NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 16; no. 1; pp. 43 - 49
• Main Authors: Pardoe, Heath R., Kuzniecky, Ruben
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2018; Springer Nature B.V
• Subjects: Adolescent; Adult; Age; Aged; Aged, 80 and over; Aging - pathology; Aging - physiology; Anatomy; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Brain architecture; Brain mapping; Cerebral Cortex - cytology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiology; Child; Cloud Computing - trends; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer programs; Cortex; Databases, Factual - trends; Forecasting; Humans; Internet; Learning algorithms; Magnetic resonance imaging; Magnetic Resonance Imaging - trends; Medical imaging; Middle Aged; Neuroimaging; Neurology; Neurosciences; Software; Software Original Article; Software services; Young Adult; Cloud computing; Age prediction; Morphometry; Software as a service
• ISSN: 1539-2791; 1559-0089; 1559-0089
• DOI: 10.1007/s12021-017-9346-9

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.