Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data

Abstract

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.

Machine learning for brain age prediction: Introduction to methods and clinical applications

“…Statistical Parametric Mapping, http://www.fil.ion.ucl.ac.uk/spm ) [13] , [14] , [15] , [16] , [17] , [18] , [19] methods to obtain measures such as regional or tissue-specific volumes, cortical thickness, or surface area. Next, the researcher may choose to use measures from the whole brain [ 4 , 11 ], perform some kind of feature selection [ 9 , [20] , [21] , [22] ], or compare both of these approaches [ 1 , 23 , 24 ]. Dimensionality reduction through automatic models like principal component analysis are commonly employed to reduce the high dimensionality of voxel-based data and remove redundant information, as this can reduce computational cost and increase accuracy [ 1 , 8 , [24] , [25] , [26] ].…”

“…the subject's MRI scan is associated with their chronological age) and then applied to a test dataset without labels to assess how well they predict the brain age of unseen subjects. The majority of these models make use of regression techniques, where structural brain features are the independent variables and chronological age is the dependent variable [ 1 , 4 , 11 , 12 , 24 ]. Overall, the available machine learning models for brain age prediction differ with regards to complexity, computational resources, and involvement by the researcher.…”

“…Overall, the available machine learning models for brain age prediction differ with regards to complexity, computational resources, and involvement by the researcher. Recent studies compared the performance of commonly used models like support vector regression and relevance vector regression to provide guidance on the most suitable model choices for brain age prediction (MAE 2.6-7.7 years [12] ; MAE 3.7-4.7 years [24] ); however, as demonstrated by Wolpert [27] in what is known as the ‘no free lunch’ theorem for machine learning, the performance of different models will depend on the characteristics of the datasets, so there is no single best model for a certain task. This means that a researcher may choose to train different types of models on their data before choosing the most suitable one.…”

“…uk/spm) [13À19] methods to obtain measures such as regional or tissue-specific volumes, cortical thickness, or surface area. Next, the researcher may choose to use measures from the whole brain [4,11], perform some kind of feature selection [9,20À22], or compare both of these approaches [1,23,24]. Dimensionality reduction through a.…”

Optimal Blood Pressure Keeps Our Brains Younger

“…Importantly, in this research, we conceptualize BrainAGE as a marker of brain health with higher BrainAGE suggesting poorer brain health. This is because extensive research is available indicating that BrainAGE (and similar approaches)-in addition to being methodologically robust and reliable Gaser, 2012, 2019;Baecker et al, 2021)-is associated with cognitive decline, the transition from MCI to Alzheimer's disease, and markers of the underlying pathology and its main genetic risk factor, APOE genotype (Gaser et al, 2013;Löwe et al, 2016;Wang et al, 2019). Moreover, BrainAGE is significantly increased in several chronic conditions including type 2 diabetes (Franke et al, 2013), stroke (Egorova et al, 2019), Parkinson's disease (Beheshti et al, 2020), Multiple Sclerosis (Cole et al, 2020), and known health and lifestyle risk factors for cardiovascular health, neurodegeneration, brain ageing, and dementia (Bittner et al, 2021).…”