Subtle signs of Alzheimer’s disease can appear long before a formal diagnosis, often manifesting as irregular behaviors that signal early brain dysfunction. However, until now, scientifically measuring these early behavioral changes has been challenging, even in animal studies. A new study published in Cell Reports introduces a groundbreaking approach using machine learning to detect early signs of Alzheimer’s in mice, offering new insights into the early stages of the disease and potential treatment strategies.
New Machine Learning Approach Uncovers Early Disease Signs
Researchers at Gladstone Institutes have developed a video-based machine learning tool that can identify previously undetectable behavioral changes in mice engineered to simulate key aspects of Alzheimer’s. The tool, called VAME (Variational Animal Motion Embedding), analyzes video footage of mice exploring an open arena, pinpointing subtle behavior patterns that may be overlooked through conventional observation methods. This study marks a significant step forward in tracking neurological diseases earlier than current diagnostic techniques allow.
“We’ve demonstrated how machine learning can revolutionize the way we detect early abnormalities in brain function,” said Dr. Jorge Palop, a Gladstone investigator and senior author of the study. “This tool opens the door to a more comprehensive understanding of how brain disorders begin and how they evolve.”
Tracking Disorganized Behavior
VAME’s deep learning platform offers a novel approach compared to traditional behavioral tests in mice, which often focus on structured tasks. These conventional tests, while useful, fail to capture the full spectrum of spontaneous behavioral changes, particularly in the early stages of disease. Dr. Stephanie Miller, a staff scientist at Gladstone and first author of the study, explained that existing tests are limited in scalability and require time-intensive methods.
In the Gladstone study, VAME was used to assess two distinct mouse models that replicate different aspects of Alzheimer’s. The machine learning tool detected a marked increase in “disorganized behavior” as the mice aged, such as erratic transitions between activities—behaviors often linked to memory and attention deficits. This approach offers a more precise and comprehensive way of monitoring the disease’s progression in animal models.
“Machine learning could one day be used to study spontaneous behaviors in humans, offering an early diagnosis for neurological diseases,” Dr. Miller said. “Smartphone-quality video is sufficient for VAME analysis, making it feasible to assess patients in both clinical settings and their homes. This technology may solve the challenge of diagnosing the preclinical stages of neurological disorders.”
Exploring Potential Treatments for Alzheimer’s
In addition to its potential for early diagnosis, the Gladstone team used VAME to test a therapeutic strategy aimed at mitigating Alzheimer’s-related behaviors in mice. The treatment was based on the work of Gladstone investigator Dr. Katerina Akassoglou, who discovered that a blood-clotting protein called fibrin contributes to neuroinflammation when it leaks into the brain through damaged blood vessels. By blocking fibrin’s toxic effects, Akassoglou’s lab has shown that it’s possible to prevent cognitive decline and protect against Alzheimer’s in animal models.
The researchers used this knowledge to genetically alter the mice, blocking fibrin’s inflammatory effects in the brain. The intervention led to a reduction in abnormal behaviors associated with Alzheimer’s, providing further evidence that fibrin and neuroinflammation are key drivers of the disease.
“It was highly encouraging to see that blocking fibrin’s inflammatory activity reduced virtually all the spontaneous behavioral changes in the Alzheimer’s mice,” said Dr. Akassoglou. “This highlights the potential of targeting neuroinflammation as a therapeutic strategy for Alzheimer’s.”
A Powerful Tool for Research and Treatment Development
The success of this study underscores the power of machine learning in neurological research. It offers an unbiased, efficient way to evaluate potential treatments in animal models and could eventually become an invaluable tool in clinical settings.
As VAME technology evolves, Dr. Palop and Dr. Miller are collaborating with other teams at Gladstone to apply it to additional behavioral studies related to neurological diseases. “Our goal is to make this tool and similar approaches more accessible to researchers and clinicians,” Dr. Miller said. “By streamlining the process of identifying disease markers and testing new treatments, we hope to accelerate the development of effective therapies for neurological disorders.”
This research represents a promising step toward early diagnosis and treatment of Alzheimer’s disease, potentially transforming how the medical community approaches these devastating conditions.
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