An open science resource for establishing reliability and reproducibility in functional connectomics - PubMed (original) (raw)

doi: 10.1038/sdata.2014.49. eCollection 2014.

Jeffrey S Anderson 2, Pierre Bellec 3, Rasmus M Birn 4, Bharat B Biswal 5, Janusch Blautzik 6, John C S Breitner 7, Randy L Buckner 8, Vince D Calhoun 9, F Xavier Castellanos 10, Antao Chen 11, Bing Chen 12, Jiangtao Chen 11, Xu Chen 11, Stanley J Colcombe 13, William Courtney 9, R Cameron Craddock 14, Adriana Di Martino 15, Hao-Ming Dong 16, Xiaolan Fu 17, Qiyong Gong 18, Krzysztof J Gorgolewski 19, Ying Han 20, Ye He 16, Yong He 21, Erica Ho 14, Avram Holmes 22, Xiao-Hui Hou 16, Jeremy Huckins 23, Tianzi Jiang 24, Yi Jiang 25, William Kelley 23, Clare Kelly 15, Margaret King 9, Stephen M LaConte 26, Janet E Lainhart 4, Xu Lei 11, Hui-Jie Li 25, Kaiming Li 18, Kuncheng Li 27, Qixiang Lin 21, Dongqiang Liu 12, Jia Liu 21, Xun Liu 25, Yijun Liu 11, Guangming Lu 28, Jie Lu 27, Beatriz Luna 29, Jing Luo 30, Daniel Lurie 14, Ying Mao 31, Daniel S Margulies 19, Andrew R Mayer 9, Thomas Meindl 6, Mary E Meyerand 32, Weizhi Nan 16, Jared A Nielsen 2, David O'Connor 14, David Paulsen 29, Vivek Prabhakaran 33, Zhigang Qi 27, Jiang Qiu 11, Chunhong Shao 34, Zarrar Shehzad 14, Weijun Tang 35, Arno Villringer 36, Huiling Wang 37, Kai Wang 16, Dongtao Wei 11, Gao-Xia Wei 25, Xu-Chu Weng 12, Xuehai Wu 31, Ting Xu 38, Ning Yang 16, Zhi Yang 25, Yu-Feng Zang 12, Lei Zhang 16, Qinglin Zhang 11, Zhe Zhang 16, Zhiqiang Zhang 28, Ke Zhao 25, Zonglei Zhen 21, Yuan Zhou 25, Xing-Ting Zhu 16, Michael P Milham 14

Affiliations

An open science resource for establishing reliability and reproducibility in functional connectomics

Xi-Nian Zuo et al. Sci Data. 2014.

Abstract

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1

Figure 1. Summary map of brain coverage for rfMRI scans in CoRR (_N_=5,093).

The color indicates the coverage ratio of rfMRI scans.

Figure 2

Figure 2. Test-retest plots of in-scanner head motion during rfMRI.

Total 1019 subjects who have at least two rfMRI sessions are selected. The green line indicates the correlation between the two sessions within the lower motion datasets (mean FD<0.2 mm). The blue line indicates the correlation for the higher motion datasets (mean FD >0.2 mm).

Figure 3

Figure 3. Individual differences in fALFF and the temporal sampling rate (TR).

Median fALFF values across each individual whole brains are plotted against the corresponding TR for each site. Different colors indicate labels of different sites.

Figure 4

Figure 4. Test-retest plots of individual variation-related functional boundaries.

Detection of functional boundaries was achieved via examination of voxel-wise coefficients of variation (CV) for fALFF, PCC, ReHo and VMHC maps. For the purpose of visualization, coefficients of variation were rank-ordered, whereby the relative degree of variation across participants at a given voxel, rather than the actual value, was plotted to better contrast brain regions. Ranking coefficients of variation (R-CV) efficiently identified regions of greatest inter-individual variability, thus delineating putative functional boundaries.

References

Data Citations

    1. Liu X., Nan W. Z., Wang K. 2014. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative. http://dx.doi.org/10.15387/fcp_indi.corr.ipcas4 -DOI
    1. Zuo X. N. 2014. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative. http://dx.doi.org/10.15387/fcp_indi.corr.ipcas7 -DOI
    1. Castellanos F. X., Di Martino A., Kelly C. 2014. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative. http://dx.doi.org/10.15387/fcp_indi.corr.nyu1 -DOI
    1. Chen A. T., Lei X. 2014. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative. http://dx.doi.org/10.15387/fcp_indi.corr.swu3 -DOI
    1. Chen A. T., Chen J. T. 2014. Functional Connectomes Project International Neuroimaging Data-Sharing Initiative. http://dx.doi.org/10.15387/fcp_indi.corr.swu2 -DOI

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