Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline

Bhim M. Adhikari, Neda Jahanshad, Dinesh Shukla, David C. Glahn, John Blangero, Richard C. Reynolds, Robert W. Cox, Els Fieremans, Jelle Veraart, Dmitry S. Novikov, Thomas E. Nichols, L. Elliot Hong, Paul M. Thompson, Peter Kochunov

    Research output: Contribution to journalConference articlepeer-review

    13 Scopus citations

    Abstract

    Big data initiatives such as the Enhancing NeuroImaging Genetics through Meta-Analysis consortium (ENIGMA), combine data collected by independent studies worldwide to achieve more generalizable estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized image analyses protocols to extract phenotypes consistently. This harmonization is particularly challenging for resting state fMRI due to the wide variability of acquisition protocols and scanner platforms; this leads to site-to-site variance in quality, resolution and temporal signal-to-noise ratio (tSNR). An effective harmonization should provide optimal measures for data of different qualities. We developed a multi-site rsfMRI analysis pipeline to allow research groups around the world to process rsfMRI scans in a harmonized way, to extract consistent and quantitative measurements of connectivity and to perform coordinated statistical tests. We used the single-modality ENIGMA rsfMRI preprocessing pipeline based on model-free Marchenko-Pastur PCA based denoising to verify and replicate resting state network heritability estimates. We analyzed two independent cohorts, GOBS (Genetics of Brain Structure) and HCP (the Human Connectome Project), which collected data using conventional and connectomics oriented fMRI protocols, respectively. We used seed-based connectivity and dual-regression approaches to show that the rsfMRI signal is consistently heritable across twenty major functional network measures. Heritability values of 20-40% were observed across both cohorts.

    Original languageEnglish (US)
    Pages (from-to)308-318
    Number of pages11
    JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
    Volume0
    Issue number212669
    DOIs
    StatePublished - 2018
    Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
    Duration: Jan 3 2018Jan 7 2018

    Keywords

    • Functional connectivity
    • Heritable
    • Introduction
    • Seed-based connectivity

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Computational Theory and Mathematics

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