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

Bhim M. Adhikari, Neda Jahanshad, Dinesh Shukla, Jessica Turner, Dominik Grotegerd, Udo Dannlowski, Harald Kugel, Jennifer Engelen, Bruno Dietsche, Axel Krug, Tilo Kircher, Els Fieremans, Jelle Veraart, Dmitry S. Novikov, Premika S.W. Boedhoe, Ysbrand D. van der Werf, Odile A. van den Heuvel, Jonathan Ipser, Anne Uhlmann, Dan J. SteinErin Dickie, Aristotle N. Voineskos, Anil K. Malhotra, Fabrizio Pizzagalli, Vince D. Calhoun, Lea Waller, Ilja M. Veer, Hernik Walter, Robert W. Buchanan, David C. Glahn, L. Elliot Hong, Paul M. Thompson, Peter Kochunov

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1453-1467
Number of pages15
JournalBrain Imaging and Behavior
Volume13
Issue number5
DOIs
StatePublished - Oct 1 2019
Externally publishedYes

Keywords

  • ENIGMA EPI template
  • Large multi-site studies
  • Processing pipelines

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Cognitive Neuroscience
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

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  • Cite this

    Adhikari, B. M., 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, D. S., Boedhoe, P. S. W., van der Werf, Y. D., van den Heuvel, O. A., Ipser, J., Uhlmann, A., ... Kochunov, P. (2019). A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. Brain Imaging and Behavior, 13(5), 1453-1467. https://doi.org/10.1007/s11682-018-9941-x