Alcohol use and smoking during pregnancy often occur in the context of maternal anxiety and depression. However, little is known regarding how various combinations of maternal alcohol consumption, smoking, and psychiatric symptoms interact to alter early central and autonomic nervous system (CNS, ANS) development and, in turn, how these changes underlie long-term neurobehavioral outcomes. The overall goals of the project are to a) comprehensively characterize maternal drinking, smoking, depression, and anxiety during pregnancy; b) perform deep phenotyping of electrocortical and physiological data from infants; c) relate maternal prenatal exposure clusters to deep phenotyping of CNS/ANS function and neurobehavioral outcomes; d) test the hypothesis that these measures mediate relationships between maternal prenatal exposures and subsequent child neurodevelopmental outcomes. To accomplish these goals, we will use existing data from an extensive study of mother-infant dyads recruited for the completed Safe Passage Study from recently completed follow-up studies which enrolled subsets of the PASS participants at 1 and 3 years of age. PASS participants were from geographically and socio-economically diverse regions in South Africa (SA) and the Northern Plains of the United States (US). The student(s) will be required to rapidly become familiar with a variety of data types, e.g., questionnaire data, physiological high-dimensional signals, neurobehavioral clinical examinations, neurodevelopmental assessments, etc. From a methodological point of view, the student(s) will support the development of: i) pipelines for the standardized and automated preprocessing and cleaning of physiological signals; ii) machine learning techniques (data-drive clustering, prediction) and iii) advanced statistical methodologies (inference, mediation, multidimensional data integration) tailored to the data available in this project. The student(s) will join a young and highly dynamic multidisciplinary team of experts, students, and RAs (some who originally joined the lab as DSI-students).

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: [Nicolo Pino](Fifer Lab (William P. Fifer))
  • Center/Lab: Psychiatry

Project Timeline

  • Earliest starting date: 10/17/2022
  • End date:
  • Number of hours per week of research expected during Fall 2022: ~15

Candidate requirements

  • Skill sets: Mandatory fluency in at least one coding language (Matlab, R, Python, SAS), preliminary experience with high-dimensional, longitudinal, and heterogeneous datasets. Ability to rapidly integrate and collaborate with a multidisciplinary team, predisposition to deliver results with a rapid turnaround and meet short term deadlines.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes