 28.8-min sequence (spatial resolution = 2 × 2 × 2 mm³ ). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. We observed significant heritability (h² = 0.2-0.4, p < .05) for functional connections from seven networks across both cohorts, with a significant positive correlation between heritability estimates across two cohorts. The similarity in heritability estimates for resting state connectivity measurements suggests that the additive genetic contribution to functional connectivity is robustly detectable across populations and imaging acquisition parameters. The overarching genetic influence, and means to consistently detect it, provides an opportunity to define a common genetic search space for future gene discovery studies.

Keywords: functional connectivity; heritable; seed-based connectivity.

Figure 1

Flowchart of ENIGMA rsfMRI analysis pipeline

Figure 2

Resting state network template ROIs based on the BrainMap activation database (Smith et al., 2009). Here, L = left, R = right, in (a) a₁/a₂ = left/right primary and association auditory cortices, in (b) r₁ = posterior cingulate/precuneus, r₂ = bilateral temporal–parietal regions and, r₃ = ventromedial frontal cortex, in (c) f₁/f₂ = left/right frontal area and p₁/p₂ = left/right parietal area, in (d) m₁/m₃ = left/right motor area and m₂ = supplementary motor area, in (e) v₁ = medial visual areas, v₂ = occipital visual areas, and v₃ = lateral visual areas, in (f) r₁ = anterior cingulate cortex and r₂ = bilateral medial frontal gyrus, in (g) r₁ = anterior cingulate cortex and r₂/r₃ = left/right insula, in (h) f₁/f₂ = left/right middle frontal gyrus and p₁/p₂ = left/right superior parietal lobule [Color figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

Heritability estimates for functional connectivity measured in the GOBS are presented as a scatter versus heritability estimates calculated in the HCP cohorts using seed‐based analysis approach (a) and dual regression analysis approach (b). The line represents the result of the linear correlation between two cohorts that reported a positive and significant correlation [r = .378, p = .023 for seed‐based approach (a) and r = .497, p = .002 for dual regression approach (b)]