AI Model Revolutionizes Brain Aging Detection and Cognitive Decline Prediction

AI can detect if your brain is aging too quickly - Earth.com

Aging affects every part of the body, but the brain holds the key to how well we navigate our later years. Some people maintain sharp cognition well into their 80s and 90s, while others experience rapid mental decline much earlier. Understanding why this happens has long been a challenge for scientists. A remarkable study has now introduced a revolutionary way to measure how fast the brain is aging with the help of artificial intelligence (AI). Unlike previous methods that provided a single estimate of brain age, this approach calculates the rate of decline over time. By tracking how quickly the brain changes between two MRI scans, researchers can determine an individual's "pace of aging." "This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic. Knowing how fast one's brain is aging can be powerful," said Andrei Irimia, an associate professor at the USC Leonard Davis School of Gerontology. Chronological age measures time since birth, but biological age reflects how fast an individual's body and brain are aging. Some people experience aging at a slower or faster rate than their peers. Earlier methods only provided a static snapshot of brain age, missing important information about ongoing changes. The new technique addresses this gap by analyzing two MRI scans from the same person at different times. By comparing these scans, researchers can calculate the rate of change and determine whether someone's brain is aging faster or slower than expected. "The pace of brain aging conveys the rate of aging-related alteration in neurobiological system integrity," wrote the researchers. "For example, faster pace reflects faster adverse cognitive changes contributing to morbidity and mortality." Some studies have attempted to measure biological aging through blood samples. However, this method does not provide an accurate picture of brain aging. "The barrier between the brain and the bloodstream prevents blood cells from crossing into the brain, such that a blood sample from one's arm does not directly reflect methylation and other aging-related processes in the brain," explained Professor Irimia. To overcome this challenge, researchers at the University of Southern California trained their AI model using MRI scans from thousands of cognitively normal adults. The model was then tested on independent groups, including patients with Alzheimer's disease. This new approach examines changes between two MRI scans over time. Instead of estimating brain age at a single point, the AI system determines how much aging has occurred between scans and calculates the rate of decline. The AI model achieved a mean absolute error of just 0.16 years when predicting brain aging in cognitively normal adults. In comparison, the best traditional model had an error of 1.85 years - more than ten times worse. Beyond tracking the pace of aging, the researchers identified key differences between sexes, age groups, and cognitive conditions. "The 3D-CNN also generates interpretable 'saliency maps,' which indicate the specific brain regions that are most important for determining the pace of aging," said Paul Bogdan, associate professor at the USC Viterbi School of Engineering. For women, brain aging was most prominent in regions such as the right precentral gyrus, superior parietal lobules, and precunei. Men, however, exhibited more aging-related changes in the left transverse frontopolar gyrus and right supramarginal gyrus. People in their 50s showed aging in different regions compared to those in their 70s. Younger individuals had more decline in the left lateral temporal lobe and right medial occipital lobe. Meanwhile, those in their 70s experienced faster aging in the right central and postcentral gyri. The study also found that individuals with faster brain aging had greater declines in cognitive function over time. "Rates of brain aging are correlated significantly with changes in cognitive function," Irimia said. "So, if you have a high rate of brain aging, you're more likely to have a high rate of degradation in cognitive function, including memory, executive speed, executive function, and processing speed." This was particularly evident in scores from the Alzheimer's Disease Assessment Scale (ADAS13). People whose brains were aging more quickly showed greater errors in cognitive tests, indicating a strong link between brain structure and cognitive function. Perhaps the most significant finding was that the pace of brain aging could predict future cognitive impairment. Among the study participants, those who later developed cognitive decline had a significantly higher rate of brain aging than those who remained cognitively normal. "The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline," Bogdan said. "Moreover, it demonstrates its applicability in both cognitively normal individuals and those with cognitive impairment." Early identification of at-risk individuals could allow for preventive measures, such as lifestyle changes or medical interventions, before symptoms appear. Irimia believes this model could help detect brain aging before cognitive symptoms emerge. With new Alzheimer's treatments available, early intervention may be crucial in improving outcomes. "One thing that my lab is very interested in is estimating risk for Alzheimer's; we'd like to one day be able to say, 'Right now, it looks like this person has a 30% risk for Alzheimer's.' We're not there yet, but we're working on it," Irimia said. "I think this kind of measure will be very helpful to produce variables that are prognostic and can help to forecast Alzheimer's risk. That would be really powerful, especially as we start developing potential drugs for prevention." Since men and women showed different patterns of brain aging, this model could also help researchers understand why they face different risks for neurodegenerative diseases. The study had some limitations. Because it calculates an average pace of aging over a set period, it may not capture recent changes in brain health. Additionally, the model performed better for cognitively normal individuals than for those with Alzheimer's, likely because it was trained on healthy adults. Future studies with larger and more diverse populations could improve the model's accuracy. However, even with its limitations, this research represents a major step forward in understanding how brain aging progresses over time. By focusing on how fast the brain is changing rather than just its current state, this research could lead to more personalized medical interventions. Maintaining brain health isn't just about age - it's about how quickly aging is taking place. The study is published in the journal Proceedings of the National Academy of Sciences. -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

New AI model measures how fast the brain ages

A new artificial intelligence model measures how fast a patient's brain is aging and could be a powerful new tool for understanding, preventing and treating cognitive decline and dementia, according to USC researchers. The first-of-its-kind tool can non-invasively track the pace of brain changes by analyzing magnetic resonance imaging (MRI) scans. Faster brain aging closely correlates with a higher risk of cognitive impairment, said Andrei Irimia, associate professor of gerontology, biomedical engineering, quantitative & computational biology and neuroscience at the USC Leonard Davis School of Gerontology and visiting associate professor of psychological medicine at King's College London. "This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic," he said. "Knowing how fast one's brain is aging can be powerful." Irimia is the senior author of the study that describes the new model and its predictive power; the study was published February 24, 2025 in Proceedings of the National Academy of Sciences. Biological brain age versus chronological age Biological age is distinct from an individual's chronological age, Irimia said. Two people who are the same age based on their birthdate can have very different biological ages due to how well their body is functioning and how "old" the body's tissues appear to be at a cellular level. Some common measures of biological age use blood samples to measure epigenetic aging and DNA methylation, which influences the roles of genes in the cell. However, measuring biological age from blood samples is a poor strategy for measuring the brain's age, Irimia explained. The barrier between the brain and the bloodstream prevents blood cells from crossing into the brain, such that a blood sample from one's arm does not directly reflect methylation and other aging-related processes in the brain. Conversely, taking a sample directly from a patient's brain is a much more invasive procedure, making it unfeasible to measure DNA methylation and other aspects of brain aging directly from living human brain cells. Previous research by Irimia and colleagues highlighted the potential of MRI scans to non-invasively measure the biological age of the brain. The earlier model used AI analysis to compare a patient's brain anatomy to data compiled from the MRI scans of thousands of people of various ages and cognitive health outcomes. However, the cross-sectional nature of analyzing one MRI scan to estimate brain age had major limitations, he said. While the previous model could, for instance, tell if a patient's brain was ten years "older" than their calendar age, it couldn't provide info on whether that additional aging occurred earlier or later in their life, nor could it indicate whether brain aging was speeding up. A more accurate picture of brain aging A newly developed three-dimensional convolutional neural network (3D-CNN) offers a more precise way to measure how the brain ages over time. Created in collaboration with Paul Bogdan, associate professor of electrical and computer engineering and holder of the Jack Munushian Early Career Chair at the USC Viterbi School of Engineering, the model was trained and validated on more than 3,000 MRI scans of cognitively normal adults. Unlike traditional cross-sectional approaches, which estimate brain age from one scan at a single time point, this longitudinal method compares baseline and follow-up MRI scans from the same individual. As a result, it more accurately pinpoints neuroanatomic changes tied to accelerated or decelerated aging. The 3D-CNN also generates interpretable "saliency maps," which indicate the specific brain regions that are most important for determining the pace of aging, Bogdan said. When applied to a group of 104 cognitively healthy adults and 140 Alzheimer's disease patients, the new model's calculations of brain aging speed closely correlated with changes in cognitive function tests given at both time points. "The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline," Bogdan said. "Moreover, it demonstrates its applicability in both cognitively normal individuals and those with cognitive impairment." He added that the model has the potential to better characterize both healthy aging and disease trajectories, and its predictive power could one day be applied to assessing which treatments would be more effective based on individual characteristics. "Rates of brain aging are correlated significantly with changes in cognitive function," Irimia said. "So, if you have a high rate of brain aging, you're more likely to have a high rate of degradation in cognitive function, including memory, executive speed, executive function, and processing speed. It's not only an anatomic measure; the changes we see in the anatomy are associated with changes we see in the cognition of these individuals." Looking ahead In the study, Irimia and coauthors also note how the new model was able to distinguish different rates of aging across various regions of the brain. Delving into these differences -including how they vary based on genetics, environment, and lifestyle factors -- could provide insight into how different pathologies develop in the brain, Irimia said. The study also demonstrated that the pace of brain aging in certain regions differed between the sexes, which might shed light onto why men and women face different risks for neurodegenerative disorders, including Alzheimer's, he added. Irimia said he is also excited about the potential for the new model to identify people with faster-than-normal brain aging before they show any symptoms of cognitive impairment. While new drugs targeting Alzheimer's have been introduced, their efficacy has been less than researchers and doctors have hoped for, potentially because patients might not be starting the drug until there is already a great deal of Alzheimer's pathology present in the brain, he explained. "One thing that my lab is very interested in is estimating risk for Alzheimer's; we'd like to one day be able to say, 'Right now, it looks like this person has a 30% risk for Alzheimer's.' We're not there yet, but we're working on it," Irimia said. "I think this kind of measure will be very helpful to produce variables that are prognostic and can help to forecast Alzheimer's risk. That would be really powerful, especially as we start developing potential drugs for prevention."

New AI tool measures brain aging speed and predicts cognitive health

University of Southern CaliforniaFeb 24 2025 A new artificial intelligence model measures how fast a patient's brain is aging and could be a powerful new tool for understanding, preventing and treating cognitive decline and dementia, according to USC researchers. The first-of-its-kind tool can non-invasively track the pace of brain changes by analyzing magnetic resonance imaging (MRI) scans. Faster brain aging closely correlates with a higher risk of cognitive impairment, said Andrei Irimia, associate professor of gerontology, biomedical engineering, quantitative & computational biology and neuroscience at the USC Leonard Davis School of Gerontology and visiting associate professor of psychological medicine at King's College London. "This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic," he said. "Knowing how fast one's brain is aging can be powerful." Irimia is the senior author of the study that describes the new model and its predictive power; the study was published February 24, 2025 in Proceedings of the National Academy of Sciences. Biological brain age versus chronological age Biological age is distinct from an individual's chronological age, Irimia said. Two people who are the same age based on their birthdate can have very different biological ages due to how well their body is functioning and how "old" the body's tissues appear to be at a cellular level. Some common measures of biological age use blood samples to measure epigenetic aging and DNA methylation, which influences the roles of genes in the cell. However, measuring biological age from blood samples is a poor strategy for measuring the brain's age, Irimia explained. The barrier between the brain and the bloodstream prevents blood cells from crossing into the brain, such that a blood sample from one's arm does not directly reflect methylation and other aging-related processes in the brain. Conversely, taking a sample directly from a patient's brain is a much more invasive procedure, making it unfeasible to measure DNA methylation and other aspects of brain aging directly from living human brain cells. Previous research by Irimia and colleagues highlighted the potential of MRI scans to non-invasively measure the biological age of the brain. The earlier model used AI analysis to compare a patient's brain anatomy to data compiled from the MRI scans of thousands of people of various ages and cognitive health outcomes. However, the cross-sectional nature of analyzing one MRI scan to estimate brain age had major limitations, he said. While the previous model could, for instance, tell if a patient's brain was ten years "older" than their calendar age, it couldn't provide info on whether that additional aging occurred earlier or later in their life, nor could it indicate whether brain aging was speeding up. A more accurate picture of brain aging A newly developed three-dimensional convolutional neural network (3D-CNN) offers a more precise way to measure how the brain ages over time. Created in collaboration with Paul Bogdan, associate professor of electrical and computer engineering and holder of the Jack Munushian Early Career Chair at the USC Viterbi School of Engineering, the model was trained and validated on more than 3,000 MRI scans of cognitively normal adults. Unlike traditional cross-sectional approaches, which estimate brain age from one scan at a single time point, this longitudinal method compares baseline and follow-up MRI scans from the same individual. As a result, it more accurately pinpoints neuroanatomic changes tied to accelerated or decelerated aging. The 3D-CNN also generates interpretable "saliency maps," which indicate the specific brain regions that are most important for determining the pace of aging, Bogdan said. When applied to a group of 104 cognitively healthy adults and 140 Alzheimer's disease patients, the new model's calculations of brain aging speed closely correlated with changes in cognitive function tests given at both time points. "The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline," Bogdan said. "Moreover, it demonstrates its applicability in both cognitively normal individuals and those with cognitive impairment." He added that the model has the potential to better characterize both healthy aging and disease trajectories, and its predictive power could one day be applied to assessing which treatments would be more effective based on individual characteristics. "Rates of brain aging are correlated significantly with changes in cognitive function," Irimia said. "So, if you have a high rate of brain aging, you're more likely to have a high rate of degradation in cognitive function, including memory, executive speed, executive function, and processing speed. It's not only an anatomic measure; the changes we see in the anatomy are associated with changes we see in the cognition of these individuals." Looking ahead In the study, Irimia and coauthors also note how the new model was able to distinguish different rates of aging across various regions of the brain. Delving into these differences -including how they vary based on genetics, environment, and lifestyle factors - could provide insight into how different pathologies develop in the brain, Irimia said. The study also demonstrated that the pace of brain aging in certain regions differed between the sexes, which might shed light onto why men and women face different risks for neurodegenerative disorders, including Alzheimer's, he added. Irimia said he is also excited about the potential for the new model to identify people with faster-than-normal brain aging before they show any symptoms of cognitive impairment. While new drugs targeting Alzheimer's have been introduced, their efficacy has been less than researchers and doctors have hoped for, potentially because patients might not be starting the drug until there is already a great deal of Alzheimer's pathology present in the brain, he explained. "One thing that my lab is very interested in is estimating risk for Alzheimer's; we'd like to one day be able to say, 'Right now, it looks like this person has a 30% risk for Alzheimer's.' We're not there yet, but we're working on it," Irimia said. "I think this kind of measure will be very helpful to produce variables that are prognostic and can help to forecast Alzheimer's risk. That would be really powerful, especially as we start developing potential drugs for prevention." University of Southern California

AI Model Predicts Brain Aging Speed to Detect Cognitive Decline Early - Neuroscience News

Summary: A new AI model can measure how fast a person's brain is aging using MRI scans, providing a powerful tool for detecting cognitive decline. Unlike previous methods, this model tracks brain aging over time, identifying regions most affected and correlating changes with cognitive function. Researchers found that faster brain aging strongly links to cognitive impairment, suggesting early intervention could help prevent neurodegenerative diseases. This breakthrough could lead to better diagnostics, personalized treatments, and earlier identification of Alzheimer's risk. A new artificial intelligence model measures how fast a patient's brain is aging and could be a powerful new tool for understanding, preventing and treating cognitive decline and dementia, according to USC researchers. The first-of-its-kind tool can non-invasively track the pace of brain changes by analyzing magnetic resonance imaging (MRI) scans. Faster brain aging closely correlates with a higher risk of cognitive impairment, said Andrei Irimia, associate professor of gerontology, biomedical engineering, quantitative & computational biology and neuroscience at the USC Leonard Davis School of Gerontology and visiting associate professor of psychological medicine at King's College London. "This is a novel measurement that could change the way we track brain health both in the research lab and in the clinic," he said. "Knowing how fast one's brain is aging can be powerful." Irimia is the senior author of the study that describes the new model and its predictive power; the study was published February 24, 2025 in Proceedings of the National Academy of Sciences. Biological brain age versus chronological age Biological age is distinct from an individual's chronological age, Irimia said. Two people who are the same age based on their birthdate can have very different biological ages due to how well their body is functioning and how "old" the body's tissues appear to be at a cellular level. Some common measures of biological age use blood samples to measure epigenetic aging and DNA methylation, which influences the roles of genes in the cell. However, measuring biological age from blood samples is a poor strategy for measuring the brain's age, Irimia explained. The barrier between the brain and the bloodstream prevents blood cells from crossing into the brain, such that a blood sample from one's arm does not directly reflect methylation and other aging-related processes in the brain. Conversely, taking a sample directly from a patient's brain is a much more invasive procedure, making it unfeasible to measure DNA methylation and other aspects of brain aging directly from living human brain cells. Previous research by Irimia and colleagues highlighted the potential of MRI scans to non-invasively measure the biological age of the brain. The earlier model used AI analysis to compare a patient's brain anatomy to data compiled from the MRI scans of thousands of people of various ages and cognitive health outcomes. However, the cross-sectional nature of analyzing one MRI scan to estimate brain age had major limitations, he said. While the previous model could, for instance, tell if a patient's brain was ten years "older" than their calendar age, it couldn't provide info on whether that additional aging occurred earlier or later in their life, nor could it indicate whether brain aging was speeding up. A more accurate picture of brain aging A newly developed three-dimensional convolutional neural network (3D-CNN) offers a more precise way to measure how the brain ages over time. Created in collaboration with Paul Bogdan, associate professor of electrical and computer engineering and holder of the Jack Munushian Early Career Chair at the USC Viterbi School of Engineering, the model was trained and validated on more than 3,000 MRI scans of cognitively normal adults. Unlike traditional cross-sectional approaches, which estimate brain age from one scan at a single time point, this longitudinal method compares baseline and follow-up MRI scans from the same individual. As a result, it more accurately pinpoints neuroanatomic changes tied to accelerated or decelerated aging. The 3D-CNN also generates interpretable "saliency maps," which indicate the specific brain regions that are most important for determining the pace of aging, Bogdan said. When applied to a group of 104 cognitively healthy adults and 140 Alzheimer's disease patients, the new model's calculations of brain aging speed closely correlated with changes in cognitive function tests given at both time points. "The alignment of these measures with cognitive test results indicates that the framework may serve as an early biomarker of neurocognitive decline," Bogdan said. "Moreover, it demonstrates its applicability in both cognitively normal individuals and those with cognitive impairment." He added that the model has the potential to better characterize both healthy aging and disease trajectories, and its predictive power could one day be applied to assessing which treatments would be more effective based on individual characteristics. "Rates of brain aging are correlated significantly with changes in cognitive function," Irimia said. "So, if you have a high rate of brain aging, you're more likely to have a high rate of degradation in cognitive function, including memory, executive speed, executive function, and processing speed. It's not only an anatomic measure; the changes we see in the anatomy are associated with changes we see in the cognition of these individuals." Looking ahead In the study, Irimia and coauthors also note how the new model was able to distinguish different rates of aging across various regions of the brain. Delving into these differences -including how they vary based on genetics, environment, and lifestyle factors - could provide insight into how different pathologies develop in the brain, Irimia said. The study also demonstrated that the pace of brain aging in certain regions differed between the sexes, which might shed light onto why men and women face different risks for neurodegenerative disorders, including Alzheimer's, he added. Irimia said he is also excited about the potential for the new model to identify people with faster-than-normal brain aging before they show any symptoms of cognitive impairment. While new drugs targeting Alzheimer's have been introduced, their efficacy has been less than researchers and doctors have hoped for, potentially because patients might not be starting the drug until there is already a great deal of Alzheimer's pathology present in the brain, he explained. "One thing that my lab is very interested in is estimating risk for Alzheimer's; we'd like to one day be able to say, 'Right now, it looks like this person has a 30% risk for Alzheimer's.' We're not there yet, but we're working on it," Irimia said. "I think this kind of measure will be very helpful to produce variables that are prognostic and can help to forecast Alzheimer's risk. That would be really powerful, especially as we start developing potential drugs for prevention." Along with Irimia and Bogdan, the study's authors included first author Chenzhong Yin and Heng Ping of the USC Viterbi School of Engineering and Phoebe E. Imms, Nahian F. Chowdhury, Nikhil N. Chaudhari, and Haoqing Wang of the USC Leonard Davis School of Gerontology. Funding: Support for the study came from the National Institutes of Health (NIH) under grants R01 NS 100973, RF1 AG 082201, and R01 AG 079957; the Department of Defense under contract W81XWH-18-1-0413; the National Science Foundation under CAREER Award CPS/CNS-1453860, grants MCB-1936775 and CNS-1932620; the U.S. Army Research Office under grant W911NF-23-1-0111; DARPA under a Young Faculty Award and under Director Award N66001-17-1-4044; an Intel Faculty Award; Northtrop Grumman; the Hanson-Thorell Research Scholarship Fund; the Undergraduate Research Associate Program; the Center for Undergraduate Research in Viterbi Engineering (CURVE) at USC; and anonymous donors. Deep learning to quantify the pace of brain aging in relation to neurocognitive changes Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood-brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer's disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM's ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals' rates of adverse cognitive change with age.