Early Career Investigator Award: Dr. Catie Chang
Elisa Guma & the Communications Committee
Interview with Dr. Catie Chang, 2023 Early Career Investigator Awardee
At the 2023 Organization for Human Brain Mapping annual meeting, held in Montreal, several outstanding scientists were recognized for their contributions to the field. Dr. Catie Chang was awarded the Early Career Investigator Award for her significant contributions to the field of human brain mapping. Dr. Chang is a Sally and Dave Hopkins Faculty Fellow and an Assistant Professor in the departments of Computer Science and Electrical and Computer Engineering at Vanderbilt University. She received her BSc in Electrical Engineering and Computer Science at MIT, after which she pursued an MSc and Ph.D. in Electrical Engineering from Stanford University in the Radiological Sciences Lab. Next, she completed a Postdoctoral Fellowship at the National Institute of Neurological Disorders and Stroke (NIH Intramural Research Program).
Her lab fosters a highly interdisciplinary and collaborative research environment, bringing together scientists with expertise in engineering, computer science, neuroscience, psychology, and medicine. Together, they focus on advancing functional neuroimaging methods to increase the understanding of human brain activity in health and disease. More precisely, using fMRI and EEG, they focus on understanding how time-varying changes in brain function relate to physiological and cognitive processes.
We had the pleasure of asking Dr. Chang a few questions about her work, research trajectory, and any advice she has for junior trainees. Read on to learn more!
Q1: Which research question excites you the most at the moment?
Catie Chang (CC): Spontaneous fMRI signals contain very rich dynamics. A question I’ve been intrigued by is, how can we push the boundaries in terms of information we can extract from these signals? Our current approaches have involved combining fMRI with complementary recordings of brain/body dynamics (such as EEG and physiological sensors) to model dynamic internal states and to understand their link with variations in behavior and cognition.
Q2: What advice do you have for junior trainees?
CC: One suggestion is to be open to new directions of collaboration, as these may end up shaping and expanding your work in unforeseen ways, and helping you to form new connections between ideas. Another thought I might add is: if you see something unexpected in your data, it may actually be something quite interesting to study :)
Q3: What are your plans for the next five years? What projects and research questions would you like to pursue?
CC: One of our broad goals has been to unravel different sources that drive BOLD fMRI signal fluctuations, and try to use these components in new ways. Some elements of BOLD signal variation—including changes in how sleepy you are, as well as physiological processes like breathing and cardiac activity—tend to be widely regarded as confounds. However, these effects may also provide valuable information about individual differences and disease. In this context, our work will continue developing computational methods for investigating fMRI signatures of alertness and autonomic states, by integrating machine learning together with multimodal functional imaging (e.g., simultaneous EEG-fMRI). We have also begun studies to probe the clinical value of these features, and I am excited to see where these studies will lead.
Edited by: Lavinia Uscătescu & Simon Steinkamp