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DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders

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Abstract

There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or “Discover Confirm Independent Component Analysis”, a software package that implements the methodology in support of our hypothesis. It relies on a “discover-confirm” approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica.

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Acknowledgements

The authors would like to thank National Science Foundation - NSF (Grant # 0966278) for funding the author MA Syed during this study. The authors acknowledge financial support for data acquisition from the U.S. Army Medical Research and Materials Command (MRMC) (Grant # 00007218, PI: M. Dretsch). Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank the personnel at the TBI clinic and behavioral health clinic, Fort Benning, GA, USA and the US Army Aeromedical Research Laboratory, Fort Rucker, AL, USA, and most of all, the soldiers who participated in the study. The authors thank Julie Rodiek and Wayne Duggan for facilitating data acquisition and Adam Goodman and Thomas Daniel for assistance in data collection. The author MA Syed is not representing the Boeing Company through this article. Author Z Yang is funded by the National Science Foundation of China (81270023, PI: Z. Yang), Foundation of Beijing Key Laboratory of Mental Disorders (2014JSJB03, PI: Z. Yang), Beijing Nova Program for Science and Technology (XXJH2015B079, PI: Z. Yang), and The Outstanding Young Investigator Award of Institute of Psychology, Chinese Academy of Sciences (Y4CX062008, PI: Z. Yang). X Hu and G Deshpande are supported in part by NIH (DA033393) and NIH (R01EY025978), respectively.

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Authors and Affiliations

  1. AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr, Suite 266D, Auburn, AL, 36849, USA

    Mohammed A. Syed, D. Rangaprakash, Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  2. Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA

    Mohammed A. Syed

  3. The Boeing Company, Seattle, WA, USA

    Mohammed A. Syed

  4. Key Laboratory of Behavioral Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Zhi Yang

  5. Department of Radiology, Northwestern University, Chicago, IL, USA

    D. Rangaprakash

  6. Department of Bioengineering, University of California Riverside, Riverside, CA, USA

    Xiaoping Hu

  7. U.S. Army Aeromedical Research Laboratory, Fort Rucker, AL, USA

    Michael N. Dretsch

  8. US Army Medical Research Directorate-West, Joint Base Lewis-McCord, Tacoma, WA, USA

    Michael N. Dretsch

  9. Department of Psychology, Auburn University, Auburn, AL, USA

    Michael N. Dretsch, Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  10. Center for Neuroscience, Auburn University, Birmingham, AL, USA

    Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  11. Alabama Advanced Imaging Consortium, Birmingham, AL, USA

    Jeffrey S. Katz, Thomas S. Denney Jr & Gopikrishna Deshpande

  12. Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India

    Gopikrishna Deshpande

  13. Center for Health Ecology and Equity Research, Auburn University, Auburn, AL, USA

    Gopikrishna Deshpande

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  1. Mohammed A. Syed
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  2. Zhi Yang
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  3. D. Rangaprakash
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  4. Xiaoping Hu
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  8. Gopikrishna Deshpande
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Syed, M.A., Yang, Z., Rangaprakash, D. et al. DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders. Neuroinform 18, 87–107 (2020). https://doi.org/10.1007/s12021-019-09422-1

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