Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations

Abstract

Brain imaging can be used to study how individuals' brains are aging, compared against population norms. This can inform on aspects of brain health; for example, smoking and blood pressure can be seen to accelerate brain aging. Typically, a single "brain age" is estimated per subject, whereas here we we identified 62 modes of subject variability, from 21,407 subjects' multimodal brain imaging data in UK Biobank. The modes represent different aspects of brain aging, showing distinct patterns of functional and structural brain change, and distinct patterns of association with genetics, lifestyle, cognition, physical measures and disease. While conventional brain-age modelling found no genetic associations, 34 modes had genetic associations. We suggest that it is important not to treat brain aging as a single homogeneous process, and that modelling of distinct patterns of structural and functional change will reveal more biologically meaningful markers of brain aging in health and disease. KeywordsBrain aging, brain imaging, UK Biobank.Corresponding author: Stephen Smith, steve@fmrib.ox.ac.uk IntroductionBrain imaging can be used to predict "brain age" -the apparent age of individuals' brains -by comparing their imaging data against a normative population dataset. The difference between brain age and actual chronological age (the "delta", or "brain age gap") is often then computed, providing a measure of whether a subject's brain appears to have aged more (or less) than the average age-matched population data. For example, looking at structural magnetic resonance imaging (MRI) data, a high degree of atrophy would cause a subject's brain to appear older than a normal age-matched brain. Estimation of brain age and the delta is of value in studying both normal aging and disease, with some diseases, such as Alzheimer's disease, showing similar patterns of change to that of accelerated healthy aging [Franke et al., 2010, Cole and Franke, 2017.The typical approach uses one or more imaging modalities, most commonly using just a single structural image from each subject. The data is then preprocessed, and features identified, for use in the brain age prediction. For example, the structural images may be warped into a standard space, and grey matter segmentation carried out; the voxelwise segmentation values themselves can then be the features. Alternatively, a smaller number of more highly-condensed features may be derived, such as volumes of grey and white matter within multiple brain regions. The resulting dataset, of multiple subjects' feature sets, along with their true ages, is then passed into a supervised-learning algorithm (e.g., regression, support vector machine or deep learning). The algorithm then learns to predict the subjects' ages from their brain imaging features. Finally, the true age is typically subtracted from the estimated brain age, to create a delta, potentially with corrections for biases such as systematic mis-estimation of brain age [Le et al., 2018.The imaging feature set can be derived from more ...

Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

“…In line with recent findings from UK Biobank [14,15], positive associations were found between brain-age deltas and diastolic blood pressure, alcohol intake, and stroke risk, concurring with previous WHII studies [32,50], and demonstrating that the brain age-delta measure reflects individual variation in neural aging processes [30]. The associations with biomedical variables were consistent across models (see Figure 5), indicating that while modality-specific brain age models may be informative in patient groups where tissue types are differently affected by disease [26,27,54,67,68], such models may be more closely related in healthy cohorts [14]. It is possible that regional modelling of modality-specific brain aging patterns may be more suitable to detect specific associations with biomedical and clinical measures [19], which could get lost in machine learning models that summarise aging across the whole brain to produce a single global prediction [27].…”

“…The associations with biomedical variables were consistent across models (see Figure 5), indicating that while modality-specific brain age models may be informative in patient groups where tissue types are differently affected by disease [26,27,54,67,68], such models may be more closely related in healthy cohorts [14]. It is possible that regional modelling of modality-specific brain aging patterns may be more suitable to detect specific associations with biomedical and clinical measures [19], which could get lost in machine learning models that summarise aging across the whole brain to produce a single global prediction [27].…”

“…In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. While the presented imaging-derived markers can help to assess general effects of clinical and biomedical risk factors on the brain, models of distinct and regional neural aging patterns may result in more refined biomarkers that can capture additional biological detail [19,26,27].…”

“…Modality-specific brain age models (based on e.g. gray and white matter separately) provide information about tissue-specific aging processes [26,27]. For instance, imaging-derived measures of gray matter are known to detect cortical atrophy in older age-groups [28], while changes in diffusion MRI measures reflect age-related decline in white matter microstructure, as well as white matter lesions, which are more prevalent in aging, relative to young, populations [29].…”

“…Functional MRI (fMRI) measures are indicative of brain network connectivity, which may change with advancing age [2,3]. Cardiovascular risk factors may influence these neural aging processes differently [26,27,30], and in a recent Whitehall II (WHII) MRI study using voxelwise analyses, allostatic load, metabolic syndrome, and multifactorial stroke risk predicted gray matter density measured decades later, while only cumulative stroke risk measured by the Framingham stroke risk score [31] predicted white matter integrity in terms of fractional anisotropy and mean diffusivity [32].…”