Palma, Marco;
Tavakoli, Shahin;
Brettschneider, Julia;
Nichols, Thomas E;
(2020)
Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression.
NeuroImage
, 219
, Article 116938. 10.1016/j.neuroimage.2020.116938.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2020.116938 |
| Publisher version: | https://doi.org/10.1016/j.neuroimage.2020.116938 |
| Language: | English |
| Additional information: | © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Brain age, Scalar-on-image regression, Prediction intervals, Quantile regression |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10207314 |
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