MRI BIOPHYSICS GROUP

bridging the micro-macro gap with diffusion MRI

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol.

TitleA resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol.
Publication TypeJournal Article
Year of Publication2018
AuthorsAdhikari, BM, Jahanshad, N, Shukla, D, Turner, J, Grotegerd, D, Dannlowski, U, Kugel, H, Engelen, J, Dietsche, B, Krug, A, Kircher, T, Fieremans, E, Veraart, J, Novikov, DS, Boedhoe, PSW, van der Werf, YD, van den Heuvel, OA, Ipser, J, Uhlmann, A, Stein, DJ, Dickie, E, Voineskos, AN, Malhotra, AK, Pizzagalli, F, Calhoun, VD, Waller, L, Veer, IM, Walter, H, Buchanan, RW, Glahn, DC, L Hong, E, Thompson, PM, Kochunov, P
JournalBrain Imaging Behav
Date PublishedSep
ISSN1931-7565 (Electronic); 1931-7557 (Linking)
KeywordsENIGMA EPI template, Large multi-site studies, Processing pipelines
Abstract

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

DOI10.1007/s11682-018-9941-x
PubMed ID30191514
Research Category: 

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