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A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability

View ORCID ProfileGregory Kiar, Eric W. Bridgeford, William R. Gray Roncal, Consortium for Reliability and Reproducibility (CoRR), Vikram Chandrashekhar, Disa Mhembere, Sephira Ryman, Xi-Nian Zuo, View ORCID ProfileDaniel S. Margulies, View ORCID ProfileR. Cameron Craddock, Carey E. Priebe, Rex Jung, Vince D. Calhoun, Brian Caffo, Randal Burns, Michael P. Milham, Joshua T. Vogelstein
doi: https://doi.org/10.1101/188706
Gregory Kiar
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
2Department of Biomedical Engineering, McGill University, Baltimore, MD, USA.
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  • ORCID record for Gregory Kiar
Eric W. Bridgeford
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
3Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
5Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
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William R. Gray Roncal
4Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
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Vikram Chandrashekhar
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
3Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
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Disa Mhembere
5Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
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Sephira Ryman
6Department of Psychology, University of New Mexico, Albuquerque, NM, USA.
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Xi-Nian Zuo
7Department of Psychology, University of Chinese Academy of Sciences (CAS), Beijing, China.
8CAS Key Laboratory of Behavioral Science, Beijing, China.
9Research Center for Lifespan Development of Mind and Brain (CLIMB), CAS Institute of Psychology, Beijing, China.
10Magnetic Resonance Imaging Research Center (MRIRC), CAS Institute of Psychology, Beijing, China.
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Daniel S. Margulies
11Department of Human Cognitive and Brain Sciences, Max-Planck Institute, Munich, Germany.
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  • ORCID record for Daniel S. Margulies
R. Cameron Craddock
12Child Mind Institute, New York, NY, USA.
13Dell Medical School, University of Texas at Austin, Austin, TX, USA.
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Carey E. Priebe
3Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
14Department of Statistics, Johns Hopkins University, MD, USA.
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Rex Jung
6Department of Psychology, University of New Mexico, Albuquerque, NM, USA.
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Vince D. Calhoun
15Department of Biomedical Engineering, University of New Mexico, Albuquerque, NM, USA.
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Brian Caffo
16Department of Biostatistics, Johns Hopkins University, MD, USA.
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Randal Burns
5Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
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Michael P. Milham
9Research Center for Lifespan Development of Mind and Brain (CLIMB), CAS Institute of Psychology, Beijing, China.
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Joshua T. Vogelstein
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
3Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
5Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
9Research Center for Lifespan Development of Mind and Brain (CLIMB), CAS Institute of Psychology, Beijing, China.
17Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
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  • For correspondence: jovo@jhu.edu
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Abstract

Modern scientific discovery depends on collecting large heterogeneous datasets with many sources of variability, and applying domain-specific pipelines from which one can draw insight or clinical utility. For example, macroscale connectomics studies require complex pipelines to process raw functional or diffusion data and estimate connectomes. Individual studies tend to customize pipelines to their needs, raising concerns about their reproducibility, and adding to a longer list of factors that may differ across studies (including sampling, experimental design, and data acquisition protocols), resulting in failures to replicate. Mitigating these issues requires multi-study datasets and the development of pipelines that can be applied across them. We developed NeuroData’s MRI to Graphs (NDMG) pipeline using several functional and diffusion studies, including the Consortium for Reliability and Reproducibility, to estimate connectomes. Without any manual intervention or parameter tuning, NDMG ran on 25 different studies (≈ 6,000 scans) from 15 sites, with each scan resulting in a biologically plausible connectome (as assessed by multiple quality assurance metrics at each processing stage). For each study, the connectomes from NDMG are more similar within than across individuals, indicating that NDMG is preserving biological variability. Moreover, the connectomes exhibit near perfect consistency for certain connectional properties across every scan, individual, study, site, and modality; these include stronger ipsilateral than contralateral connections and stronger homotopic than heterotopic connections. Yet, the magnitude of the differences varied across individuals and studies—much more so when pooling data across sites, even after controlling for study, site, and basic demographic variables (i.e., age, sex, and ethnicity). This indicates that other experimental variables (possibly those not measured or reported) are contributing to this variability, which if not accounted for can limit the value of aggregate datasets, as well as expectations regarding the accuracy of findings and likelihood of replication. We, therefore, provide a set of principles to guide the development of pipelines capable of pooling data across studies while maintaining biological variability and minimizing measurement error. This open science approach provides us with an opportunity to understand and eventually mitigate spurious results for both past and future studies.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 24, 2018.
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A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability
Gregory Kiar, Eric W. Bridgeford, William R. Gray Roncal, Consortium for Reliability and Reproducibility (CoRR), Vikram Chandrashekhar, Disa Mhembere, Sephira Ryman, Xi-Nian Zuo, Daniel S. Margulies, R. Cameron Craddock, Carey E. Priebe, Rex Jung, Vince D. Calhoun, Brian Caffo, Randal Burns, Michael P. Milham, Joshua T. Vogelstein
bioRxiv 188706; doi: https://doi.org/10.1101/188706
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A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability
Gregory Kiar, Eric W. Bridgeford, William R. Gray Roncal, Consortium for Reliability and Reproducibility (CoRR), Vikram Chandrashekhar, Disa Mhembere, Sephira Ryman, Xi-Nian Zuo, Daniel S. Margulies, R. Cameron Craddock, Carey E. Priebe, Rex Jung, Vince D. Calhoun, Brian Caffo, Randal Burns, Michael P. Milham, Joshua T. Vogelstein
bioRxiv 188706; doi: https://doi.org/10.1101/188706

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