Artificial Intelligence Model Predicts Cognitive Diseases Using Brain Age
“Our study harnesses the power of deep learning to identify areas of the brain that are aging in ways that reflect a cognitive decline that may lead to Alzheimer’s,” said Andrei Irimia, assistant professor of gerontology, biomedical engineering, quantitative and computational biology and neuroscience at the USC Leonard Davis School of Gerontology and corresponding author of the study.
“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 or function as ‘young’ as the brain of a thirty-year-old, or it may look or function as ‘old’ as that of a sixty-year-old.”
Lower Cognitive Scores Increase Risk of Alzheimer’s Disease
Irimia and his colleagues compiled the brain MRIs of 4,681 cognitively normal people, some of whom later developed cognitive decline or Alzheimer’s disease.
Novel Artificial Intelligence Model to Assess Risk of Neurodegenerative Diseases
They constructed an AI model called a neural network using these data to predict participants’ ages from brain MRIs. To begin, the researchers trained the network to generate comprehensive anatomic brain maps that indicate subject-specific aging processes. They then compared the perceived (biological) brain ages of research participants to their actual (chronological) ages. The wider the disparity between the two, the lower the participant’s cognitive scores, which represent their risk of Alzheimer’s disease.
According to the findings, 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 approximately 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.”
Gender Disparity in Brain Aging
The new model also reveals gender disparities in how aging affects different brain regions. Males age faster than females in certain areas of the brain, and vice-versa.
Males, who are at a higher risk of motor impairment due to Parkinson’s disease, age quicker in the motor cortex of the brain, which is responsible for motor function. The findings also suggest that ordinary aging may be slower in the right hemisphere of the brain in women.
Can Algorithms Predict Aging
This research has far-reaching implications beyond illness risk assessment. Irimia envisions a day in which the study’s revolutionary deep learning algorithms are utilized to assist people to understand how quickly 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.”
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