Deep learning of brain age captures domain-specific cognitive impairment

What is it about?

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.

Featured Image

Photo by Robina Weermeijer on Unsplash

Photo by Robina Weermeijer on Unsplash

Why is it important?

The age of the human brain, as revealed through deep learning of anatomic MRIs, reflects brain changes related to cognitive decline. Compared to chronological age, our inferred brain ages are significantly more strongly associated with early signs of Alzheimer’s disease. Our deep learning approach reveals differences in patterns of neurological aging between males and females and between persons with and without cognitive impairment. These findings provide insight into early identification of persons at high risk of Alzheimer’s disease.

Perspectives