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Decoding the Neural Generator of the Reward Positivity with simultaneous EEG-fMRI
Poster Session A - Saturday, March 29, 2025, 3:00 – 5:00 pm EDT, Back Bay Ballroom/Republic Ballroom
Jaleesa S. Stringfellow1 (jss388@newark.rutgers.edu), Malte R. Güth2, Travis E. Baker1; 1Rutgers Newark, 2University of Minneapolis
The reward positivity (RewP) is an event-related brain elicited by reward feedback is used to study neural mechanisms of goal-directed behavior in typical and atypical populations. While the RewP is believed to originate from or near the anterior midcingulate cortex (MCC), its neural source remains a topic of debate. To resolve this issue, we utilize a new method based on simultaneous EEG-fMRI data and machine learning to decode the neural source of the RewP. We acquired EEG-fMRI data from twenty-eight healthy participants (Age: M = 23.5 years, SD = 4.24 years; 16 female) while they navigated a virtual T-maze to find rewards. We first performed a linear discriminant analysis (LDA) classification on single-trial EEG data to identify the peak temporal classification window. Next, the EEG-trained LDA classifier was then tested on the fMRI data sample set using single-trial conditions labeled as features. The EEG data accurately classified reward and no-reward conditions within a range of 250-350 ms, with a peak accuracy of 0.547 at 275 ms post-feedback. Within this time window, EEG information classified BOLD activity in the midcingulate cortex (Glasser Region a34prime, accuracy score t (21) = -2.97, p<0.01), as well as posterior cingulate (Glasser Region d23ab, accuracy score t (21) = -2.99, p<0.01). These results suggest that the neural source of the RewP comes, at least in part, from the MCC and algins with prior research and shows the feasibility of fusing EEG and fMRI data using machine learning techniques to compare neural activity across different modalities.
Topic Area: EXECUTIVE PROCESSES: Goal maintenance & switching