Harmonization of resting-state fMRI analytical steps within the ENIGMA Consortium
Poster No:
1853
Submission Type:
Abstract Submission
Authors:
Clara Moreau1, Lea Waller2, Sophia Thomopoulos3, Pierre Bellec4, Frank Hillary5, Ilya Veer6, Paul Thompson3
Institutions:
1University of Montreal, Montreal, Quebec, 2Charité - Universitätsmedizin Berlin, Berlin, Berlin, 3University of Southern California, Los Angeles, CA, 4University of Montreal, Montreal, QC, 5Pennsylvania State University, State College, PA, 6University of Amsterdam, Amsterdam, Netherlands
First Author:
Co-Author(s):
Introduction:
The ENIGMA (Enhancing Neuroimaging and Genetics through Meta-Analysis) Consortium has completed the largest structural neuroimaging studies of over 15 brain disorders, integrating data from 45 countries worldwide (Thompson et al., 2020). Beyond brain anatomy, resting-state functional MRI (rs-fMRI) is particularly appropriate for studying and comparing psychiatric disorders, as it eliminates the need to harmonize task-based fMRI paradigms, which are often tailored to specific ages or conditions. Despite its potential, the field has faced significant reproducibility challenges across studies, limiting the ability to draw reliable conclusions. These challenges are largely due to a lack of methodological harmonization (Botvinik-Nezer et al., 2020; Luppi et al., 2024). Even when using the same preprocessing pipeline (e.g., fMRIPrep) (Esteban et al., 2019; Waller et al., 2022), variations in parameters and settings are frequent, which significantly impact results. In this study, we investigated whether a consensus could be reached, and aimed to determine the specific steps for an international multiverse analysis of multiple brain disorders and conditions.
Methods:
We designed and conducted a survey targeting field experts and users from the ENIGMA Consortium (https://bit.ly/ENIGMA-survey). For pre-processing, participants were first asked: 'Which pre-processing steps are the most important to harmonize?'. Subsequently, each pre-processing step was assessed to determine whether it was essential, beneficial, or should never be done. We included a specific question on the type of denoising method that they would consider as valid (Wang et al., 2024): (1) 'simple' (in nilearn), (2) aCompCor, (3) ICA-AROMA, (4) aCompCor and ICA-AROMA together, (5) 'simple' with global signal regression (GSR), (6) ICA-AROMA and GSR combined (Figure 1). For post-processing, participants were asked to determine which derivative features should be extracted and harmonized across all ENIGMA working groups.
Results:
60 participants (28 experts, 32 users) completed the survey, representing 33 ENIGMA working groups. Participants considered confound removal methods, GSR and motion scrubbing to be the most critical steps to harmonize. There was a consensus that slice timing correction, temporal filtering (with a 128-second cutoff), spatial smoothing (FWHM 6 mm), and that susceptibility distortion correction (when field maps are available) should always be performed.
Opinions diverged regarding whether GSR should be done. While 8 experts / 4 users argued that GSR should never be applied, 14 experts / 7 users considered it to be beneficial, and 3 experts considered it as required. We concluded that GSR should be included in one multiverse pipeline. We observed disagreement among responders regarding the best confound removal methods, with the highest support going to 'simple' denoising and aCompCor (see Figure 1). Visual quality control with a framewise displacement threshold of 0.5 mm was considered necessary.
Features considered as the most essential to be extracted by every working group were connectomes derived from atlas-based connectivity. The following atlases were considered as reasonable choices: Schaefer 2018 400 parcels, Yeo 17 networks, FreeSurfer subcortical, and Buckner 2011 for cerebellum.
Guidelines for future ENIGMA rs-fMRI analyses were developed based on the survey results, incorporating the preferences of analysts conducting fMRI studies within the consortium and recommendations from independent experts. These guidelines are summarized in Figure 2.
Opinions diverged regarding whether GSR should be done. While 8 experts / 4 users argued that GSR should never be applied, 14 experts / 7 users considered it to be beneficial, and 3 experts considered it as required. We concluded that GSR should be included in one multiverse pipeline. We observed disagreement among responders regarding the best confound removal methods, with the highest support going to 'simple' denoising and aCompCor (see Figure 1). Visual quality control with a framewise displacement threshold of 0.5 mm was considered necessary.
Features considered as the most essential to be extracted by every working group were connectomes derived from atlas-based connectivity. The following atlases were considered as reasonable choices: Schaefer 2018 400 parcels, Yeo 17 networks, FreeSurfer subcortical, and Buckner 2011 for cerebellum.
Guidelines for future ENIGMA rs-fMRI analyses were developed based on the survey results, incorporating the preferences of analysts conducting fMRI studies within the consortium and recommendations from independent experts. These guidelines are summarized in Figure 2.
·Figure 1: Denoising fMRI methods considered as a valid choice by experts (in red) and users (in blue).
·Figure 2: Guidelines for future ENIGMA rs-fMRI analyses
Conclusions:
Standardizing and harmonizing methods for rs-fMRI analyses offers substantial benefits by ensuring that data and results are comparable across diverse study populations. This standardization facilitates the pooling of data for large-scale cross-disorder, transdiagnostic, and cross-modality studies, enabling the development of normative and lifespan models in the future.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroinformatics and Data Sharing:
Workflows 1
Keywords:
Data analysis
FUNCTIONAL MRI
Workflows
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
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Wang, H.-T., Meisler, S. L., Sharmarke, H., Clarke, N., Gensollen, N., Markiewicz, C. J., Paugam, F., Thirion, B., & Bellec, P. (2024). Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn. PLoS Computational Biology, 20(3), e1011942. https://doi.org/10.1371/journal.pcbi.1011942