New Machine Learning Tool Detects Early Alzheimer's Signs in Mice

In a **groundbreaking study** published in *Cell Reports*, scientists at the Gladstone Institutes have leveraged a new video-based machine learning tool, VAME (Variational Animal Motion Embedding), to identify early, otherwise undetectable signs of Alzheimer's disease in mice. These subtle behavioral patterns, often missed through traditional testing, provide a **promising avenue for early diagnosis** and tracking the progression of brain dysfunctions. The tool analyzes spontaneous behavior in mice, revealing significantly increased disorganized actions, potentially linked to memory and attention deficits. The study involved evaluating two types of genetically engineered mice and found increased disorganization with age. In addition to detecting early signs, the researchers tested a therapeutic intervention that blocks fibrin, a blood-clotting protein that exacerbates neural inflammation once leaked into the brain. This approach successfully reduced abnormal behavior in the Alzheimer's mouse models, indicating a potential new treatment pathway. Highlighting VAME's potential, the researchers suggest that similar methods could be adapted for human use, potentially offering a way to diagnose early neurological diseases non-invasively, even using smartphone-quality video. The research team, including key contributors Stephanie Miller, PhD, and Jorge Palop, PhD, aims to make this tool accessible for broader scientific and clinical applications. This could accelerate the development of new treatments for neurodegenerative diseases, ultimately transforming clinical practices.