Subtle behavioral changes linked to early-stage Alzheimer’s disease can now be detected through artificial intelligence, offering new possibilities for earlier diagnosis and treatment evaluation. Using an innovative machine learning tool called VAME (Variational Animal Motion Embedding), researchers have discovered previously undetectable behavioral patterns that could serve as early indicators of the disease.
Published in Cell Reports | Estimated reading time: 4 minutes
A team of scientists at Gladstone Institutes has developed a groundbreaking video-based machine learning approach that can identify subtle behavioral changes in mice engineered to mimic Alzheimer’s disease. Their work reveals how artificial intelligence can spot irregular behaviors reflecting very early stages of brain dysfunction—changes that occur long before conventional methods can detect any signs of cognitive decline.
“We’ve shown the potential of machine learning to revolutionize how we analyze behaviors indicative of early abnormalities in brain function,” says Gladstone investigator Jorge Palop, PhD, senior author of the study. “We leveraged a valuable tool that opens the door to a more complete understanding of devastating brain disorders and how they begin.”
The scientists utilized VAME to analyze video footage of mice exploring an open arena. Unlike traditional behavioral tests that rely on predefined tasks, VAME’s deep learning platform could capture the full range of spontaneous behavioral changes—particularly those that emerge in the disease’s earliest stages.
In both humanized App knockin mice and transgenic mouse models of Alzheimer’s, the AI system identified increased levels of “disorganized behavior” as the animals aged. Mice exhibited unusual patterns and transitioned more frequently between different activities—behavioral changes that might be associated with memory and attention deficits.
Remarkably, when the researchers tested a potential therapeutic intervention targeting inflammation in the brain, the AI system could detect improvements in the mice’s behavioral patterns. This suggests that machine learning approaches may offer new ways to evaluate the effectiveness of potential Alzheimer’s treatments.
“Similar machine learning approaches could be used one day to study spontaneous behaviors in humans, potentially providing early diagnosis of neurological diseases,” says Stephanie Miller, PhD, first author of the study. “I envision this technology will be used to assess patients in the clinic and even in their homes. It gives scientists and doctors a way to solve the very hard problem of diagnosing preclinical stages of disease.”
Key Terms Glossary
- VAME (Variational Animal Motion Embedding): An AI-based tool that analyzes video footage to identify and categorize subtle behavioral patterns
- Disorganized behavior: Irregular patterns of activity and frequent transitions between different behaviors that may indicate early cognitive dysfunction
- Spontaneous behavior: Natural, unprompted activities, as opposed to responses to specific tasks or challenges
- Preclinical stage: The period before obvious symptoms appear when subtle changes are beginning to occur
Quick Quiz
What advantage does VAME have over traditional behavioral tests?
VAME can capture spontaneous behavioral changes without relying on predefined tasks, allowing it to detect subtle alterations that might be missed by conventional methods.
What key behavioral change did the AI system identify in the Alzheimer’s mouse models?
The system detected increased “disorganized behavior,” including unusual patterns and more frequent transitions between different activities.
How might this technology be used in the future?
The technology could potentially be adapted to study spontaneous behaviors in humans, helping to diagnose neurological diseases earlier and assess treatments’ effectiveness.
Why is early detection of Alzheimer’s important?
Early detection allows for earlier intervention and treatment, potentially slowing the disease’s progression before significant cognitive decline occurs.
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