Abstract
We propose a new framework for estimating neuroimaging-derived “brain-age” at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18–90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age “gaps.” To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.
Highlights
Brain ageing is associated with cognitive decline and an increased risk of neurodegenerative disease, though these effects vary greatly between individuals
We provide an in-depth analysis of the structural differences seen in people with mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) patients
For AIBL, we obtain an average of 10.23 ± 7.08 years, for Wayne State 8.09 ± 6.08 years, respectively for OASIS3 we get an average of 8.08 ± 6.40 years
Summary
Brain ageing is associated with cognitive decline and an increased risk of neurodegenerative disease, though these effects vary greatly between individuals. Even hippocampal atrophy, which is often thought to be characteristic of Alzheimer’s disease, can be seen in many other neurological and psychiatric conditions, and in normal ageing (Laakso et al, 1996). Both normal ageing and dementia can affect the same brain regions (Lockhart and DeCarli, 2014). This fact complicates research into the earliest stages of age-related neurodegenerative diseases. The brain-age paradigm can offer information on whether an individual’s brain is changing as expected for their age. The difference between chronological age and “brain-predicted age” obtained from neuroimaging data has been provided insights into the relationship between brain ageing and disease, and may be a useful biomarker
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