Summary: Researchers at Monash University are studying the age of the brain, which does not always correspond to the age of its host. Their work aims to unravel this mystery from different angles.
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 effort to find answers.
Jo Wrigglesworth holds a Ph.D. candidate at the Monash School of Public Health and Preventive Medicine, specializing in brain age. While working alongside Associate Professor Joanne Ryan on epigenetics research, she discovered (in 2017) a new method to predict aging based on neuroimaging data and machine learning.
She published a systematic review of the research in 2021, then applied it to a group of healthy older Australians recruited into the ASPREE (ASPirin in Reducing Events in the Elderly) clinical trial, and found that their brains looked younger than normal (also defined as “slowed brain aging”).
But it could just as well have shown aging, “atrophied” brains.
“It’s a useful algorithm,” she says, “and it showed that our group had what I would call slow aging. The general concept 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 poorer outcomes, such as cognitive decline, we have not yet 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 men had a faster rate of brain aging over a three-year period.
“However, there are other complexities where people have atrophy, but they’re still functionally fine, which is where the element of understanding these people can be really important.”
Wrigglesworth explored this notion in a new article published in Frontiers of the neurosciences of aging.
“With all of these things,” she says, “you have to do research, test them, see the possibilities, and then if there’s something out there, hopefully put it in a clinical setting. But for now, we still have a long way to go. »
She says it’s a new but “growing” science.
“Brain age is relatively new, and as such, we still have a lot to explore before we can consider its clinical potential. For example, there may not be a single universal brain age biomarker to address all situations. We have to consider several models, involving different brain characteristics.
Brain damage a factor
At the Turner Institute for Brain and Mental Health and Department of Neurosciences, researcher Dr. Gershon Spitz specializes in traumatic brain injury (TBI) and brain age.
In an article published last year in Neuroimage: clinicalhe led research that revealed, for the first time, that a single traumatic brain injury can result in an “older” brain decades after the initial injury.
This “aging” is very particular, specifies 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 find yourself in throughout your life,” says Dr. Spitz.
“It’s a gradual process that takes years, 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 like Alzheimer’s disease, possibly Parkinson’s disease, and chronic traumatic encephalopathy. , or CTE, which we see in some NFL and AFL athletes.
Research has studied people with a single moderate or severe head injury an average of 22 years after their injury.
“We had a fantastic data set of a hundred people with brain injury and a hundred people without. What we’ve shown is that relative to their chronological age, their brain age looks older than it should.
The researchers took these findings one step further: “It’s good to find an anomaly signature on an MRI, say, but it’s even better to show that it has some clinical relevance,” he says. .
“So we went a step further and looked at the extent to which this brain age difference was associated with clinical outcomes. We found an association with the cognitive domain of verbal memory.
“So the greater the gap between your chronological age and your brain age, the worse your verbal memory may be.”
Verbal memory is the ability to encode, acquire and recall a list of words.
“This is one of the areas that shows early signs of deficiency in age-related diseases. So there’s a bit of this interesting signature of something that’s long-term chronic,” says Dr. Spitz, “and the more we study it, the more we think there’s something there for some people. .
A new global first study, soon recruiting but using essentially the same cohort of people with TBI and people without, will try to get closer to the heart of brain age in all of this. Does brain age accelerate faster in people with TBI who might also have signals towards verbal memory loss?
“Essentially, what we would assume is that individuals who exhibit certain signatures of pathology at this baseline should show steeper trajectories or more pronounced decline over the five years,” says Dr. Spitz.
The magnitude of this change should also be associated with the change in their neuropsychological abilities.
“That’s what I would suggest we find. Do people considered high risk actually show this change over time? »
About this Brain Age Research News
Author: Press office
Source: Monash University
Contact: Press Office – Monash University
Picture: Image is in 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 of the neurosciences of aging
Abstract
Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function
Introduction: Neuroimaging-based “brain age” can identify individuals with “advanced” or “resilient” brain aging. Brain-predicted age difference (Brain-PAD) is predictive of cognitive and physical health outcomes. However, it is unclear how individual health and lifestyle factors may alter the relationship between brain-PAD and future cognitive or functional performance. We sought to identify health-related subgroups of older adults with resilient or advanced brain PAD, and 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 elderly people (73.8 ± 3.6 years, 42.3% female), recruited into the trial ASPREE (ASPirin in Reducing Events in the Elderly) larger. Participants were grouped as having resilient (n=159) or advanced (n=167) brain PAD, and latent class analysis (LCA) was performed using a set of cognitive measures, mode of life and health. We examined the associations between class membership and longitudinal change in cognitive function and the accumulation of frailty deficit (FI) index using age-adjusted linear mixed models. , sex and education.
Results: The resilient and advanced brain aging subgroups were comparable in all pre-stroke characteristics. Two typically similar latent classes were identified for the two subgroups of cerebral elderly: class 1 was characterized by a low prevalence of obesity and better physical health and class 2 by poor cardiometabolic, physical and cognitive health. Among resilient aging brains, class 1 was associated with a decrease in cognition and class 2 with an increase over 5 years, although this was a small effect equivalent to a standard deviation difference of 0 .04 per year. No significant class distinction was evident with FI. For the cerebrally advanced elderly, there was no evidence of 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. In particular, 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 lifestyle and health indices alongside brain age.