# Markerless Tracking of 3D Posture to Reveal the Sensory Origins of Body Schema > **NIH NIH F32** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2022 · $69,802 ## Abstract The goal of this proposed research is to reveal the sensory origins underlying the body schema representation. Body schema is the brain's internal model of the body's spatial configuration. This internal representation is critical for sensorimotor processing, movement control, and self-awareness, and is continuously updated during movement. Body schema representations are disrupted when somatosensory input is lost. The first step toward discover the neural correlates of body schema is to uncover neural mechanisms that generate body posture representation. We hypothesize that sensory inputs from primary somatosensory cortex (S1) and secondary somatosensory cortex (S2) to the posterior parietal cortex (PPC) are transformed to construct a body posture representation. To delineate the mechanisms underlying the neural coding of body posture, this project will utilize large- scale monitoring, apply interventional tools, develop new data analysis tools, and integrate new approaches. Our approach is to perform large-scale electrophysiological recording and novel markerless tracking of 3D posture in freely moving mice. To track posture, the first aim is to adapt a markerless tracking pipeline comprised of a deep 3D convolutional neural network to process high-speed videography of mouse behavior from multiple cameras. The second aim is to perform large-scale recording of neurons in S1, S2, and PPC and use advanced computational approaches to determine which postural features best explain the activity of neurons in these cortical areas. Finally, the third aim is to use optogenetic and projection-specific manipulations to address the causal impact of proprioceptive inputs from S1 and S2 on coding of posture in PPC. This research promises to uncover how sensory inputs are involved in generating the body schema representation and guiding behavior. Extensive training will be required to carry out this project and achieve my goal of earning a tenure-track professor position. The rigorous methodological and intellectual environment in Dr. Fan Wang’s lab and the Duke Neurobiology community will advance my conceptual knowledge and technical skills. I will implement deep learning techniques through training and collaboration with specialists. I will learn new techniques by attending Neuropixel and computational neuroscience courses. Finally, I will develop my professional skills by frequent attendance of seminars, workshops, and meeting with a postdoctoral mentorship committee. The proposed project will be conducted in the Department of Neurobiology at the Duke University Medical Campus. This interdisciplinary community at Duke will bolster the research and training included in this application through frequent interaction with talented and collaborative faculty, organization of seminars and symposia, numerous opportunities to practice research talks and receive valuable feedback, formation of a personalized postdoctoral mentorship committee, extensive career and... ## Key facts - **NIH application ID:** 10410450 - **Project number:** 5F32MH122995-04 - **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY - **Principal Investigator:** Kyle Scott Severson - **Activity code:** F32 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $69,802 - **Award type:** 5 - **Project period:** 2020-06-01 → 2023-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10410450 ## Citation > US National Institutes of Health, RePORTER application 10410450, Markerless Tracking of 3D Posture to Reveal the Sensory Origins of Body Schema (5F32MH122995-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10410450. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*