Parkinson's Disease Drosophila Model

Building behavioral setup and developing Parkinson's disease model using cutting-edge neuroscience and machine learning techniques

Overview

During my research internship at Japan's premier comprehensive research institute, I worked under Dr. Fujiwara to explore the intricacies of Parkinson's disease using cutting-edge techniques combining behavioral neuroscience with machine learning.

Problem

Understanding the neural mechanisms underlying motor control deficits in Parkinson's disease requires sophisticated experimental setups and analysis tools. Traditional approaches to studying gait patterns provide limited insights into the complex neural dynamics.

Solution

Built a behavioral setup for freely walking drosophila using high-speed camera technology. Developed a Parkinson's disease model using the Split-gal4 technique to specifically inhibit dopaminergic neurons in the drosophila central complex. Analyzed complex gait patterns using Machine Learning tools including DeepLabCut for pose estimation and UMAP for dimensionality reduction.

Impact

• •Successfully built behavioral tracking setup using high-speed cameras
• •Developed novel Parkinson's disease model using Split-gal4 technique
• •Applied ML tools (DeepLabCut, Bonsai, UMAP) for gait analysis
• •Contributed to understanding of dopaminergic neuron function

Key Skills Applied