{"id":732,"date":"2023-06-22T00:53:33","date_gmt":"2023-06-22T00:53:33","guid":{"rendered":"https:\/\/gero.usc.edu\/labs\/irimialab\/how-old-is-your-brain-really-artificial-intelligence-knows\/"},"modified":"2023-06-22T00:53:33","modified_gmt":"2023-06-22T00:53:33","slug":"how-old-is-your-brain-really-artificial-intelligence-knows","status":"publish","type":"post","link":"https:\/\/gero.usc.edu\/labs\/irimialab\/how-old-is-your-brain-really-artificial-intelligence-knows\/","title":{"rendered":"How old is your brain, really? Artificial intelligence knows"},"content":{"rendered":"

The human brain holds many clues about a person\u2019s long-term health \u2014 in fact, research shows that a person\u2019s brain age is a more useful and accurate predictor of health risks and future disease than their birthdate. A new artificial intelligence model that analyzes MRI brain scans developed by USC researchers could be used to accurately capture cognitive decline linked to neurodegenerative diseases like Alzheimer\u2019s much earlier than previous methods.\u00a0<\/span>\u00a0<\/span><\/p>\n

Brain aging is considered a reliable biomarker for neurodegenerative disease risk. Such risk increases when a person\u2019s brain exhibits features that appear \u201colder\u201d than expected for someone of that person\u2019s age. By tapping into the deep learning capability of the team\u2019s novel AI model to analyze the scans, the researchers can detect subtle brain anatomy markers that are otherwise very difficult to detect and that correlate with cognitive decline. Their findings, recently published in the journal\u00a0<\/span>Proceedings of the National Academy of Sciences<\/a>, offer an unprecedented glimpse into human cognition<\/span>.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cOur 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\u2019s,\u201d said\u00a0<\/span>Andrei Irimia<\/span><\/a>, assistant professor of gerontology, biomedical engineering and neuroscience at the USC Leonard Davis School of Gerontology and corresponding author of the study.<\/span><\/p>\n

\u201cPeople age at different rates, and so do tissue types in the body,\u201d Irimia said. \u201cWe know this colloquially when we say, \u2018So-and-so is 40, but looks 30.\u2019 The same idea applies to the brain. The brain of a 40-year-old may look as \u2018young\u2019 as the brain of a 30-year-old, or as \u2018old\u2019 as that of a 60-year-old.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n

Brain aging: A more accurate alternative to existing methods\u00a0<\/span>\u00a0<\/span><\/h2>\n

Researchers collated the brain MRIs of 4,681 cognitively normal participants, some of whom went on to develop cognitive decline or Alzheimer\u2019s disease later in life.\u00a0<\/span>\u00a0<\/span><\/p>\n

The researchers utilized a neural network developed by<\/span>\u00a0<\/span>Paul Bogdan<\/a><\/span>, associate professor of electrical and computer engineering at the USC Viterbi School of Engineering, and members of his<\/span>\u00a0<\/span>Cyber Physical Systems<\/a><\/span>\u00a0<\/span>lab group. This neural network \u2014 an AI model that \u2018learns\u2019 through intensive, complex data analysis \u2014 allowed the research team to predict participants\u2019 ages from their brain MRIs:<\/span>\u00a0<\/span>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\u2019 cognitive scores, which reflect Alzheimer\u2019s risk.<\/span>\u00a0<\/span><\/p>\n

The results show that the team\u2019s 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. <\/span>\u00a0<\/span><\/p>\n

\n
\n

Researchers collated the brain MRIs of 4,681 cognitively normal participants, some of whom went on to develop cognitive decline or Alzheimer\u2019s disease later in life (illustration courtesy of the Irimia Laboratory).<\/p>\n<\/div>\n<\/div>\n

\u201cInterpretable AI can become a powerful tool for assessing the risk for Alzheimer\u2019s and other neurocognitive diseases,\u201d said Irimia, who also holds faculty positions with the USC Viterbi School of Engineering and\u00a0<\/span>the\u00a0<\/span>USC Dornsife College of Letters, Arts and Sciences.<\/span>\u00a0<\/span><\/p>\n

\u201cThe earlier we can identify people at high risk for Alzheimer\u2019s disease, the earlier clinicians can intervene with treatment options, monitoring<\/span>\u00a0and disease management.<\/span>\u201d<\/span><\/p>\n

Brain aging: differences according to sex\u00a0<\/span>\u00a0<\/span><\/h2>\n

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.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

Males, who are at higher risk of motor impairment due to Parkinson\u2019s disease, experience faster aging in the brain\u2019s 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.\u00a0<\/span>\u00a0<\/span><\/p>\n

An emerging field of study shows promise for personalized medicine\u00a0<\/span>\u00a0<\/span><\/h2>\n

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.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cOne 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,\u201d Irimia said.\u00a0<\/span>\u00a0<\/span><\/p>\n

\u201cMany 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.\u201d<\/span>\u00a0<\/span><\/p>\n

The post How old is your brain, really? Artificial intelligence knows<\/a> appeared first on USC Leonard Davis School of Gerontology<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

The human brain holds many clues about a person\u2019s long-term health \u2014 in fact, research shows that a person\u2019s brain age is a more useful and accurate predictor of health risks and future disease than their birthdate. A new artificial intelligence model that analyzes MRI brain scans developed by USC researchers could be used to Read More<\/a><\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-732","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"acf":[],"yoast_head":"\nHow old is your brain, really? 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