PT  - JOURNAL ARTICLE
AU  - Marco Palma
AU  - Shahin Tavakoli
AU  - Julia Brettschneider
AU  - Thomas E. Nichols
AU  - for the Alzheimer’s Disease Neuroimaging Initiative
TI  - Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression
AID  - 10.1101/853341
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 853341
4099  - http://biorxiv.org/content/early/2020/05/03/853341.short
4100  - http://biorxiv.org/content/early/2020/05/03/853341.full
AB  - Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised functional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject.Competing Interest StatementThe authors have declared no competing interest.