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Decentralized Mixed Effects Modeling in COINSTAC

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Abstract

Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborative projects become commonplace in neuroimaging, data must increasingly be stored and analyzed from different locations. In such settings, substantial overhead can occur in terms of data transfer and coordination between participating research groups. In some cases, data cannot be pooled together due to privacy or regulatory concerns. In this work, we propose a decentralized LME model to perform a large-scale analysis of data from different collaborations without data pooling. This method is efficient as it overcomes the hurdles of data sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results highlight gray matter reductions in the temporal lobe/insula and medial frontal regions in schizophrenia, consistent with prior studies. Our analysis also demonstrates that decentralized LME models achieve similar performance compared to the models trained with all the data in one location. We also implement the decentralized LME approach in COINSTAC, an open source, decentralized platform for federating neuroimaging analysis, providing an easy to use tool for dissemination to the neuroimaging community.

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Acknowledgements

This work was funded by NIH R01DA040487 and NSF 2112455.

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Authors and Affiliations

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

    Sunitha Basodi, Javier Tomas Romero, Sandeep Panta & Vince D. Calhoun

  2. St. Jude Children’s Research Hospital, Memphis, TN, USA

    Rajikha Raja

  3. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Harshvardhan Gazula

  4. Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Thomas Maullin-Sapey & Thomas E. Nichols

  5. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK

    Thomas E. Nichols

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  1. Sunitha Basodi
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  2. Rajikha Raja
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  3. Harshvardhan Gazula
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  4. Javier Tomas Romero
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  7. Thomas E. Nichols
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  8. Vince D. Calhoun
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Contributions

All the authors helped improve the manuscript. SB designed decentralized models, performed the data analysis for all the models and wrote the initial manuscript. RR and HG designed and provided insights into the decentralized regression models. SP manages the COINSTAC project. JTR and SP provided guidance about implemention of decentralized algorithms in COINSTAC. TMS and TEN have helped in integrating BLMM into COINSTAC's decentralized LME modeling and helped in revising the manuscript. VDC supervised all the stages of the project, helped in revising the mauscript and also funded the project

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Correspondence to Vince D. Calhoun.

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Basodi, S., Raja, R., Gazula, H. et al. Decentralized Mixed Effects Modeling in COINSTAC. Neuroinform 22, 163–175 (2024). https://doi.org/10.1007/s12021-024-09657-7

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