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SAN FRANCISCO — Could a computer detect the first subtle signs of Alzheimer’s disease long before doctors diagnose the patient? Scientists have developed a cutting-edge technology that could transform how we identify Alzheimer’s years before traditional symptoms become obvious. Using nothing more than video footage of mice, researchers at Gladstone Institutes have created a machine learning tool (a form of AI) that can detect subtle behavioral changes that might signal the earliest whispers of brain dysfunction.
Imagine a world where a simple video recording could reveal the first, nearly invisible signs of a devastating neurological disease. That’s precisely the promise of VAME (Variational Animal Motion Embedding), a sophisticated computer algorithm that can spot behavioral irregularities invisible to the human eye.
“We’ve shown the potential of machine learning to revolutionize how we analyze behaviors indicative of early abnormalities in brain function,” says Dr. Jorge Palop, the study’s senior author, in a media release.
Unlike traditional medical tests that require complex tasks or expensive equipment, this technology can work with smartphone-quality video.
Published in the journal Cell Reports, the researchers studied two groups of genetically modified mice designed to simulate different aspects of Alzheimer’s disease. Instead of forcing the mice through predetermined tests, the team simply recorded their natural movements in an open arena. The machine learning tool then analyzed these recordings, revealing fascinating insights.
What did VAME discover?
As the mice aged, the system detected a significant increase in “disorganized behavior.” This doesn’t mean the mice were simply moving differently — they were showing more erratic patterns of activity, frequently switching between tasks in ways that might indicate emerging memory and attention problems.
“I envision this technology will be used to assess patients in the clinic and even in their homes,” explains Dr. Stephanie Miller, the study’s first author. “It gives scientists and doctors a way to solve the very hard problem of diagnosing preclinical stages of disease.”
The study didn’t stop at detection. The researchers also tested a potential treatment approach by blocking a specific blood-clotting protein called fibrin, which previous research suggested might contribute to brain inflammation. Remarkably, this intervention dramatically reduced the abnormal behavioral changes in the Alzheimer’s mice.
“It was highly encouraging to see that blocking fibrin’s inflammatory activity in the brain reduced virtually all of the spontaneous behavioral changes in Alzheimer’s mice,” notes Dr. Katerina Akassoglou, another researcher involved in the study.
While the research is preliminary and conducted on mice, it represents a potentially revolutionary approach to understanding and potentially treating Alzheimer’s. By catching the disease’s earliest signals, doctors might one day be able to intervene much earlier, potentially slowing or preventing cognitive decline.
The team’s ultimate goal is clear. As Dr. Miller puts it, her aim is “to make this tool and similar approaches more accessible to biologists and clinicians in order to shorten the time it takes to develop powerful new medicines.”
Paper Summary
Methodology
The researchers used an advanced machine-learning platform called VAME (Variational Animal Motion Embedding) to observe and analyze spontaneous behaviors in genetically engineered Alzheimer’s disease (AD) mouse models. These models mimic human AD symptoms, such as neuroinflammation and amyloid buildup. The mice were recorded exploring an open arena for 25 minutes.
Using high-frame-rate videography, the VAME platform segmented mouse movements into distinct patterns or “motifs” that reflect different behaviors. The study compared behaviors across different mouse groups, examining age-related changes, genetic differences, and responses to therapeutic interventions targeting neuroinflammation.
Key Results
The study found that mice genetically modified to replicate Alzheimer’s showed significant differences in how they moved and behaved compared to healthy mice. As they aged, these changes became more pronounced. For instance, the Alzheimer’s model mice showed increased randomness in their movements and had trouble maintaining normal activity patterns. When treated to block inflammation caused by a specific protein (fibrinogen), many of these abnormalities were reduced. This suggests that inflammation plays a big role in driving these behavioral changes.
Study Limitations
The study focused only on mice, which limits how well the findings apply to humans. The researchers also didn’t assess other factors, like cognitive functions or brain activity, in direct connection to the observed behaviors. Additionally, the study couldn’t definitively determine if the behavior changes were directly tied to Alzheimer’s progression or if they were simply associated with the observed symptoms.
Discussion & Takeaways
The findings highlight how subtle changes in everyday behavior, like increased randomness or inability to sustain normal activity, could signal the early stages of Alzheimer’s. The research also emphasizes the role of inflammation in driving these changes and suggests that targeting inflammation could be an effective treatment approach. Furthermore, the VAME platform proved more precise than traditional methods, offering a potential tool for earlier and more accurate diagnosis of Alzheimer’s in preclinical stages.
Funding & Disclosures
This study was funded by several U.S. National Institutes of Health (NIH) grants, including RF1AG062234 and R01AG073082. European funding came from organizations like the German DFG Collaborative Research Center. The lead researchers disclosed affiliations, including a connection to Therini Bio, a company focusing on inflammation-related therapeutics. All other contributions and data are openly accessible under the CC BY license.
