Bhim M. Adhikari1, Neda Jahanshad2, Dinesh Shukla1, David C. Glahn3, John Blangero4, Richard C. Reynolds5, Robert W. Cox5, Els Fieremans6, Jelle Veraart6, Dmitry S. Novikov6, Thomas E. Nichols7, L. Elliot Hong1, Paul M. Thompson2, Peter Kochunov1
1Maryland Psychiatry Research Center, Department of Psychiatry, University of Maryland School ofMedicine
2Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC
3Department of Psychiatry, Yale University, School of Medicine
4Genomics Computing Center, University of Texas at Rio Grande Valley
5National Institute of Mental Health
6Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine
7Department of Statistics, University of Warwick
Email: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, john.blangero@UTRGV.edu, email@example.com, firstname.lastname@example.org, email@example.com, Jelle.Veraart@nyumc.org, Dmitry.Novikov@nyumc.org, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com
Pacific Symposium on Biocomputing 23:307-318(2018)
© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
Big data initiatives such as the Enhancing NeuroImaging Genetics through MetaAnalysis 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 modelfree 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 seedbased 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.