AI Reveals Three Distinct Brain Aging Patterns: Implications for Alzheimer’s and Dementia

by Administrator
brain aging patterns

The National Institute on Aging (NIA) recently published a groundbreaking study in JAMA Psychiatry, revealing three distinct brain aging patterns and dementia risk through AI-assisted analysis of over 20 years of data. This research, conducted by an international team including NIA-funded researchers from the University of Pennsylvania and NIA Intramural Research Program scientists, utilized machine learning to analyze neuroimaging, clinical, and cognitive data from more than 27,000 participants. The study identified a normal aging trajectory and two subgroups with accelerated brain aging, offering new insights into predicting Alzheimer’s disease and vascular dementia.

Key Takeaways:

  • AI analysis identified three brain aging trajectories: typical aging and two accelerated aging subgroups
  • Study analyzed over 20 years of data from 27,000+ participants
  • Findings could improve early detection and prediction of Alzheimer’s and vascular dementia

Study Overview

The study leveraged data from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Researchers examined structural brain changes associated with genetics, cardiovascular risk, beta-amyloid, cognitive decline, smoking, and white matter hyperintensity (WMH), which are lesions linked to Alzheimer’s and cognitive impairment. The AI analysis identified three distinct brain aging patterns:

  1. Typical Aging (A1): This group exhibited minor brain atrophy, fewer cardiovascular genetic risk factors, and normal WMH levels. Their average brain age was slightly younger than their chronological age.
  2. Accelerated Aging Subgroup 1 (A2): Participants in this group had the highest and fastest-growing WMH levels, higher cardiovascular disease-associated genetic risk factors, and amyloid plaques in the brain. Their brain age was two to three years older than their chronological age.
  3. Accelerated Aging Subgroup 2 (A3): This subgroup showed more widespread brain atrophy, faster cognitive decline, moderate cardiovascular risk factors, and a brain age three to five years older than their chronological age.

The Power of AI in Brain Aging Research

The study leveraged machine learning to analyze over two decades of neuroimaging, clinical, and cognitive data from more than 27,000 participants. By harnessing the power of artificial intelligence, researchers were able to identify complex patterns in brain aging that were previously difficult to detect.

Three Brain Aging Trajectories Revealed

The AI analysis uncovered three distinct brain aging patterns:

  1. Typical Aging (A1):
    • Minor brain atrophy
    • Fewer cardiovascular genetic risk factors
    • Normal white matter hyperintensity (WMH) levels
    • Brain age slightly younger than chronological age
  2. Accelerated Aging Subgroup 1 (A2):
    • Highest and fastest-growing WMH levels
    • Higher cardiovascular disease-associated genetic risk factors
    • Presence of amyloid plaques in the brain
    • Brain age 2-3 years older than chronological age
  3. Accelerated Aging Subgroup 2 (A3):
    • More widespread brain atrophy
    • Faster cognitive decline
    • Moderate cardiovascular risk factors
    • Brain age 3-5 years older than chronological age

Implications and Future Directions

The findings from this study are a significant step towards better prediction and classification of brain aging and cognitive decline trajectories related to Alzheimer’s. The researchers aim to expand this work to more diverse participant samples and longer time periods to enable additional follow-up on physical and cognitive outcomes. This research was supported by various NIA grants, highlighting the importance of continued funding in this area.

AI and Brain Aging: Broader Context

The use of AI in brain aging research is not limited to this study. Various other research initiatives have demonstrated the potential of AI to revolutionize our understanding of brain aging and neurodegenerative diseases.

Drexel University’s AI Technique

Researchers at Drexel University’s Creativity Research Lab developed an AI technique to estimate brain age using electroencephalogram (EEG) brain scans. This method offers a less expensive and less invasive alternative to MRI, making early, regular screening for degenerative brain diseases more accessible. The AI model can estimate brain age based on EEG scans, providing a measure of general brain health and identifying premature brain aging, which can be caused by diseases, toxins, poor nutrition, or injuries .

Mount Sinai’s HistoAge Model

Mount Sinai researchers developed the HistoAge algorithm, which predicts age at death based on the cellular composition of human brain tissue specimens. This model demonstrated strong associations with cognitive impairment, cerebrovascular disease, and Alzheimer’s-type protein aggregation, offering a reliable metric for exploring neurodegenerative progression. The HistoAge model represents a new paradigm for assessing aging and neurodegeneration in human samples, providing more rigorous and unbiased metrics of cellular changes underlying degenerative diseases .

University of Southern California’s Neural Network

A neural network developed by researchers at the University of Southern California (USC) can predict participants’ ages from their brain MRIs with high accuracy. This model can identify areas of the brain aging in ways that reflect cognitive decline, potentially leading to Alzheimer’s. The AI model’s ability to detect subtle brain anatomy markers offers an unprecedented glimpse into human cognition and could pave the way for tailored interventions addressing unique aging patterns .

Expanding the Research

The researchers aim to build on this work by:

  • Analyzing more diverse participant samples
  • Extending the study over longer time periods
  • Conducting additional follow-up on physical and cognitive outcomes


The integration of AI in brain aging research is transforming our understanding of neurodegenerative diseases and cognitive decline. The NIA’s recent study, along with other pioneering research, underscores the potential of AI to improve prediction, diagnosis, and treatment of age-related brain disorders. Continued advancements in AI technology and expanded research efforts will be crucial in developing more effective strategies for maintaining brain health and mitigating the impact of neurodegenerative diseases.


  1. National Institute on Aging. AI data analysis maps three distinct brain aging patterns. NIA
  2. Drexel University. New AI-Technology Estimates Brain Age Using Low-Cost EEG Device. Drexel News
  3. Mount Sinai. AI Uncovers Secrets of Brain Aging. Neuroscience News
  4. University of Southern California. How old is your brain, really? Artificial intelligence knows. USC News
  5. UF Health. UF Health researchers find new method to use MRIs to delve into secrets of brain aging. UF Health News

Related Articles

Are you sure want to unlock this post?
Unlock left : 0
Are you sure want to cancel subscription?
Update Required Flash plugin