Characterizing Spontaneous Movements During Early Development of Mice
Mice start to actively sense their environment and react to it during the second week after birth, a critical period during which opening of eye and ear events and active whisking behavior emerge. During this period, cortical connectivity and excitability undergo major reorganization. Circuit formation and anatomical refinement of the primary sensory cortices during this period have been extensively studied, mostly focused on the developmental process of the feedforward ascending circuitry such as the thalamocortical circuit formation. However, little is known about the factors and developmental mechanisms that drive the specificity of corticocortical projections, especially cortical top-down (feedback) projections to primary sensory cortex and more importantly the related functional changes. We will investigate how the distinct subset of neurons in the neocortex that encode a behavior state emerges during this cortical development.
To determine the developmental emergence of the behavior state-dependent neurons in the neocortex, we will conduct longitudinal chronic calcium imaging in the primary somatosensory cortex to follow the same population of pyramidal neurons over time. Spontaneous body movements will be monitored using video recording. We choose the imaging period from the postnatal day 8 to p30. We are particularly interested in 1) when the correlated activity appears, 2) whether neuronal activity before the onset of whisking and coordinated movement can predict the subpopulation of the behavior-sate related neurons, and 3) what instructs the formation of the behavior state-sensitive subnetworks in the neocortex.
This BESIP project will focus on developing an analysis method of spontaneous movements using DeepLabCut and other methods. The goal is to automatically identify and classify sequence of body movements of 1-2 weeks old mice. This analysis of spontaneous body movement data will be correlated to cortical neuronal data which is acquired with a two-photon calcium imaging system.
This will be a hands-on software development project best suited for students with interests in Biomedical Engineering and/or Computer Science. Prior programming experience with MATLAB, Python, or other image processing and data analysis 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 video recording data of spontaneous movements together with movement detection data using piezoelectric transducer.
Pohida lab: 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.