Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020). Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access, 8, 140699–140725.

Article  PubMed  PubMed Central  Google Scholar 

Basodi, S., Raja, R., Ray, B., Gazula, H., Sarwate, A. D., Plis, S., Liu, J., Verner, E., & Calhoun, V. D. (2022). Decentralized brain age estimation using mri data. Neuroinformatics, pages 1–10.

Bearden, C. E., & Thompson, P. M. (2017). Emerging global initiatives in neurogenetics: the enhancing neuroimaging genetics through meta-analysis (enigma) consortium. Neuron, 94(2), 232–236.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Beckmann, C., Jenkinson, M., & Smith, S. (2003a). General multilevel linear modeling for group analysis in fmri. NeuroImage, 20, 1052–63.

Article  PubMed  Google Scholar 

Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003b). General multilevel linear modeling for group analysis in fmri. Neuroimage, 20(2), 1052–1063.

Article  PubMed  Google Scholar 

Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R. (2013). Statistical analysis of longitudinal neuroimage data with linear mixed effects models. NeuroImage, 66, 249–260.

Article  PubMed  Google Scholar 

Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., et al. (2018). The adolescent brain cognitive development (abcd) study: imaging acquisition across 21 sites. Developmental cognitive neuroscience, 32, 43–54.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen, G., Saad, Z., Britton, J., Pine, D., & Cox, R. (2013a). Linear mixed-effects modeling approach to fmri group analysis. NeuroImage, 73.

Chen, G., Saad, Z. S., Britton, J. C., Pine, D. S., & Cox, R. W. (2013b). Linear mixed-effects modeling approach to fmri group analysis. Neuroimage, 73, 176–190.

Article  PubMed  Google Scholar 

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.

Fischl, B. (2012). Freesurfer. Neuroimage, 62(2), 774–781.

Article  PubMed  Google Scholar 

Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Group, B. D. C., et al. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313–327.

Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102.

Article  Google Scholar 

Friston, K., Stephan, K., Lund, T., Morcom, A., & Kiebel, S. (2005). Mixed-effects and fmri studies. NeuroImage, 24, 244–52.

Article  CAS  PubMed  Google Scholar 

Friston, K. J., Stephan, K. E., Lund, T. E., Morcom, A., & Kiebel, S. (2005). Mixed-effects and fmri studies. Neuroimage, 24(1), 244–252.

Article  CAS  PubMed  Google Scholar 

Gazula, H., Holla, B., Zhang, Z., Xu, J., Verner, E., Kelly, R., Schumann, G., & Calhoun, V. D. (2019). Decentralized multi-site vbm analysis during adolescence shows structural changes linked to age, body mass index, and smoking: A coinstac analysis. bioRxiv, page 846386.

Gollub, R. L., Shoemaker, J. M., King, M. D., White, T., Ehrlich, S., Sponheim, S. R., Clark, V. P., Turner, J. A., Mueller, B. A., Magnotta, V., et al. (2013). The mcic collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics, 11(3), 367–388.

Article  PubMed  PubMed Central  Google Scholar 

Jennrich, R. I., & Schluchter, M. D. (1986). Unbalanced repeated-measures models with structured covariance matrices. Biometrics, 42(4), 805–820.

Article  MathSciNet  CAS  PubMed  Google Scholar 

Koerner, T. K., & Zhang, Y. (2017). Application of linear mixed-effects models in human neuroscience research: a comparison with pearson correlation in two auditory electrophysiology studies. Brain sciences, 7(3), 26.

Article  PubMed  PubMed Central  Google Scholar 

Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, pages 963–974.

Lange, N. (2003). What can modern statistics offer imaging neuroscience? Statistical methods in medical research, 12(5), 447–469.

Article  MathSciNet  PubMed  Google Scholar 

Larobina, M., & Murino, L. (2014). Medical image file formats. Journal of digital imaging, 27(2), 200–206.

Article  PubMed  Google Scholar 

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.

Article  CAS  Google Scholar 

Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C. (2016). The first step for neuroimaging data analysis: Dicom to nifti conversion. Journal of neuroscience methods, 264, 47–56.

Article  PubMed  Google Scholar 

Lindquist, M. A., Spicer, J., Asllani, I., & Wager, T. D. (2012). Estimating and testing variance components in a multi-level glm. NeuroImage, 59(1), 490–501.

Article  PubMed  Google Scholar 

Lindstrom, M. J., & Bates, D. M. (1988). Newton-raphson and em algorithms for linear mixed-effects models for repeated-measures data. Journal of the American Statistical Association, 83(404), 1014–1022.

MathSciNet  Google Scholar 

Madhyastha, T., Peverill, M., Koh, N., McCabe, C., Flournoy, J., Mills, K., King, K., Pfeifer, J., & McLaughlin, K. A. (2018). Current methods and limitations for longitudinal fmri analysis across development. Developmental Cognitive Neuroscience, 33:118 – 128. Methodological Challenges in Developmental Neuroimaging: Contemporary Approaches and Solutions.

Maullin-Sapey, T., & Nichols, T. (2022). Blmm: Parallelised computing for big linear mixed models. bioRxiv.

Maullin-Sapey, T., & Nichols, T. E. (2021). Fisher scoring for crossed factor linear mixed models. Statistics and computing, 31(5), 1–25.

Article  MathSciNet  Google Scholar 

Ming, J., Verner, E., Sarwate, A., Kelly, R., Reed, C., Kahleck, T., Silva, R., Panta, S., Turner, J., Plis, S., et al. (2017). Coinstac: Decentralizing the future of brain imaging analysis. F1000Research, 6.

Mumford, J. A., & Nichols, T. (2006). Modeling and inference of multisubject fmri data. IEEE Engineering in Medicine and Biology Magazine, 25(2), 42–51.

Article  PubMed  Google Scholar 

Mumford, J. A., & Poldrack, R. A. (2007). Modeling group fmri data. Social cognitive and affective neuroscience, 2(3), 251–257.

Article  PubMed  PubMed Central  Google Scholar 

Pinheiro, J., & Bates, D. (2006). Mixed-effects models in S and S-PLUS. Springer science & business media.

Plis, S. M., Sarwate, A. D., Wood, D., Dieringer, C., Landis, D., Reed, C., Panta, S. R., Turner, J. A., Shoemaker, J. M., Carter, K. W., et al. (2016). Coinstac: a privacy enabled model and prototype for leveraging and processing decentralized brain imaging data. Frontiers in neuroscience, 10, 365.

Article  PubMed  PubMed Central  Google Scholar 

Rootes-Murdy, K., Gazula, H., Verner, E., Kelly, R., DeRamus, T., Plis, S., Sarwate, A., Turner, J., & Calhoun, V. (2022). Federated analysis of neuroimaging data: A review of the field. Neuroinformatics, 20(2), 377–390.

Article  PubMed  Google Scholar 

Sarwate, A. D., Plis, S. M., Turner, J. A., Arbabshirani, M. R., & Calhoun, V. D. (2014). Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation. Frontiers in neuroinformatics, 8, 35.

Article  PubMed  PubMed Central  Google Scholar 

Senanayake, N., Podschwadt, R., Takabi, D., Calhoun, V. D., & Plis, S. M. (2022). Neurocrypt: Machine learning over encrypted distributed neuroimaging data. Neuroinformatics, 20(1), 91–108.

Article  PubMed  Google Scholar 

Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015). Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine, 12(3), e1001779.

Article  PubMed  PubMed Central  Google Scholar 

Szucs, D., & Ioannidis, J. P. (2020). Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals. NeuroImage, 221, 117164.

Article  PubMed  Google Scholar 

White, T., Blok, E., & Calhoun, V. D. (2020). Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Human Brain Mapping.

Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004). Multilevel linear modelling for fmri group analysis using bayesian inference. NeuroImage, 21(4), 1732–1747.

Article  PubMed  Google Scholar 

Yu, Z., Guindani, M., Grieco, S. F., Chen, L., Holmes, T. C., & Xu, X. (2021). Beyond t test and anova: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research. Neuron.