Machine learning reveals behaviors linked with early Alzheimer's, points to new treatments

Originally published at MedicalXpress by Gladstone Institutes, on November 26, 2024


Aged AppNL-G-F mice show robust AD-related pathology and mild impairments in the Morris water maze. Credit: Cell Reports (2024). DOI: 10.1016/j.celrep.2024.114870

Subtle signs of Alzheimer's disease can emerge decades before a diagnosis—often in the form of irregular behaviors that reflect very early stages of brain dysfunction. But until now, identifying and measuring these slight behavioral changes in a scientific way hasn't been feasible, not even when studying Alzheimer's in mice.

In a study published in Cell Reports, a team of scientists at Gladstone Institutes used a new video-based machine learning tool to pinpoint otherwise-undetectable signs of early disease in mice that were engineered to mimic key aspects of Alzheimer's. Their work sheds light on a new strategy for identifying neurological disease earlier than currently possible and tracking how it develops over time.

"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, Ph.D., 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 used a machine learning platform called VAME, short for "Variational Animal Motion Embedding," to analyze video footage of mice exploring an open arena. The open-source tool identified subtle behavioral patterns captured on camera—changes that might not be noticed by simply looking at the mice.

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