Cutting-Edge “BrainAGE” Algorithm Can Calculate Your Brain’s Actual Biological Age

Have you ever taken an online quiz, or played a mobile game, that claimed to be able to tell you how old your brain is? While those entertaining claims were surely false, a new technological advancement in the field of neuroscience is now actually able to calculate an individual’s biological brain age — separate from their chronological age. This revolutionary technique can offer individualized insights into brain health and development, potential cognitive decline and overall well-being.

How does BrainAGE work?

Brain Age Gap Estimation, or “BrainAGE,” is a novel algorithm developed by neuroscience researchers to accurately assess the brain’s biological age. It can estimate the biological age of the brain by comparing an individual’s structural MRI data to a normative brain model based on hundreds of healthy individuals, ultimately providing a quantitative measure of brain development and aging. By analyzing various brain regions, BrainAGE can estimate whether an individual’s brain appears older or younger than their chronological age as well as by how many years. The BrainAGE algorithm is rooted in the knowledge that brain aging is a dynamic and non-linear process. As the human brain matures, specific regions of the brain grow (and atrophy) at different times, in specific orders and with non-linear patterns of alteration across the cortex.¹ By using machine learning techniques, the BrainAGE model can analyze these complex patterns to give an impressively accurate estimation of the brain’s biological age. About 95% of the time, the algorithm can calculate the brain’s biological age within about a year for healthy children and adolescents and within 5 years for healthy adults, giving it the potential to offer valuable insights into brain health and age-related conditions.

BrainAGE has calculated that individuals considered at risk for developing Alzheimer’s disease had a calculated BrainAGE of 6 years older on average than their chronological age…

Applications of BrainAGE

Because BrainAGE can offer individualized biomarker data, it has a wealth of valuable healthcare applications as the field of personalized medicine continues to develop.

Predicting Cognitive Decline
BrainAGE has shown promising results in predicting cognitive decline and neurodegenerative disorders such as Alzheimer’s disease. For example, BrainAGE has calculated that individuals considered at risk for developing Alzheimer’s disease had a calculated BrainAGE of 6 years older on average than their chronological age; although these results were collected before any of these individuals began showing symptoms, they unfortunately did begin to show Alzheimer’s symptoms within a few years, demonstrating BrainAGE’s ability to predict cognitive decline before it begins.² By identifying individuals with accelerated brain aging, early interventions and targeted treatments can be implemented to slow down or prevent cognitive decline.

Individualized Wellness
BrainAGE contributes to the field of personalized medicine by providing a quantitative measure of brain health. It allows healthcare professionals to tailor treatment plans based on an individual’s brain age, potentially leading to more effective interventions and improved patient outcomes. Because brain health is often linked to overall physical health, information about brain development can be a signal for other health concerns in an individual. BrainAGE’s ability to predict personalized brain aging trajectories can help clinicians to discover important protective and harmful environmental influences on overall health, and it can also help track an individual’s neurological disease progression more accurately over time, if applicable.³

Understanding Abnormal Development in Childhood
BrainAGE not only provides insights into age-related decline, but it also helps to illuminate the mechanisms of brain development in childhood and adolescence. By studying individuals with younger-than-expected brain ages, researchers can uncover protective factors that contribute to maintaining youthful brain structure in adults, as well as risk factors that slow brain development in children. For example, recent research has shown that preterm birth is a risk factor for slower brain aging and development in children and adolescents.⁴ Patterns of lower biological brain age in children with certain conditions may unfortunately correlate with being cognitively “behind,” which can lead to academic and behavioral issues. Thus, the BrainAGE model could prove to be an incredibly valuable tool for detecting abnormal brain development early in life, so personalized treatments can be created for these children early on.

The Future of BrainAGE

BrainAGE is revolutionizing the field of neuroimaging and aging research by providing a quantitative measure of brain aging and development based on MRI data. With its ability to predict cognitive decline, guide personalized medicine, identify risk factors and detect abnormal neurological development, BrainAGE holds immense potential for improving brain health and overall well-being. As research in this subfield continues to evolve, further application of the BrainAGE algorithm will undoubtedly be invaluable for the field of personalized medicine. By slowly unraveling the mysteries of brain maturation, we pave the way for interventions and treatments that can promote healthy aging and enhance our quality of life for decades to come.

References

1. Franke, K., & Gaser, C. (2019). Ten years of BrainAGE as a neuroimaging biomarker of brain aging: What insights have we gained? Frontiers in Neurology, 10(789). https://doi.org/10.3389/fneur.2019.00789
2. Franke, K., & Gaser, C. (2012). Longitudinal changes in individual BrainAGE in healthy aging, mild cognitive impairment, and Alzheimer’s disease. GeroPsych, 25(4), 235 – 245. https://doi.org/10.1024/1662-9647/a000074
3. Cole, J. H., & Franke, K. (2017). Predicting age using neuroimaging: Innovative brain ageing biomarkers. Trends in Neurosciences, 40(12), 681 – 690. https://doi.org/10.1016/j.tins.2017.10.001
4. Franke, K., Luders, E., May, A., Wilke, M., & Gaser, C. (2012). Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI. NeuroImage, 63(3), 1305 – 1312. https://doi.org/10.1016/j.neuroimage.2012.08.001