Poster No:
952
Submission Type:
Abstract Submission
Authors:
Ziwei Zhang1, Monica Rosenberg1,2
Institutions:
1Department of Psychology, The University of Chicago, Chicago, IL, 2Neuroscience Institute, The University of Chicago, Chicago, IL
First Author:
Ziwei Zhang
Department of Psychology, The University of Chicago
Chicago, IL
Co-Author:
Monica Rosenberg
Department of Psychology, The University of Chicago|Neuroscience Institute, The University of Chicago
Chicago, IL|Chicago, IL
Introduction:
We experience surprise, a transient process supported by distributed brain networks (Mazancieux et al., 2023), when reality conflicts with our expectations. Characterizing brain network dynamics allows us to discover commonalities between surprise in distinct contexts. We investigated network dynamics by computing two brain regions' moment-by-moment co-deflections, known as their co-fluctuation or edge time series (Faskowitz et al. 2020; Zamani Esfahlani et al., 2020) using fMRI. We propose an edge-fluctuation-based predictive model (EFPM) that predicts moment-to-moment changes in belief-inconsistent surprise across datasets.
Methods:
We analyzed existing dataset collected as participants performed a task where they learned to predict the location of an upcoming object (N=32; McGuire et al., 2012; Kao et al., 2020). McGuire et al. (2012) developed a normative model tracking change point probability (changes in the mean of an occluded generative distribution of the object's location; CPP) and uncertainty (about the generative mean; RU). We operationalized surprise in this task as a composite measure of the two (CPP+RU*[1-CPP]). To identify functional brain networks whose strength predicted this measure, we calculated the edge time series (Faskowitz et al., 2020, Zamani Esfahlani, 2020) of all pairs of 268 brain regions in a functionally defined atlas as the product of their z-scored BOLD-signal time series. To build the EFPM, we used cross-validation to identify edges whose strength varied across trials with surprise (Fig. 1). In each training fold, we selected n-1 participants and calculated the partial Spearman correlation (rho) between their edge time series and their surprise time course, controlling for head motion. We selected edges significantly correlated with surprise across the group. In the held-out individual, we correlated the strength of these edges with the belief-inconsistent surprise. This process was repeated so that each individual was held out once.
After training the surprise EFPM in the learning task, we tested whether it generalized to predict surprise in a naturalistic context in an independent dataset. We analyzed openly available fMRI data collected as novel participants watched NCAA basketball games (N=20; Antony et al., 2021). Antony et al. (2021) provided a measure of surprise from change in a team's win probability. We calculated moment-to-moment surprise EFPM summary score (Fig. 1) and ran a linear mixed effects model using this time course to predict belief-inconsistent surprise in the basketball videos, controlling for nuisance regressors (e.g., head motion).

Results:
EFPM successfully predicted task surprise in held-out individuals (Fig. 2). Edges positively correlated with surprise were stronger on trials with more unexpected outcomes (mean within-subject partial rho=0.09; p=0.001) whereas edges negatively correlated with surprise showed the opposite pattern (mean within-subject partial rho=-0.10; p=0.001). The surprise EFPM also predicted surprise in the NCAA basketball videos (ß=0.037, t(65136.852)=3.947, p=0.044), even when controlling for low-level factors (e.g., video luminance). Moreover, neither models built from BOLD activation nor from connectivity in canonical networks generalized across datasets to predict surprise. EFPMs built from other related behavioral measures also did not predict surprise out-of-sample.
Conclusions:
We identified a brain network model, the surprise EFPM, that predicts surprise in controlled and naturalistic tasks from high-frequency edge dynamics. This model generalizes across contexts and uniquely predicts surprise, capturing expectation violations better than models built from other brain networks, fMRI measures, and behavioral metrics. Thus, the surprise EFPM captures common neural underpinnings of surprise experienced in distinct cognitive contexts in different groups of individuals.
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Learning and Memory:
Learning and Memory Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
FUNCTIONAL MRI
Learning
Other - Surprise
1|2Indicates the priority used for review
Provide references using author date format
Antony, J. W., Hartshorne, T. H., Pomeroy, K., Gureckis, T. M., Hasson, U., McDougle, S. D., & Norman, K. A. (2021). Behavioral, Physiological, and Neural Signatures of Surprise during Naturalistic Sports Viewing. Neuron, 109(2), 377-390.e7. https://doi.org/10.1016/j.neuron.2020.10.029
Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 1644–1654. https://doi.org/10.1038/s41593-020-00719-y
Kao, C.-H., Khambhati, A. N., Bassett, D. S., Nassar, M. R., McGuire, J. T., Gold, J. I., & Kable, J. W. (2020). Functional brain network reconfiguration during learning in a dynamic environment. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-15442-2
Mazancieux, A., Mauconduit, F., Amadon, A., Willem de Gee, J., Donner, T. H., & Meyniel, F. (2023). Brainstem fMRI signaling of surprise across different types of deviant stimuli. Cell Reports, 42(11), 113405. https://doi.org/10.1016/j.celrep.2023.113405
McGuire, J. T., Nassar, M. R., Gold, J. I., & Kable, J. W. (2014). Functionally Dissociable Influences on Learning Rate in a Dynamic Environment. Neuron, 84(4), 870–881. https://doi.org/10.1016/j.neuron.2014.10.013
Zamani Esfahlani, F., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2020). High-amplitude cofluctuations in cortical activity drive functional connectivity. Proceedings of the National Academy of Sciences, 117(45), 28393–28401. https://doi.org/10.1073/pnas.2005531117