Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction - Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H. (Frontiers in Aging Neuroscience, 2018)
Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain ageing, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalisation to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimising resampling parameters using Bayesian optimisation. Using data on N=2003 healthy individuals (aged 16-90 years) we trained support vector machines to i) distinguish between young (<22 years) and old (>50 years) brains (classification) and ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N=648, aged 18-88 years). Bayesian optimisation was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5mm3, smoothing kernel = 2.3mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73mm3, smoothing kernel = 3.68mm). This was compared to performance using default values of 1.5mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimisation framework to the new dataset, out-performing the parameters optimised for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimisation to derived case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimisation is conducted in specific contexts. This potentially motivates use of optimisation techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias.