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 mm ). 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 mm ). 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 (h = 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.

Functional network connectivity impairments and core cognitive deficits in schizophrenia

“…ENIGMA rsfMRI workflow was developed to alleviate potential pitfalls such as variances in the quality of T1w data and registration biases that are common for data collected using different scanners and protocols. It uses the Marchenko‐Pastur Principal Component Analysis (MPPCA) denoising to improve SNR/tSNR properties of time series data and rsFC measurements were shown to be consistently heritable across multiple cohorts (Adhikari, et al, ).…”

“…Resting‐state network templates were defined based on the probabilistic ROIs from the analysis of the BrainMap activation database and resting‐state fMRI dataset (Smith et al, ). We defined the binary masks of the resting state template regions from the auditory network (AN), AttN, default mode network (DMN), executive control network (ECN), frontoparietal network (FPN), salience network (SN), sensorimotor network (SMN), and visual network (VN) (Figure S2; Adhikari et al, ). Resting‐state FC (rsFC) values were extracted from these template ROIs using seed‐based and dual regression analysis approaches (more details are provided in the Supporting Information) and the subsequent analysis was performed using these measures (as detailed in the Supporting Information).…”

Effects of ketamine and midazolam on resting state connectivity and comparison with ENIGMA connectivity deficit patterns in schizophrenia

“…Resting‐state network templates were defined based on the probabilistic regions of interest (ROIs) from an independent components analysis of the BrainMap activation database and resting‐state fMRI dataset (Smith et al, ). Binary masks of the resting state template regions were defined for the auditory network (AN), attention network (AttN), default mode network (DMN), executive‐control network (ECN), frontoparietal network (FPN), salience network (SN), sensorimotor network (SMN), and visual network (VN; Figure S2; Adhikari et al, ). Resting‐state functional connectivity (rsFC) values between functional connections were extracted from these template ROIs using seed‐based and dual regression analysis approaches (as detailed in the Supporting Information) and the subsequent analysis was performed using these measures.…”

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

“…ENIGMA's future developments will include the coordinated analyses of new data modalities (such as resting state and task-related functional MRI [160][161][162][163] , as well as geostatistical and mobile sensor data), and deeper or more refined analyses of current imaging modalities. Diffusion MRI, in particular, is moving towards multi-shell protocols that can better differentiate cellular and microstructural sources of variance that may explain patterns observed with DTI 17 .…”