Postdoctoral Fellow-MSH-13400-374

Salary: $74,692-$82,740

Department: Psychiatry 

Physical work location: 100 E 104th Street, 4th Floor, New York, NY 10029 and 521 W 57^th st 6^th floor, New York, NY 10019

Name PI or Supervisor: Dr. Martijn Figee & Dr. Shalaila Haas

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

Details of Research Project: 

This new initiative is focused on using behavioral measures and computational methods to define novel clinical signatures that can be used for individual-level prediction and clinical decision making in treating mental disorders. This study, titled, “Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR)”, will use objective, scalable, and cost-effective measurements to define novel clinical signatures that can be used for individual-level prediction and clinical decision-making in treating mental health disorders. 

This pilot study will explore the safety, feasibility, clinical effects and mechanism of deep brain stimulation in the substantia nigra pars reticulata (SNr DBS) in 5 patients with treatment resistant schizophrenia. The pilot study will primarily investigate safety of the DBS surgical procedure and chronic stimulation, compliance with postsurgical care, follow-up DBS programing and research visits, and home-based procedures, including managing the DBS patient-controller and daily recordings of DBS sensing, symptom severity and audiovisual measures. Secondary outcomes are DBS related clinical changes, including hallucinations, delusions, negative symptoms and cognitive function, and optional intraoperative measures including acute symptom changes, electrophysiology, dopamine voltammetry, and brain biopsy.   

Technical Duties: (include any protocols)

Research & Analysis: Conduct empirical studies in computational psychiatry, analyze multi-modal datasets, and synthesize insights to support hypothesis-driven research and evidence-based treatment strategies.

• Collaboration and Mentorship: Work collaboratively within an interdisciplinary team, and contribute to mentoring junior researchers, including graduate students and research assistants.

• Subject recruitment and enrollment: Assist in recruiting and consenting patients with chronic schizophrenia that have previously failed to respond to antipsychotics including clozapine and are able to safely comply with a study involving brain surgery and management of an implanted device.  

• Research data collection: Assist in collecting intraoperative data, pre-and postoperative MRI scans and follow-up recordings, and assist in patient device management. 

• Research analysis: Assist in analyzing home-based audiovisual (face, speech) data that can be correlated with recordings of symptoms and brain sensing.   

Educational and other Requirements for the position: 

Education: M.D. or Ph.D. in Neuroscience, Medicine, Computer Science, Machine Learning, Data Science, Biomedical Informatics, Electrical Engineering, or a related field.

• Research Background: Demonstrated experience in one or more of the following areas: computational psychiatry, machine learning, and AI.

• Technical Skills: Proficiency in programming languages such as Python or MATLAB is highly desirable.

• Analytical Abilities: Excellent data analysis skills and familiarity with methodologies used in biomedical research.

• Communication Skills: Strong written and verbal communication skills, with a proven track record of research publications.

Experience Required: 1-2 years of Research Experience

Goals/Outcomes of the Research Project: 

This study, titled, “Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR)”, will use objective, scalable, and cost-effective measurements to define novel clinical signatures that can be used for individual-level prediction and clinical decision-making in treating mental health disorders. 

This study, titled, "Substantia Nigra Deep Brain Stimulation for Treatment Resistant Schizophrenia”, will pilot the feasibility of deep brain stimulation in patients with schizophrenia including home-based recordings of symptom severity, audiovisual features and brain activity. These pilot data will be used to apply for an NIH-funded study investigating the efficacy and mechanism of SNr DBS in schizophrenia.