Brain Age | Can We Truly Measure How Old Your Brain Is?

'Brain age' is a biological metric that estimates the physiological age of the brain, which may differ from an individual's chronological age (the number of years they have been alive). This estimation is not a guess but a calculated prediction derived from neuroimaging data, primarily Magnetic Resonance Imaging (MRI) scans. Scientists use a sophisticated computational technique called machine learning. In this process, a computer algorithm is 'trained' on thousands of brain scans from a diverse population of healthy individuals. The algorithm learns to identify complex patterns of age-related changes in brain structure and function. For instance, it analyzes features like the volume of gray matter (which contains most of the brain's neuronal cell bodies) and the integrity of white matter tracts (the bundles of nerve fibers that connect different brain regions). Once trained, this model can be applied to a new individual's brain scan to predict an age. This predicted age is their 'brain age.' If the predicted brain age is higher than the person's actual age, it suggests the brain is aging faster than expected, which can be an indicator of potential health risks. Conversely, a younger brain age suggests a healthier, more resilient brain. The concept provides a comprehensive biomarker for brain health.

The prediction of brain age relies on quantifying specific neuroanatomical and functional features, often referred to as biomarkers. The most critical biomarkers are derived from structural MRI. These include gray matter volume, which naturally decreases with age as neurons shrink or are lost. Another key biomarker is cortical thickness, the thickness of the brain's outer layer, which is crucial for higher cognitive functions like memory and attention; this also tends to thin over time. Furthermore, the integrity of white matter is assessed. White matter consists of myelinated axons that transmit signals between brain regions, and its structural integrity can be measured using a technique called Diffusion Tensor Imaging (DTI). Age-related decline in white matter integrity can slow down information processing. In addition to structural data, functional MRI (fMRI) can provide biomarkers by measuring brain activity and the connectivity between different brain networks. Age can alter these functional connections, leading to less efficient brain network organization. By integrating these multiple biomarkers, machine learning models create a robust and holistic estimate of brain age.

A higher predicted brain age relative to one's chronological age, a metric known as the 'brain age gap' or 'delta', is considered an indicator of accelerated brain aging. This gap is not a diagnosis in itself, but it is a significant risk factor. Scientific studies have consistently linked a larger brain age gap to an increased vulnerability for various neurological and psychiatric conditions. For example, individuals with a higher brain age are at greater risk for developing neurodegenerative diseases such as Alzheimer's and Parkinson's disease. It is also associated with poorer outcomes after a brain injury and is linked to cognitive decline in otherwise healthy adults. Furthermore, an older-appearing brain has been observed in individuals with conditions like depression and schizophrenia, suggesting that accelerated aging may be a shared component across different mental health disorders.

Yes, the measurement of brain age has limitations and potential for inaccuracy. The precision of the prediction depends heavily on the quality and diversity of the data used to train the machine learning algorithm. If the training dataset is not representative of the general population (e.g., lacks diversity in age, sex, ethnicity, or socioeconomic status), the model's predictions may be biased and less accurate for certain groups. Technical variability between different MRI scanners and imaging protocols can also introduce noise and affect the consistency of measurements. Moreover, brain age is a dynamic measure influenced by transient factors like sleep quality, stress, and hydration at the time of the scan. Therefore, a single brain age measurement should be interpreted with caution as one piece of a larger health assessment, not as an absolute or deterministic value.

While you cannot reverse your chronological age, evidence strongly suggests that you can positively influence your brain's biological age. The brain age gap is not fixed; it can be modified by lifestyle interventions. Engaging in regular physical activity is one of the most effective ways to support brain health. Aerobic exercise, in particular, has been shown to increase blood flow to the brain, promote the formation of new neurons (a process called neurogenesis), and potentially reduce the brain age gap. Diet is also critical. A diet rich in antioxidants and omega-3 fatty acids, such as the Mediterranean diet, can protect the brain from oxidative stress and inflammation, which are key drivers of aging. Equally important are sufficient, high-quality sleep and effective stress management, as both chronic sleep deprivation and high stress levels can accelerate brain aging. Lastly, staying mentally active through learning new skills, social engagement, and challenging cognitive tasks helps maintain neural networks and build cognitive reserve. These lifestyle factors contribute to brain resilience and can help maintain a younger, healthier brain structure and function over time.