# Research Intern Positions: Mapping Brainstem Respiratory Circuits with Novel Sodium fMRI Research Intern Positions: Mapping Brainstem Respiratory Circuits with Novel Sodium fMRI Prof. Xin Yu, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medicdal School and Massachusetts General Hospital Fall 2026 Our laboratory develops advanced MRI and multimodal neuroimaging methods to investigate how neuronal activity is organized across the brain. We recently developed neuronal activity-related sodium fMRI (NARS-fMRI), a new 23Na-based quantum ionic fMRI approach designed to detect rapid activity-related changes that are distinct from conventional hemodynamic fMRI. This project will apply NARS-fMRI to investigate brainstem circuits that generate and regulate respiration. Respiratory control depends on interactions among multiple small and deeply located brainstem nuclei, yet existing methods have limited ability to map how activity propagates across these structures in the intact brain. Using our 15.2 Tesla preclinical MRI system, we aim to characterize respiration-linked neural activity patterns and functional interactions across brainstem circuits. We plan to recruit multiple research interns with interests in either experimental neuroimaging or computational/AI-based image analysis. **Responsibilities:** Interns may focus on one or both of the following areas: *Experimental and multimodal fMRI:* Assist with preclinical high-field MRI experiments and multimodal data acquisition combining NARS-fMRI with respiratory monitoring, fiber photometry, and other physiological or neural measurements. Depending on experience, training, and institutional requirements, students may gain exposure to RF-coil preparation and implantation procedures, fiber-photometry experiments, animal preparation, and multimodal fMRI acquisition. All animal-related work will be conducted under appropriate supervision and after completion of required training. *Computational imaging and AI-based analysis:* Process and analyze high-temporal-resolution NARS-fMRI datasets, including image reconstruction, motion and physiological-noise correction, brainstem nucleus segmentation, time-series analysis, and visualization of neural activity propagation. A major methodological direction will be the development of AI-based spatiotemporal denoising methods to improve temporal signal-to-noise ratio while preserving the amplitude, timing, and spatial fidelity of rapid neuronal responses. Potential approaches may include self-supervised denoising, deep learning, and hybrid model-based/AI methods for dynamic MRI. Students from the experimental and computational tracks will work toward a common goal of integrating MRI, respiratory, and optical measurements to better understand the neural circuitry underlying breathing. The project may also be extended to investigate altered brain-wide circuit dynamics in mouse models carrying neuropsychiatric risk-gene mutations, including models relevant to autism spectrum disorder and bipolar disorder. **Requirements and Expectations:** Approximately 5–8 hours per week is preferred. Prior experience with neuroscience, MRI, programming, image processing, signal processing, or quantitative data analysis is helpful but not required. Students with backgrounds in neuroscience, biomedical engineering, physics, computer science, applied mathematics, or related fields are encouraged to apply. Prior MRI experience is not required, and appropriate training will be provided. **Additional Information:** The laboratory is located at the Athinoula A. Martinos Center for Biomedical Imaging in Charlestown, Massachusetts. Experimental work will be conducted in person, while some computational analysis may be performed remotely when appropriate. The position is unpaid and may be eligible for research course credit, including MBB 90r subject to program approval. Students interested in developing the work into a longer-term research project or senior thesis are especially encouraged to apply. **To Apply:** Please contact Xin Yu, PhD at with a brief description of your research interests and whether you are primarily interested in the experimental or computational/AI track. Please include a CV or résumé and, if available, a brief description of relevant coursework or prior research experience. (posted 9/2026) See also:- [ Undergrad Research or Opportunity ](/page-type/undergrad-research-opportunity)