Deriving Brain Age From Imaging In The UK Biobank

Pixabay License | Source: Gordon Johnson , no changes made.

Magnetic resonance imaging (MRI) can be used to obtain a measure of “brain age”, which may be different from chronological age. This is achieved by comparing an individual’s brain scan with a population-wide dataset. The “brain age gap” (or delta), the difference between the brain age and the chronological age, provides an idea of whether the brain has aged more or less than the average. It is possible that the “brain age gap” could give an early warning of diseases such as Alzheimer’s.

A study led by Karla L Miller of the Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK, analysed six brain imaging modalities from UK Biobank (Miller et al., 2016), identifying 62 different mechanisms of brain age variation. These were used individually and in combination to gain a better understanding of brain age prediction and the underlying mechanisms. The results were published in the journal eLife.

The authors generated 3913 IDPs (imaging-derived phenotypes). The IDPs summarize different aspects of brain structure or function. The IDPs include functional and structural connectivity between specific pairs of regions, localised tissue microstructure and biological makeup, and the geometry of cortical and subcortical structures. These IDPs were correlated with different brain aging mechanisms, or modes.

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The 62 modes of brain aging were used in a genome-wide association (GWAS) to identify 156 SNPs with high association with the particular brain age phenotype under study. In addition 57 of the 62 modes were significantly heritable. Interestingly none of these modes of ageing correlated strongly with Alzheimer’s or Parkinson’s genetics.

Several associations were made between non-imaging and non-genetic factors recorded in the UK biobank, such as disease status and several measures of body composition.

The study is limited by a potentially inaccurate model of how brain aging changes over time. For example the model assumes brain aging in relation to a particular “mode” is constant over time when in fact it could accelerate. In addition false positive brain age gap could be identified due to differences in brain structure between individuals at baseline, which does not in fact reflect brain aging. The authors note that the availability of more brain imaging data could allow them to refine their models further.

“Here, we aimed to study how multiple, biologically distinct, modes of population variation in brain structure and function reflect different aspects of the aging brain. We investigated the modes’ distinct associations with genetics, life factors and biological body measures, in the context of the modelling of brain age and brain-age delta – a measure of whether subjects’ brains appear to be aging faster or slower than the population average,” concluded the authors.