Simultaneous Detection and Quantification of Age-Dependent Dopamine Release

Dopamine (DA) is a key biomarker for neurodegenerative diseases such as Parkinson’s. However, detailed insights into how DA release in the brain changes with aging remain challenging. This study presents a machine learning framework to automatically detect and quantify dopamine (DA) release using the near-infrared catecholamine nanosensors (nIRCats) dataset of acute mouse brain tissue across three age groups (4, 8.5, and 12 weeks), focusing on the dorsolateral (DLS) and dorsomedial striatum (DMS). 251 image frames from the dataset were analyzed to extract features for training a CatBoost regression model. The model achieved validation Mean Squared Error (MSE) of 0.004 and R² value of 0.79. When the acceptable prediction range was expanded to include values within ±10% of the actual DA release and mouse age, model performance improved to a validation MSE of 0.001 and R² value of 0.97. To enhance speed while maintaining much of the predictive accuracy, the model was distilled into a kernelized Ridge regression model. These results demonstrate that the proposed approach can accurately and automatically predict spatial and age-dependent dopamine dynamics; a crucial requirement for optimizing deep brain stimulation therapies for neurodegenerative disorders such as Parkinson’s disease (PD) and depression.