Heritability estimates on resting state fMRI data using the ENIGMA analysis pipeline
Bhim M. Adhikari, Neda Jahanshad, Dinesh Shukla, Dinesh Shukla,, Richard C. Reynolds, Robert W. Cox, Els Fieremans, Jelle Veraart, Dmitry S., Novikov, L. Elliot Hong, Paul M. Thompson, Peter Kochunov

TL;DR
This study introduces a harmonized multi-site rsfMRI analysis pipeline within the ENIGMA framework, demonstrating consistent heritability estimates of 20-40% across major functional networks in two large cohorts.
Contribution
The paper presents a new harmonized analysis pipeline for multi-site rsfMRI data that reliably estimates heritability of resting state networks across diverse datasets.
Findings
Heritability of rsfMRI measures ranges from 20-40%.
The pipeline produces consistent results across different cohorts.
Resting state networks show significant heritability.
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 accurate estimates of effect sizes and more reliable and reproducible outcomes. Such efforts require harmonized analyses protocols to consistently extract phenotypes. Even so, challenges include wide variability of fMRI 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…
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