“Slowing the aging of the brain” may sound like an unrealistic advertising stunt, but in reality, the fantasy, with the support of science, could become a reality. “Brain age” does not reflect the average function of a person’s actual age, but is more related to the degree of aging relative to the actual age of the brain. We all know that although some people seem to be old, they are still quick-thinking and flexible.
You can’t believe it when you think the woman you’re talking to on a plane is only in her 40s, but her brain is in her 70s. As the name “brain age” refers to, the concept hopes to capture the biological complexity behind cognitive separation.
It’s not just pure academic fun. Longevity researchers increasingly want to think that how long you live isn’t the best indicator of overall health. Accurate and simple measurement sits the age of a person’s real biological brain may be a more effective warning. After all, if you know your brain is aging faster than expected, you can intervene early in the process.
A study published in the journal Nature Neuroscience incorporates three completely different areas into a single algorithm, including neuroscience, longevity and machine learning, which can predict a person’s brain age based solely on MRI scans.
The study, which used data from nearly 50,000 people over the age span of more than 80, first looked at how common brain disorders affect brain aging, such as depression and autism. What’s more, the team delved into human genome data from the British Biobank to pinpoint a set of genes associated with neurological diseases, particularly those that accelerate brain aging.
Study author Tobias Kaufmann, of the University of Oslo in Norway, said: “We have revealed genes that are clearly associated with brain aging in healthy individuals, and they overlap with those associated with our common brain diseases. “
The direct use of this “brain age gap” indicator can serve as a biomarker of brain aging, helping doctors make more informed diagnoses of their elderly patients.
But Dr. Janine Bijsterbosch of Washington University School of Medicine in St. Louis, Missouri, said that in addition to the findings of the study, perhaps its most important contribution was to confirm the effectiveness of an interdisciplinary approach that “could only be possible by studying brain scans in a large number of people.” Covers scanners, locations, and settings.
Want to convert? Data from a single lab is no longer enough to look for tiny, complex but powerful signs of brain aging, or other neurological measurements and health insights. To better uncover the mysteries of our brains and bridge racial and socio-economic divides, we need to recognize and use strategies in our research, namely, “people with more power.”
Brain age and healthy life expectancy
In late 2015, a series of expert reviews in the journal Nature Medicine reinforced an emerging trend in longevity research. Rather than trying to extend life, the current focus should be more on extending healthy life expectancy, i.e. how long a person can survive without disease, or how long a common age-related disease occurs.
This immediately raises the question: How do you measure a person’s “real” biological age? This is an unresolved problem. But for the brain, there is a sign that is playing a leading role, namely the age gap in the brain, or the difference between someone’s actual age and the age of the brain. This indicator can indicate that someone’s brain ages faster or slower than normal.
“Molecular orchestras” that control the brain’s maturation and rate of change throughout its life cycle play an important role in the brain structure, but it can be measured using MRI. Similarly, the “biological dance” that determines the physical connection of neural circuits is the basis for brain disorders such as autism, schizophrenia, bipolar disorder, or depression.
This led the team to ask the question: Is there a way to use MRI scans to measure someone’s brain age gap? What happens to different mental disorders? Can we link brain age to specific genes, revealing genes that accelerate and slow brain aging?
Kaufman and his colleagues aren’t the first to try to solve the problem, but they’re certainly the most ambitious. They explain that previous studies have been “small-scale” because they focus only on a limited age range, often with a single mental disorder or the size of a maximum of a few hundred people. These studies do not provide an overall dynamic picture of changes in brain structure throughout the life cycle.
Since no lab could provide the data they needed, the team decided to collect MRI scan data from several locations, which was obtained by different MRI scanners under different settings. In the past, this has been crazy, as these changes have made it extremely difficult to compare images between Apple and Apple.
Using cooking as an analogy is like trying to identify hundreds of thousands of handwritten recipes for the same dish, each written in a personal format using a series of units and abbreviations, and trying to decipher an average “baseline” recipe to fully judge the accuracy and value of all other recipes.
The team relied on a range of advanced data methods to transform data from 45,615 people into standardized collections, a task that took a lot of effort, time and trial. As a soundness check, they then include this information in their machine learning algorithms to re-examine potential standardized errors. Next, using data from more than 35,000 healthy people between the ages of 3 and 89, they trained artificial intelligence to predict normal brain aging trajectories. The algorithm was then validated by data from another 4,353 healthy people. Finally, the team compared brain scans of nearly 5,800 people with various brain diseases to match each person’s brain age to a general trajectory.
The researchers made several discoveries. The biggest age gaps in the brain are severe mental disorders, including schizophrenia, multiple sclerosis and dementia. In contrast, developmental brain disorders, such as autism and attention deficit hyperactivity disorder (ADHD), do not appear to particularly affect brain age.
Leaving aside the overall changes in the brain, the team also found that the brain regions that contribute to the age gap in the brain were those that were already involved in this particular mental disorder. In Alzheimer’s disease, for example, the structure of the subsurface region of the brain slowly withers, and they are also the areas that trigger the age gap in the brain measured by algorithms.
This is an important validation, the researchers said. It shows that artificial intelligence can condense information from a large number of brain images into an explanatory score without losing information about individual brain regions entirely. In other words, some diseases may cause one brain region to age faster than others. Artificial intelligence can decipher these differences and guide potential treatments.
Another benefit of aggregating data sets is that they contain genetic information related to brain scans. Accelerated brain aging may be the result of poor genetic genes, and harmful environmental or lifestyle choices can exacerbate this situation. The researchers say analyzing genes is a way to begin exploring the factors that influence changes in the brain’s aging trajectory.
Perhaps unsurprisingly, an analysis suggests that the age gap in the brain is at least partly inheritable. The team also found genes that appear to have an effect on the brain’s age gap and brain disorders. In other words, each gene varies from person to person and has its own effects.
“Genetic variations associated with age gaps in the healthy human brain partially overlap with attention deficit hyperactivity disorder (ADHD) observed in autism,” the researchers said. These results suggest that there is a common molecular genetic mechanism between the age gap in the brain and brain disease. “
Age of the individual brain
Artificial intelligence is the first step in helping to determine the age of an individual’s brain. Dr. Bethbos said it was difficult to move from average results to separate scans because MRI scans were relatively noisy and varied from person to person. More research is needed, but given its size, it has laid a solid foundation for it.
Ultimately, the researchers hope to predict a person’s brain age gap and track progress to help adjust their treatment plan, based on their genes, before a high-risk brain disease attack.
Dr Bethbos said: “We are a long way from exploiting the brain age gap in this way. “But with a number of large-scale biomarker studies, this model of the intersection of neuroscience and artificial intelligence is just the beginning.