Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression.
Neuroimage
; 219: 116938, 2020 10 01.
Article
en En
| MEDLINE
| ID: mdl-32502669
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Encéfalo
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Envejecimiento
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Incertidumbre
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Enfermedad de Alzheimer
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Neuroimagen
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Disfunción Cognitiva
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Límite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Año:
2020
Tipo del documento:
Article