AI and optogenetics enable precise Parkinson’s diagnosis and treatment in mice

Globally recognized figures Muhammad Ali and Michael J. Fox have long suffered from Parkinson’s disease. The disease presents a complex set of motor symptoms, including tremors, rigidity, bradykinesia, and postural instability. However, traditional diagnostic methods have struggled to sensitively detect changes in the early stages, and drugs targeting brain signal regulation have had limited clinical effectiveness.

Recently, Korean researchers successfully demonstrated the potential of a technology that integrates AI and optogenetics as a tool for precise diagnosis and therapeutic evaluation of Parkinson’s disease in mice. They have also proposed a strategy for developing next-generation personalized treatments.

A collaborative research team, comprising Professor Won Do Heo’s team from the Department of Biological Sciences, Professor Daesoo Kim’s team from the Department of Brain and Cognitive Sciences, and Director Chang-Jun Lee’s team from the Institute for Basic Science (IBS) Center for Cognition and Sociality, achieved a preclinical research breakthrough by combining AI analysis with optogenetics.

Their work simultaneously demonstrated the possibility of early and precise diagnosis and treatment in an animal model of Parkinson’s disease. The study, with Dr. Bobae Hyeon, a postdoctoral researcher at the KAIST Institute for Biological Science, as the first author, is published in the journal Nature Communications.

The research team created a Parkinson’s disease mouse model with two stages of severity. These were male mice with alpha-synuclein protein abnormalities, a standard model used to simulate human Parkinson’s disease for diagnostic and therapeutic research.

In collaboration with Professor Kim’s team at KAIST, they introduced AI-based 3D pose estimation for behavioral analysis. The team analyzed more than 340 behavioral features—such as gait, limb movements, and tremors—from the Parkinson’s mice and condensed them into a single metric: the AI-predicted Parkinson’s disease score (APS).

The analysis showed that the APS exhibited a significant difference from the control group as early as two weeks after the disease was induced. It also proved more sensitive in assessing the disease’s severity than traditional motor function tests. The study identified key diagnostic features, including changes in stride, asymmetrical limb movements, and chest tremors. The top 20 behavioral features included hand/foot asymmetry, changes in stride and posture, and an increase in high-frequency chest movement.

To confirm that these behavioral indicators were not just general motor decline but specific to Parkinson’s, the team applied the same analysis to a mouse model of Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, in collaboration with Director Lee’s team at IBS. Since both Parkinson’s and ALS cause motor function problems, if the APS simply reflected poor motor skills, a high score should have appeared in both diseases.

However, the analysis of the ALS animal model showed that despite a decline in motor function, the mice did not exhibit the high APS seen in the Parkinson’s model. Instead, their scores remained low, and their behavioral changes were distinctly different. This demonstrates that APS is directly related to specific, characteristic changes that only appear in Parkinson’s disease.

For treatment, the research team used optoRET, an optogenetics technology that precisely controls neurotrophic signals with light. This technique proved effective in the animal model, leading to smoother gait and limb movements and a reduction in tremors.

Specifically, a regimen of shining light on alternate days was found to be the most effective, and it also showed a tendency to protect dopamine-producing neurons in the brain.

Professor Won Do Heo of KAIST stated, “This is the first time in the world that a preclinical framework has been implemented that connects early diagnosis, treatment evaluation, and mechanism verification of Parkinson’s disease by combining AI-based behavioral analysis with optogenetics. This lays a crucial foundation for future personalized medicine and customized treatments for patients.”