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Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis

Neuroinformatics volume 19, pages 553–566 (2021)Cite this article

Abstract

There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a ‘default mode network’ pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a “Large-N” study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.

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Data Availability

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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Acknowledgments

This work was funded by the National Institutes of Health (grants: R01EB005846, 1R01DA040487) and the National Science Foundation (grants: 1539067, 1631819 and CCF-1909468). The study was supported in part by grant LSHM-CT-2007-037286 from the European Union–funded FP6 Integrated Project IMAGEN; the European Research Council Advanced Grant STRATIFY 695313 from the Horizon 2020; grant PR-ST-0416-10004 from the European Research Area Network on Illicit Drugs; grant MR/N027558/1 from BRIDGET (Brain Imaging Cognition Dementia and Next Generation Genomics); the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01), the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). cVEDA is jointly funded by the Indian Council for Medical Research (ICMR/MRC/3/M/2015-NCD-I) and the Newton Grant from the Medical Research Council(MR/N000390/1), United Kingdom. One of the co-authors, BH, was supported by Department of Biotechnology, Government of India grant (BT/PR17316/MED/31/326/2015) for “Accelerating program for discovery in brain disorders using stem cells”. Last but not least, we would like to acknowledge Matt Hickmann (University of Bristol), Mireille B. Toledano (Imperial College London) and Sylvane Desrivieres (King’s College London) for their important contributions to cVEDA.

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    Authors and Affiliations

    1. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA

      Harshvardhan Gazula, Eric Verner, Ross Kelly, Sergey M. Plis, Jessica A. Turner & Vince D. Calhoun

    2. Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India

      Bharath Holla, Sanjeev Jain, Pratima Murthy & Vivek Benega

    3. Department of Integrative Medicine, NIMHANS, Bengaluru, India

      Bharath Holla & Gunter Schumann

    4. Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King’s College London, London, SE5 8AF, UK

      Zuo Zhang & Jiayuan Xu

    5. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, People’s Republic of China

      Jiayuan Xu

    6. Department of Neuroimaging and Interventional Radiology NIMHANS, Bengaluru, India

      Rose Dawn Bharath

    7. Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, UK

      Gareth J. Barker

    8. Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India

      Debasish Basu

    9. ICMR-Regional Occupational Health Centre, Kolkata, India

      Amit Chakrabarti

    10. Rural Health Centre, Rishi Valley, India

      Kartik Kalyanram

    11. Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India

      Kalyanaraman Kumaran

    12. Regional Institute of Medical Sciences, Imphal, Manipur, India

      Lenin Singh

    13. Division of Nutrition, St. John’s Research Institute, Bengaluru, India

      Rebecca Kuriyan

    14. Centre for Addiction Medicine, NIMHANS, Bengaluru, India

      Pratima Murthy & Vivek Benega

    15. Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA

      Anand D. Sarwate

    16. PONS Research Group, Department of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin, Germany

      Gunter Schumann

    17. Leibniz Institute for Neurobiology, Magdeburg, Germany

      Gunter Schumann

    18. Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, People’s Republic of China

      Gunter Schumann

    Authors
    1. Harshvardhan Gazula
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    Contributions

    HG implemented the decentralized regression algorithm and led the manuscript writing. BH contributed data and was instrumental in writing up the results and discussion sections. ZZ contributed data to the study as well as contributed to some parts of the writing. EV managed the COINSTAC project, coordinated the analysis, and edited the paper. RK was the lead software developer for the COINSTAC platform. GS is a co-investigator and has been instrumental in facilitating this multi-site study. VC led the team, formed the vision, and helped interpret the results. All others who were not mentioned here are part of either the cVEDA or IMAGEN consortia.

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    Correspondence to Harshvardhan Gazula, Bharath Holla or Vince D. Calhoun.

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    The authors declare no conflict of interest.

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    Information Sharing Statement

    More specific details about accessing the datasets used in the study can be found at Zhang et al. (2020) for cVEDA and Schumann et al. (2010) for IMAGEN. The COINSTAC software can be accessed at https://github.com/trendscenter/coinstac.

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    H. Gazula, Bharath Holla and V. Calhoun all contributed equally to this work

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    Gazula, H., Holla, B., Zhang, Z. et al. Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis. Neuroinform 19, 553–566 (2021). https://doi.org/10.1007/s12021-020-09502-7

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    Keywords

    • COINSTAC
    • Decentralized voxel-based morphometry
    • Adolescence