NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction
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The availability of cloud computing services has enabled the widespread adoption of the “software as a service” (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named “NAPR” (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6–89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.
KeywordsCloud computing Morphometry Age prediction Software as a service
The NAPR project was supported by FACES (Finding a Cure for Epilepsy and Seizures) and Amazon Web Services Cloud Credits for Research.
- Cole J.H., Poudel R.P.K., Tsagkrasoulis D., Caan M.W.A., Steves C., Spector T.D., et al. (2017b) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.07.059
- Cole, J. H., Ritchie, S. J., Bastin, M. E., Valdes Hernandez, M. C., Munoz Maniega, S., Royle, N., et al. (2017c). Brain age predicts mortality. Mol Psychiatry. https://doi.org/10.1038/mp.2017.62.
- Cole J.H., Leech R., Sharp D.J., Alzheimer's Disease Neuroimaging Initiative (2015). Prediction of brain age suggests accelerated atrophy after traumatic brain injury, Annals of Neurology. 77(4):571–581. https://doi.org/10.1002/ana.24367.
- Gorgolewski, K., Burns, C., Madison, C., Clark, D., Halchenko, Y., Waskom, M., et al. (2011). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics., 5, 13. https://doi.org/10.3389/fninf.2011.00013.CrossRefPubMedPubMedCentralGoogle Scholar
- Gorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capot, M., Chakravarty, M. M., et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol, e1005209, 13.Google Scholar
- Karatzoglou A, Smola A, Hornik K, Zeileis A. (2004) Kernlab-an s4 package for kernel methods in R. Journal of Statistical Software, 11(9), 1–20Google Scholar
- Koutsouleris, N., Davatzikos, C., Borgwardt, S., Gaser, C., Bottlender, R., Frodl, T., et al. (2013). Accelerated brain aging in schizophrenia and beyond: A neuroanatomical marker of psychiatric disorders. Schizophr Bull. https://doi.org/10.1093/schbul/sbt142.
- Ooms, J. (2014) The opencpu system: Towards a universal interface for scientific computing through separation of concerns. eprint arXiv:1406.4806Google Scholar
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in python. J Mach Learn Res, 12, 2825–2830.Google Scholar
- Rasmussen, C.E., Williams C.K.I. (2005) Gaussian processes for machine learning (adaptive computation and machine learning). p. 266 London: The MIT Press.Google Scholar
- Savalia, N. K., Agres, P. F., Chan, M. Y., Feczko, E. J., Kennedy, K. M., & Wig, G. S. (2017). Motion-related artifacts in structural brain images revealed with independent estimates of in-scanner head motion. Hum Brain Mapp, 38, 472–492. https://doi.org/10.1002/hbm.23397.CrossRefPubMedGoogle Scholar
- Tipping, M. E. (2001). Sparse bayesian learning and the relevance vector machine. J Mach Learn Res, 1, 211–244.Google Scholar