Such inter-individual differences between predicted biological and chronological age have been studied in relation to lifestyle variables 16, 17, 18, 19 and to genetic determinants 14, 17, 20. Similar phenomena are already apparent in others who have mild cognitive impairment (MCI) that progresses to dementia 12, 15. As an example, brain age predictive models using data from structural magnetic resonance imaging (MRI) have shown accelerated biological aging in individuals who develop AD dementia 11, 12, 13, 14. We investigated whether predicted brain age may be a relevant biomarker of neurodegenerative disease 2, inasmuch as disease may cause deviations from normal aging trajectories, and the factors that influence these deviations may be studied.
Recent availability of large-scale neuroimaging datasets has facilitated the application of machine learning techniques and enabled development of models that can predict behavior and characteristics of brain structure and function known to change with age 2, 3, 4, 5, 6, 7, 8, 9, 10.
However, increasing evidence suggests that neural systems vulnerable to age are also vulnerable to Alzheimer’s disease (AD) and other neurodegenerative diseases 1. It is not fully understood how neurodegenerative diseases affect brain regions and networks that are also affected by normal aging. The brain shows major changes over the course of aging. Nature Communications volume 12, Article number: 5346 ( 2021)
Dominantly Inherited Alzheimer Network (DIAN) Study Group &.Alzheimer’s Disease Neuroimaging Initiative (ADNI),.Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease
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