Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data
| Autor: | Vince D. Calhoun, Pedro F. da Costa, Walter H. L. Pinaya, Sandra Vieira, Jessica Dafflon, Cristina Scarpazza, Rafael Garcia-Dias, Andrea Mechelli, Lea Baecker, João Ricardo Sato |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Support Vector Machine Computer science biological ageing Neuroimaging Machine learning computer.software_genre regression analysis 050105 experimental psychology Machine Learning 03 medical and health sciences 0302 clinical medicine Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging Research Articles Aged Radiological and Ultrasound Technology business.industry Dimensionality reduction 05 social sciences Brain morphometry Age Factors Brain Regression analysis Middle Aged Magnetic Resonance Imaging Regression Data set Support vector machine Neurology Test set Principal component analysis Female Neurology (clinical) Artificial intelligence Anatomy healthy ageing business computer 030217 neurology & neurosurgery Research Article |
| Zdroj: | Baecker, L, Dafflon, J, Da Costa, P F, Garcia Dias, R, Vieira, S, Scarpazza, C, Calhoun, V D, Sato, J R, Mechelli, A & Pinaya, W H L 2021, ' Brain age prediction: A comparison between machine learning models using region-and voxel-based morphometric data ', Human Brain Mapping, vol. 42, no. 8, pp. 2332-2346 . https://doi.org/10.1002/hbm.25368 Human Brain Mapping |
| DOI: | 10.1002/hbm.25368 |
| Popis: | Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such “brain age prediction” vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47–73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole‐brain region‐based or voxel‐based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross‐validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel‐level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research. We compared the machine learning models support vector regression, relevance vector regression and Gaussian process regression for brain age prediction using different types of morphometric input and sample sizes of more than 10,000 subjects. The mean absolute error across the different models ranged from 3.7 to 4.7 years. The type of data input (region‐ or voxel‐level) had a greater impact on performance than the choice of model. |
| Databáze: | OpenAIRE |
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