ECoRR: Enhanced Consortium for Reliability and Reproducibility
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1824
Submission Type:
Abstract Submission
Authors:
Chong-jing Luo1, Wei Luo2,3, Yinshan Wang1, Peng Gao1, Xi-Nian Zuo4
Institutions:
1State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University, Beijing, China, 2Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 3School of Education Sciences, Nanning Normal University, Nanning, China, 4State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author:
Chong-jing Luo
State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University
Beijing, China
Co-Author(s):
Wei Luo
Institute of Psychology, Chinese Academy of Sciences|School of Education Sciences, Nanning Normal University
Beijing, China|Nanning, China
Institute of Psychology, Chinese Academy of Sciences|School of Education Sciences, Nanning Normal University
Beijing, China|Nanning, China
Yinshan Wang
State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University
Beijing, China
Peng Gao
State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience andLearning, Beijing Normal University
Beijing, China
Xi-Nian Zuo
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Introduction:
Magnetic Resonance Imaging (MRI), with its noninvasiveness and high spatiotemporal resolution, has been widely employed in neuroscience research and clinical diagnosis. High test-retest reliability of MRI-based brain phenotypes is fundamental for ensuring the replicability and reproducibility of research findings and serves as a prerequisite for clinical utility. Under the impetus of large-scale data-sharing initiatives such as the Human Connectome Project (HCP, Van Essen et al., 2013), the Consortium for Reliability and Reproducibility (CoRR, Zuo et al., 2014; Zuo & Xing, 2014), the 1000 Functional Connectomes Project (FC1000, Biswal et al., 2010), and the Adolescent Brain Cognitive Development (ABCD, Jernigan et al., 2018) study, open data sharing and data-intensive, multicenter research have emerged as major trends in neuroimaging of the human brain. These developments not only lead to numerous significant scientific discoveries, but also enhance the credibility and reproducibility of research outcomes through cross-dataset validation. Building on the foundation of the CoRR project, we build enhanced efforts on Consortium for Reliability and Reproducibility (eCoRR) to support the thriving open-science community and advance psychometric research for human neuroscience.
Methods:
The eCoRR dataset consists of 23 repeated-measurement datasets, with the number of measurements ranging from 2 to 10 and the intervals between the initial scan ranging from 1 minute to 1,046 days, depending on the dataset, collected across 11 centers in China, the United States, and Europe. To ensure data consistency and comparability, all datasets underwent a uniform preprocessing pipeline within the Connectome Computation System (CCS, Xu et al., 2015; Xing et al., 2022) framework. Structural image preprocessing (Figure 1A) included: (1) denoising, (2) skull stripping, (3) cortical reconstruction using FreeSurfer (version 6.0), (4) registration of 2D surfaces and 3D volumes to standard space, and (5) transformation of 2D surfaces to the CIFTI grayordinates space. Functional image preprocessing (Figure 1B) included: (1) discarding unstable volumes, (2) despiking, (3) slice timing correction, (4) motion correction, (5) spatiotemporal normalization, (6) registration to structural images, (7) tissue segmentation, (8) regression of signals from white matter, cerebrospinal fluid, and global mean, (9) artifact removal using ICA-AROMA to mitigate the effects of field inhomogeneity, physiological rhythms (e.g., respiration and heartbeat), and head motion, (10) spatial smoothing, (11) band-pass filtering (0.01-0.1 Hz), (12) projection to FreeSurfer surface space and CIFTI grayordinates space (10k). To accommodate diverse research needs, we also provide versions of the dataset without global signal regression and without temporal filtering. Additionally, the eCoRR dataset includes manual quality control assessments and MRIQC-based quality control metrics (Esteban et al., 2017), enabling efficient evaluation and selection of high-quality data.
Results:
Excluding data that failed preprocessing, the final eCoRR dataset comprises 1,064 participants (536 females, aged 10–85 years; Figure 1C). The dataset includes 2,014 T1-weighted structural images and 2,566 resting-state functional images, along with corresponding demographic information. The majority of the processed data demonstrate high quality (Figure 1D: green = resting-state images, purple = T1-weighted images). For example, 98.3% of T1-weighted images have CJV values below 0.70, and 92.7% of resting-state images have FD_mean values below 0.25.
Conclusions:
To promote open data sharing, advance research on brain structure, function, and test-retest reliability, and provide a robust data foundation for the continued development of cognitive neuroscience research and clinical applications, eCoRR will make all the processed data openly accessible through the Science Data Bank (https://doi.org/10.57760/sciencedb.o00133.00019).
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Open Data
STRUCTURAL MRI
Other - Test-retest Reliability
1|2Indicates the priority used for review
·Figure 1. Enhanced Consortium for Reliability and Reproducibility (eCoRR)
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS ONE, 12(9), e0184661. https://doi.org/10.1371/journal.pone.0184661
3. Jernigan, T. L., Brown, S. A., & Dowling, G. J. (2018). The Adolescent Brain Cognitive Development Study. Journal of Research on Adolescence, 28(1), 154–156. https://doi.org/10.1111/jora.12374
4. Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
5. Xing, X.-X., Xu, T., Jiang, C., Wang, Y.-S., & Zuo, X.-N. (2022). Connectome Computation System: 2015–2021 updates. Science Bulletin, 67(5), 448–451. 2023-06-02. https://doi.org/10.1016/j.scib.2021.11.021
6. Xu, T., Yang, Z., Jiang, L., Xing, X.-X., & Zuo, X.-N. (2015). A Connectome Computation System for discovery science of brain. Science Bulletin, 60(1), 86–95. https://doi.org/10.1007/s11434-014-0698-3
7. Zuo, X.-N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C. S., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., Chen, A., Chen, B., Chen, J., Chen, X., Colcombe, S. J., Courtney, W., Craddock, R. C., Di Martino, A., Dong, H.-M., … Milham, M. P. (2017). An open science resource for establishing reliability and reproducibility in functional connectomics. Scientific Data, 1(1). https://doi.org/10.1038/sdata.2014.49