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Functional Network Integration Mediates Arousal Effects on Naturalistic Recall

Poster Session C - Sunday, April 14, 2024, 5:00 – 7:00 pm EDT, Sheraton Hall
Also presenting in Data Blitz Session 3 - Saturday, April 13, 2024, 1:00 – 2:30 pm EDT, Ballroom West.

Jadyn S. Park1 (, Jin Ke1, Kruthi Gollapudi1, Ioannis Papps2, Yuan Chang Leong1; 1The University of Chicago, 2Keck School of Medicine, University of Southern California

A consistent finding in memory research is that arousing stimuli are more likely to be remembered than neutral ones. Yet, the neural mechanisms underlying how arousal supports memory are not fully understood. Here, we examined whether functional network integration is a potential mechanism by which high arousal events are better remembered. We used two publicly available fMRI datasets, where participants watched an hour-long movie clip immediately followed by a free verbal recall session. Using graph theoretical approaches, we tested how the dynamic re-organization of functional networks during encoding was associated with recall performance. Further, we leveraged large language models to estimate the arousal level of movie events and validated the results with human ratings from behavioral experiments. Across both datasets, whole-brain functional network integration was associated with more rich and accurate recall (Dataset1: b=.19, SE=.03, t(809)=5.66, p<.001; Dataset2: b=.16, SE=.03, t(1017)=5.26, p<.001). Events with high arousal were indeed better remembered (Dataset1: b=.20, SE=.04, t(796)=4.57 p<.001; Dataset2: b=.19, SE=.04, t(1004)=4.65, p<.001) and coincided with events with greater functional network integration (Dataset1: b=.22, SE=.04, t(796)=4.91, p<.001; Dataset2: b=.21, SE=.04, t(1004)=5.33, p<.001). A formal mediation analysis revealed that functional network integration mediated the effects of arousal on recall (Dataset1: b=.04, 95%CI=[.02, .06], p<.001; Dataset2: b=.03, 95%CI=[.02, .05], p<.001). Our results suggest that arousal-dependent biases in memory are related to dynamic changes in the integration of functional networks. Combining approaches from systems and affective neuroscience, our work contributes to building an integrative theoretical framework that bridges affective states, ongoing cognition, and functional network topology.

Topic Area: LONG-TERM MEMORY: Episodic


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