Summary: Researchers at Monash University are studying the age of the brain, which may not always match the age of its host. Their work aims to unravel this mystery from different perspectives.
Source: Monash University
Do you know how old your brain is? This is not a trick question: a brain may not be the same age as its host.
Two Monash researchers are working on this question from different angles in an attempt to find answers.
Jo Wrigglesworth is a PhD. candidate at Monash School of Public Health and Preventive Medicine specializing in brain age. While working alongside Associate Professor Joanne Ryan on epigenetics research, she learned (in 2017) of a new method for predicting aging based on neuroimaging and machine learning data.
He published a systematic review of the research in 2021, then applied it to a group of healthy elderly Australians recruited from the ASPREE (ASPirin in Reducing Events in the Elderly) clinical trial and found that their brains looked younger than the norm (termed also referred to as “decelerated aging brain”).
But it could just as easily have shown aged and “atrophied” brains.
“It’s a useful algorithm,” he says, “and it showed that our group had what I would call slowed aging. The general idea is that it could provide a personalized measure of the risk of cognitive decline, sooner than expected.
“Although we know that brain atrophy tends to be associated with worse outcomes such as cognitive decline, we have yet to overcome the diversity of aging in our population. Brain age is one approach to capturing our unique phenotypes.
His research includes findings of an association between accelerated brain aging and poor cognitive function, and that older males had a faster rate of brain aging over a three-year period.
“However, there are other complexities where people have atrophy, but are still functionally fine, and that’s where the element of understanding those people can be really important.”
Wrigglesworth explored this notion in a new article published in Frontiers in Aging Neuroscience.
“With all of these things,” he says, “you have to research it, test it, see the possibilities, and then if there’s something there, hopefully bring it into a clinical setting. But right now, we still have a long way to go.” .
He says this is a new but “thriving” science.
“Brain age is relatively new, and as such there is much more to explore before its clinical potential can be considered. For example, there is a possibility that there is no universal biomarker of brain age to address all situations. We need to consider more models, involving different features of the brain.”
Brain injury a factor
At the Turner Institute for Brain and Mental Health and the Department of Neuroscience, Dr. Gershon Spitz, a research scientist, specializes in traumatic brain injury (TBI) and brain age.
In an article published last year inNeuroimaging: clinicconducted research that found, for the first time, that a single traumatic brain injury can result in “older-seeming” brains decades after the initial injury.
This “aging” is very peculiar, says the newspaper.
“We recognized that a massive blow to the brain can lead to processes that interact with how you age with the environment you’re in throughout your life,” explains Dr. Spitz.
“It is a progressive process that takes years, maybe even decades.
“This new way of looking at injury has to do with the idea that traumatic brain injury initiates certain processes that can lead to neurodegenerative diseases such as Alzheimer’s, possibly Parkinson’s disease, and chronic traumatic encephalopathy, or CTE , which we see in some NFL and AFL athletes.”
The research studied people with a single moderate or severe head injury an average of 22 years after the injury.
“We had a fantastic dataset of a hundred people with a brain injury and a hundred people without. What we’ve shown is that compared to their chronological age, their brain age looks older than it should.”
The researchers took these findings a step further: “It’s nice to find some sign of an abnormality on an MRI, say, but it’s even better to show that it has some clinical relevance,” he says.
“So we actually took it a step further and looked at the extent to which this brain age gap was associated with clinical outcomes. We found an association with the cognitive domain of verbal memory.
“So the greater the deviation between your chronological age and your brain age, the worse your verbal memory can be.”
Verbal memory is the ability to encode, acquire and recall a list of words.
“It is one of the domains showing the first signs of impairment in age-related diseases. So there’s some of this interesting signature of something that’s chronic over the long term,” Dr. Spitz says, “and the more we look into that, the more we think there’s something there for some people.”
A new world-first study, recruiting early but using essentially the same cohort of people with TBI and people without, will try to get closer to the nitty-gritty of brain age in all of this. Does the brain accelerate at a faster rate in people with a head injury who may also have signals towards verbal memory loss?
“Essentially, what we could hypothesize is that individuals who have certain disease signatures in this baseline should show steeper trajectories or steeper decline over the five-year period,” says Dr. Spitz.
The extent of this change should also be associated with the change in their neuropsychological capabilities.
“That’s what I’m suggesting we find. Do those individuals thought to be at high risk actually exhibit this change over time?
About this brain age research news
Author: Press office
Source: Monash University
Contact: Press Office – Monash University
Image: Image is public domain
Original research: Free access.
“Health-Related Heterogeneity in Brain Aging and Associations with Longitudinal Change in Cognitive Function” by Jo Wrigglesworth et al. Frontiers in Aging Neuroscience
Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function
Introduction: Brain age based on neuroimaging can identify individuals with advanced or resilient brain ageing. Predicted brain age difference (brain-PAD) is predictive of cognitive and physical health outcomes. However, it is not known how individual health and lifestyle factors might modify the relationship between brain PAD and future cognitive or functional performance. We aimed to identify health-related subgroups of older individuals with resilient or advanced brain PAD and determine whether membership in these subgroups is differentially associated with changes in cognition and frailty over three to five years.
Methods: Brain-PAD was predicted from T1-weighted images acquired from 326 community-dwelling older adults (73.8 ± 3.6 years, 42.3% female), recruited from the larger ASPREE (ASPirin in Reducing Events in the Elderly) study . Participants were grouped according to resilient (n=159) or advanced (n=167) brain PAD, and latent class analysis (LCA) was performed using a range of cognitive, lifestyle, and health measures . We examined associations of class membership with longitudinal change in cognitive function and frailty deficit accumulation index (FI) using linear mixed models adjusted for age, sex, and education.
Results: The resilient and advanced brain aging subgroups were comparable in all characteristics before LCA. Two typically similar latent classes were identified for both subgroups of brain agers: class 1 was characterized by low prevalence of obesity and better physical health, and class 2 by poorer cardiometabolic, physical, and cognitive health. Among brain resilient older adults, class 1 was associated with a decrease in cognition and class 2 with an increase over 5 years, although it was a small effect equivalent to a standard deviation difference of 0.04 per year. No significant class distinctions were evident with FI. For cerebral advanced older adults, there was no evidence for an association between class membership and changes in cognition or FI.
Conclusion: These results demonstrate that the relationship between brain age and cognitive trajectories may be influenced by other health-related factors. Notably, people with age-resistant brains had different trajectories of cognitive change depending on their baseline cognitive and physical health status. Future predictive models of aging outcomes will likely be aided by considering the mediating or synergistic influence of multiple health and lifestyle indices together with brain age.