
School of Medicine Publications and Presentations
Document Type
Conference Proceeding
Publication Date
1-1-2018
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.
Recommended Citation
Adhikari, B. M., Jahanshad, N., Shukla, D., Glahn, D. C., Blangero, J., Reynolds, R. C., Cox, R. W., Fieremans, E., Veraart, J., Novikov, D. S., Nichols, T. E., Hong, L. E., Thompson, P. M., & Kochunov, P. (2018). Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 23, 307–318. http://doi.org/10.1142/9789813235533_0029
First Page
308
Last Page
318
Publication Title
Pacific Symposium on Biocomputing
DOI
10.1142/9789813235533_0029
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the CreativeCommons Attribution Non-Commercial (CC BY-NC) 4.0 License.