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Decentralized Brain Age Estimation Using MRI Data

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Abstract

Recent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for biological brain age estimation using support vector regression models and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that our decentralized brain age regression models can achieve similar performance compared to the models trained with all the data in one location.

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Data Availability

UPENN-PNC (Satterthwaite et al., 2014) and UKBiobank (Miller et al., 2016) are openly available MRI datasets which can be accessed upon request. Please contact the authors of Satterthwaite et al. (2014), Miller et al. (2016) for data.

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Acknowledgements

We sincerely thank Debbrata Kumar Saha and Biozid Bostami for their comments. We are also grateful to the funding institutions for their support (National Institutes of Health, National Institute on Drug Abuse and the National Institute of Mental Health).

Funding

This work was funded by the National Institutes of Health (R01DA040487), National Institute on Drug Abuse (R01DA049238) and the National Institute of Mental Health (R01MH121246)

Author information

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, Rajikha Raja, Bhaskar Ray, Sergey Plis, Jingyu Liu, Eric Verner & Vince D. Calhoun

  2. Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

    Rajikha Raja

  3. Department of Computer Science, Georgia State University, Atlanta, GA, USA

    Bhaskar Ray, Jingyu Liu & Vince D. Calhoun

  4. Princeton Neuroscience Institute, Princeton, NJ, USA

    Harshvardhan Gazula

  5. Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA

    Anand D. Sarwate

  6. Department of Psychology, Georgia State University, Atlanta, GA, USA

    Vince D. Calhoun

Authors
  1. Sunitha Basodi
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  2. Rajikha Raja
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  3. Bhaskar Ray
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  4. Harshvardhan Gazula
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  5. Anand D. Sarwate
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  6. Sergey Plis
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  7. Jingyu Liu
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  8. Eric Verner
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  9. Vince D. Calhoun
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Contributions

All the authors helped improve the manuscript. SB designed decentralized models for FNC features, performed the data analysis for all the models and wrote the initial manuscript. RR and HG designed the decentralized model for FreeSurfer and GM features. BR designed feature extraction strategies. AS and SP designed and provided insights into the decentralized regression models and helped in tuning their performances. JL provided guidance about feature extraction and decentralized model design. EV manages the COINSTAC project and helped write the paper. VDC supervised all the stages of the project and also funded the project.

Corresponding author

Correspondence to Sunitha Basodi.

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All the authors declare that they have no competing interests.

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Basodi, S., Raja, R., Ray, B. et al. Decentralized Brain Age Estimation Using MRI Data. Neuroinform 20, 981–990 (2022). https://doi.org/10.1007/s12021-022-09570-x

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