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Brain Imaging Behav. 2018 Sep 6. doi: 10.1007/s11682-018-9941-x. [Epub ahead of print]

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

Author information

1
Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA. badhikari@som.umaryland.edu.
2
Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA.
3
Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
4
Department of Psychology, Georgia State University, Atlanta, GA, USA.
5
Department of Psychiatry, University of Münster, Münster, Germany.
6
Department of Clinical Radiology, University of Münster, Münster, Germany.
7
Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany.
8
Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
9
Department of Psychiatry, Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, Netherlands.
10
Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.
11
Centre for Addiction and Mental Health, Toronto, ON, Canada.
12
Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.
13
Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
14
Department of Psychiatry, The Zucker Hillside Hospital, Glen Oaks, New York, NY, USA.
15
The Mind Research Network & The University of New Mexico, Albuquerque, NM, USA.
16
Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Matte, Berlin, Germany.
17
Department of Psychiatry, Yale University, School of Medicine, New Haven, CT, USA.

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.

KEYWORDS:

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

PMID:
30191514
DOI:
10.1007/s11682-018-9941-x

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