Introduction to NYC Health Disparities Using Data Science

Alfredo Spagna, Lecturer in the Discipline of Psychology, Director of Undergraduate Studies in Neuroscience & Behavior, Department of Psychology

Xiaofu He, Assistant Professor of Clinical Neurobiology at Columbia University Medical Center

ABSTRACT
Do we need a computer to understand how we behave? Recent breakthroughs in understanding the inner workings of the mind have been substantially supported by the development of refined statistical and computational models. Analytical approaches from data science have had a significant impact on multiple subfields of cognitive neuroscience, from understanding the computational structure of the visual system to providing insights regarding abnormal psychological and psychiatric conditions. Existing training programs in cognitive neuroscience, however, has not kept pace with the increasing availability and scale of public data or with the increasing complexity of cutting-edge analysis pipelines. This grant embraces an interdisciplinary approach and aims to educate the next generation of neuroscientists interested in the use of non-invasive neuroimaging techniques to study the human brain at work. 

To achieve this goal, we propose three different courses, starting with an undergraduate-level seminar, “Fundamentals of Human Brain Imaging”, followed by two graduate-level courses, “Tools for Reproducible and Collaborative Neuroscience” and “Human Neuroimaging: Data Acquisition, Analysis, and Sharing”. This educational initiative is not yet present in our curriculum and represents an opportunity to provide our students with the knowledge needed to answer key questions in cognitive neuroscience. In these courses, students will learn to understand different techniques for measuring brain activity in humans, to deal with the challenge of handling big data sets, and to ensure that analysis pipelines can be verified, reproduced, and shared. Course designs will align with the most recent theories and methods in teaching and learning. They will incorporate a variety of active learning techniques to promote engagement and critical thinking and foster an inclusive environment open to all students, including those who have limited previous data science knowledge.

This grant also provides a unique opportunity to build long-lasting collaborations between expert instructors from the Data Science Institute and the Psychology Department.

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