Location: New York
Cap-Exempt H-1B Position — No lottery required
Department: Psychiatry Physical work location: 1255 Fifth Avenue, Suite C1,New York, NY 10029 Name PI or Supervisor: Dr. Natalie Rasgon Web link to Lab: n/a Web link to Department: https://icahn.mssm.edu/about/departments-offices/psychiatry Administrative Contact: (phone and email): Abigail Polanco This project examines the neurobiological and cognitive consequences of surgically induced menopause. Surgically induced menopause provides a clinically important model of abrupt ovarian hormone deprivation and offers a unique opportunity to study how rapid changes in reproductive endocrine status affect brain structure, function, metabolism, cognition, mood, and risk-related biomarkers. The project will use multimodal neuroimaging together with cognitive, clinical, hormonal, and biological measures to characterize brain and behavioral changes following surgical menopause. The broader aim is to identify neural and multidomain biomarkers that may clarify mechanisms of risk and resilience and inform future strategies for early identification, prevention, and intervention in women’s brain health. Technical Duties: (include any protocols) The postdoctoral fellow will be responsible for neuroimaging and multidomain data analysis for the surgically induced menopause project. Duties will include the following: Develop and implement reproducible workflows for neuroimaging data preprocessing, quality control, analysis, and documentation. Process and analyse structural MRI and other available neuroimaging modalities, Generate imaging-derived measures relevant to brain structure, function, ageing, neurodegeneration, and hormone-related brain changes. Conduct rigorous quality control of imaging data, including visual inspection, automated QC metrics, motion assessment, artefact detection Integrate neuroimaging measures with cognitive, clinical, hormonal, demographic, treatment-related, and biomarker data. Apply appropriate statistical models for longitudinal and cross-sectional analy