Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression
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 uncertai...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-10-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920304249 |
| _version_ | 1818210752105807872 |
|---|---|
| author | Marco Palma Shahin Tavakoli Julia Brettschneider Thomas E. Nichols |
| author_facet | Marco Palma Shahin Tavakoli Julia Brettschneider Thomas E. Nichols |
| author_sort | Marco Palma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T05:21:36Z |
| format | Article |
| id | doaj.art-75cc3c7787394052b0f495de7830541e |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-12T05:21:36Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-75cc3c7787394052b0f495de7830541e2022-12-22T00:36:36ZengElsevierNeuroImage1095-95722020-10-01219116938Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regressionMarco Palma0Shahin Tavakoli1Julia Brettschneider2Thomas E. Nichols3Department of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; Corresponding author.Department of Statistics, University of Warwick, Coventry, CV4 7AL, United KingdomDepartment of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; The Alan Turing Institute, London, NW1 2DB, United KingdomDepartment of Statistics, University of Warwick, Coventry, CV4 7AL, United Kingdom; Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United KingdomPrediction 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.http://www.sciencedirect.com/science/article/pii/S1053811920304249Brain ageScalar-on-image regressionPrediction intervalsQuantile regression |
| spellingShingle | Marco Palma Shahin Tavakoli Julia Brettschneider Thomas E. Nichols Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression NeuroImage Brain age Scalar-on-image regression Prediction intervals Quantile regression |
| title | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression |
| title_full | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression |
| title_fullStr | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression |
| title_full_unstemmed | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression |
| title_short | Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression |
| title_sort | quantifying uncertainty in brain predicted age using scalar on image quantile regression |
| topic | Brain age Scalar-on-image regression Prediction intervals Quantile regression |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920304249 |
| work_keys_str_mv | AT marcopalma quantifyinguncertaintyinbrainpredictedageusingscalaronimagequantileregression AT shahintavakoli quantifyinguncertaintyinbrainpredictedageusingscalaronimagequantileregression AT juliabrettschneider quantifyinguncertaintyinbrainpredictedageusingscalaronimagequantileregression AT thomasenichols quantifyinguncertaintyinbrainpredictedageusingscalaronimagequantileregression |