Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank.

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Bethlehem, Richard AI  ORCID logo
Whiteside, David J 
Swaddiwudhipong, Nol  ORCID logo
Rowe, James B 

BACKGROUND: Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease. METHODS: We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. RESULTS: We show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. CONCLUSIONS: This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Alzheimer’s Disease Neuroimaging Initiative
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Commun Med (Lond)
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Springer Science and Business Media LLC
Medical Research Council (G1100464)
Wellcome Trust (103838/Z/14/Z)
European Commission Horizon 2020 (H2020) Societal Challenges (848077)
National Institute for Health and Care Research (IS-BRC-1215-20014)
Cambridge University Hospitals NHS Foundation Trust (CUH) (146281)
Medical Research Council (MC_UU_00005/12)
W.D. Armstrong Trust Fund, University of Cambridge, UK. Cambridge Centre for Parkinson-plus NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). Medical Research Council (SUAG/051 R101400) Wellcome Trust (103838) EU GOD-DS21 scheme (Grant agreement No. 848077).