Decoding neuronal activity patterns from large datasets
Our understanding of cortical information processing and function in behavior is limited. This is due, at least in part, to the lack of fundamental knowledge on single neuron-level input and output connectivity. Notably, the relationship between connectivity and function remains largely unexplored.
To tackle this problem, we use two-photon imaging to the simultaneous capture the activity of hundreds of cells in awake behaving mice. The fundamental step in deciphering cortical circuits is to comprehend neuronal activity and behavioral patterns. Modern techniques for monitoring neuronal activity, such as two-photon imaging of genetically-encoded fluorescent activity indicators, allow for a relatively large spatiotemporal resolution and result in large datasets, posing computational challenges that include single cell isolation, reduction of dimensions, network analysis and behavioral predictions.
This summer project will focus on developing image-processing algorithms and data analysis to automatically identify and trace cell and axon activity in images captured with our two-photon imaging system. Under the guidance of their engineering mentors, the BESIP student will be responsible for designing methods using image processing techniques such as image registration, machine learning, thresholding, connected component analysis, and statistical analysis to create robust segmentation and data analysis algorithms. The primary objectives are the development and implementation of methods for:
- Image registration and segmentation
- Estimation of spike trains from fluorescence time-series, supported by experimental data
- Statistical analysis of datasets
This will be a hands-on software development project best suited for students with interests in Biomedical Engineering, Electrical Engineering, and/or Computer Science. Prior programming experience with MATLAB or other similar image processing and data analysis programming environments is preferred. Working closely with an interdisciplinary team, the student will gain valuable experience with multiple procedures and technologies including animal research, two-photon imaging, scientific programming, noise filtering, machine learning, and image acquisition.
Lee lab: Lee lab will provide calcium imaging data and interact with a student to develop a pipeline for the imaging data analysis.
Pohida group: Provides electrical, electronic, electro-optical, mechanical, computer, and software engineering expertise to NIH projects that require in-house technology development. Collaborations involve advanced signal transduction and data acquisition; real-time signal and image processing; control and monitoring systems (e.g., robotics and process automation); and rapid prototype development. Collaborations result in the design of first-of-a-kind biomedical/clinical research systems, instrumentation, and methodologies.