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

By Xi-Nian Zuo, Jeffrey S. Anderson, Pierre C. Bellec, Rasmus M. Birn, Bharat B. Biswal, J. Blautzik, J. Breitner, Randy L. Buckner, Vince D. Calhoun, F. X. Castellanos and 75 more. Scientific Data, 2014

473 citations recorded by the source corpus.

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. Design Type(s) Test-retest Reliability Measurement Type(s) nuclear magnetic resonance assay Technology Type(s) functional MRI scanner Factor Type(s) protocol Sample Characteristic(s) Homo sapiens • brain Design Type(s) Test-retest Reliability Measurement Type(s) nuclear magnetic resonance assay Technology Type(s) functional MRI scanner Factor Type(s) protocol Sample Characteristic(s) Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/16c30c6449182f6ad1235a58321d4bc4c297fbf6

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Citations in the corpus

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, methodology): “Given the increasing availability of multi-session rs-fMRI from many different research groups (Zuo et al., 2014; Holmes et al., 2015; Poldrack et al., 2015; Filevich et al., 2017; Gordon et al., 2017c), it might be possible to add another layer to the hierarchical model to account for inter-site va”