AI-powered analysis accurately reflects risk of cognitive decline and Alzheimer’s disease based on brain age

“People age at different rates, and so do tissue types in the body. We know this colloquially when we say, ‘So-and-so is forty, but looks thirty. The same idea applies to the brain. The brain of a forty-year-old may look as ‘young’ as the brain of a thirty-year-old, or it may look as ‘old’ as that of a sixty-year-old.”

A more accurate alternative to existing methods

Irimia and his team collated the brain MRIs of 4,681 cognitively normal participants, some of whom went on to develop cognitive decline or Alzheimer’s disease later in life.

Using these data, they created an AI model called a neural network to predict participants’ ages from their brain MRIs. First, the researchers trained the network to produce detailed anatomic brain maps that reveal subject-specific patterns of aging. They then compared the perceived (biological) brain ages with the actual (chronological) ages of study participants. The greater the difference between the two, the worse the participants’ cognitive scores, which reflect Alzheimer’s risk

The results show that the team’s model can predict the true (chronological) ages of cognitively normal participants with an average absolute error of 2.3 years, which is about one year more accurate than an existing, award-winning model for brain age estimation that used a different neural network architecture.

“Interpretable AI can become a powerful tool for assessing the risk for Alzheimer’s and other neurocognitive diseases,” said Irimia, who also holds faculty positions with the USC Viterbi School of Engineering and USC Dornsife College of Letters, Arts and Sciences. “The earlier we can identify people at high risk for Alzheimer’s disease, the earlier clinicians can intervene with treatment options, monitoring, and disease management. What makes AI especially powerful is its ability to pick up on subtle and complex features of aging that other methods cannot and that are key in identifying a person’s risk many years before they develop the condition.”

Brains age differently according to sex

The new model also reveals sex-specific differences in how aging varies across brain regions. Certain parts of the brain age faster in males than in females, and vice versa.

Males, who are at higher risk of motor impairment due to Parkinson’s disease, experience faster aging in the brain’s motor cortex, an area responsible for motor function. Findings also show that, among females, typical aging may be relatively slower in the right hemisphere of the brain.

An emerging field of study shows promise for personalized medicine

Applications of this work extend far beyond disease risk assessment. Irimia envisions a world in which the novel deep learning methods developed as part of the study are used to help people understand how fast they are aging in general.

“One of the most important applications of our work is its potential to pave the way for tailored interventions that address the unique aging patterns of every individual,” Irimia said.

“Many people would be interested in knowing their true rate of aging. The information could give us hints about different lifestyle changes or interventions that a person could adopt to improve their overall health and well-being. Our methods could be used to design patient-centered treatment plans and personalized maps of brain aging that may be of interest to people with different health needs and goals.”

2 Comments

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2 responses to “AI-powered analysis accurately reflects risk of cognitive decline and Alzheimer’s disease based on brain age

  1. How interesting! There is actually a similar AI-analysis test on the market already–the NeuroQuant MRI. While the neurologist read the MRI as normal, the AI’s volumetric assessment suggested I have Alzheimer’s. Based on the areas of atrophy and edema, they can determine which type of Alzheimer’s disease one has.

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