We will have a 30-min presentation by ICS Assist. Research Professor Marta Čeko and Assoc. Research Professor Leanne Hirshfield on Concurrent fMRI-fNIRS and concurrent fMRI-EEG: New capabilities at ICS, and ICS Director Tammy Sumner will present our ICS Academic Awards!

Title: Concurrent fMRI-fNIRS and concurrent fMRI-EEG: New capabilities at ICS

Abstract: We will introduce our newly acquired MRI-compatible Near-infrared Spectroscopy (NIRS) and MRI-compatible electroencephalogram (EEG) equipment for muli-modal brain imaging to the community. This technology opens up exciting possibilities for our community. In addition to showcasing the capabilities of this equipment, we will outline potential research avenues it enables. By highlighting these prospects, we aim to ignite enthusiasm for innovative multi-modal neuroimaging experiments within our community and pave paths toward collaboration. 

Bios: Dr. Čeko received her BSc in Molecular Biology from the University of Vienna, Austria; her MSc in Medical Neuroscience from the Charité University in Berlin, Germany; and her PhD in Neuroscience at McGill University, Canada. She completed her postdoctoral training at NIH and at CU Boulder. Dr. Čeko’s research explores brain mechanisms of pain and affect in health and disease. She combines computational modeling with neuroimaging, behavioral data and physiological data to advance the understanding of underlying neural mechanisms and develop predictive and generalizable brain and physiology-based models of pain and emotion. She primarily uses fMRI in her research, complemented with naturalistic experimental designs and ecologically-valid research to bridge to bridge tightly controlled fMRI research with real-world science.

Dr. Hirshfield received her BA in Computer Science from Hamilton College, NY; her MSc in Computer Science from Colorado School of Mines, CO; and her PhD in Computer Science from Tufts University, MA. Dr. Hirshfield’s research explores the use of non-invasive brain measurement to passively classify users’ social, cognitive, and affective states in order to enhance usability testing and adaptive system design. She works primarily with fNIRS, a relatively new non-invasive brain imaging device that is safe, portable, robust to noise, which can be implemented wirelessly; making it ideal for research in human-computer interaction. The high density fNIRS equipment in Hirshfield’s lab provides rich spatio-temporal data that is well suited as input into deep neural networks and other advanced machine learning algorithms. A primary tenet of Hirshfield’s machine learning research involves building and labeling large cross-participant, cross-task fNIRS training datasets in order to build robust and generalizable models that can avoid overfitting and succeed in ecologically valid environments outside the lab.