Abstract
With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The “Brain Age Gap Estimation (BrainAGE)” method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.
Highlights
With population growth and prolonged lifespan, the numbers of individuals with a range of disabling disorders, including neurodegenerative diseases such as cognitive decline and dementia, are rising [1]
Post-hoc tests in the male sample showed advanced brain aging by 2.5 years (p < 0.05) in those who had been prenatally exposed to the famine during early gestation, whereas those who had been born before the famine showed delayed brain aging by −1.8 years, resulting in a difference of about 4 years (p < 0.05; Figure 11A)
In the female maternal nutrient restriction (MNR) offspring, baboon-specific BrainAGE scores were increased by 2.7 years, as compared to female control subjects (CTR) offspring (p = 0.01; Figure 11B), strongly suggesting premature brain aging resulting from prenatal undernutrition during the whole gestation
Summary
With population growth and prolonged lifespan, the numbers of individuals with a range of (non-fatal, but) disabling disorders, including neurodegenerative diseases such as cognitive decline and dementia, are rising [1]. Another study used a number of parameters derived from different MRI modalities (i.e., T1, T2, T2∗, DTI), generating and testing their brain age model by utilizing multiple linear regression in a sample of healthy individuals aged 20–74 years, resulting in an overall age prediction accuracy of r = 0.96 [74]. Another very recent study used a number of parameters derived from T1 and T2∗, including cortical and subcortical measures as well as connectivity data, generating and testing the brain age model by utilizing linear support vector regression (SVR) [79] This approach showed very good performance during cross-validation within the reference sample (combined model: r = 0.93, MAE = 4.3 years), but a rather fair generalizability when validating the brain age model in an independent sample of healthy subjects, with data acquired on a different scanner (combined model: r = 0.86, MAE = 8.0 years). Making music seems to have a slowing effect on the aging of the brain, especially for amateur musicians, while professional musicians revealed a lower effect probably due to stress-related interferences
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
