Machine Learning Uncovers Early Alzheimer’s Behaviors: Paving the Way for Innovative Treatments
Scientists have found early signs of Alzheimer’s disease can appear decades before diagnosis. These signs often manifest as subtle behavioral changes linked to early brain dysfunction. Traditional methods for detecting these changes have not been effective, even in mouse studies.
A recent study by scientists at Gladstone Institutes published in Cell Reports introduced a video-based machine learning tool called VAME (Variational Animal Motion Embedding). This tool helps identify early indicators of Alzheimer’s in specially engineered mice.
The researchers used VAME to analyze video footage of mice in an open area. Unlike typical tests that require mice to perform specific tasks, VAME noted spontaneous behavioral patterns that might go unnoticed otherwise. This approach offers a broader perspective on behavioral changes due to neurological diseases, particularly in their initial stages.
In the study, two types of Alzheimer’s mice displayed increased “disorganized behavior” as they aged. These behaviors included unusual activity patterns and frequent shifts between different activities, which relate to memory and attention problems.
Dr. Stephanie Miller highlighted that similar machine learning methods might one day assist in diagnosing neurological diseases in humans using smartphones. This technology could allow assessments in clinics and homes, aiding in the early detection of diseases.
Furthermore, the team investigated the effects of a potential Alzheimer’s treatment using VAME. They focused on a blood-clotting protein, fibrin, which causes toxic effects in the brain. By genetically blocking fibrin’s harmful actions, the scientists found a reduction in abnormal behaviors linked to Alzheimer’s in mice.
Dr. Katerina Akassoglou commented that inhibiting fibrin’s inflammatory effects significantly decreased behavioral changes in the Alzheimer’s mice. Machine learning can objectively assess potential treatments and may become a crucial clinical tool in the future.
The study represents a significant step in understanding and diagnosing Alzheimer’s earlier. The Gladstone team aims to make VAME more accessible for future neurological studies, helping speed up the development of new treatments.