Identifying shared brain states during movie watching
Poster No:
1227
Submission Type:
Abstract Submission
Authors:
Yulia Nurislamova1, Simon Eickhoff1, Susanne Weis1, Xuan Li2
Institutions:
1Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany, 2Institute of Neuroscience and Medicine Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
First Author:
Yulia Nurislamova
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University
Düsseldorf, Germany
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University
Düsseldorf, Germany
Co-Author(s):
Simon Eickhoff
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University
Düsseldorf, Germany
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University
Düsseldorf, Germany
Susanne Weis
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University
Düsseldorf, Germany
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University
Düsseldorf, Germany
Xuan Li, PhD
Institute of Neuroscience and Medicine Brain and Behaviour (INM-7), Research Centre Jülich
Jülich, Germany
Institute of Neuroscience and Medicine Brain and Behaviour (INM-7), Research Centre Jülich
Jülich, Germany
Introduction:
Movie watching in the MRI scanner, or naturalistic viewing (NV), has shown great promise in studying higher brain functions (Eickhoff et al., 2020). Stimuli in NV, approximating the real-world environment, allow for capturing intricate cortical interactions. While previous studies investigating static functional connectivity (FC) in NV have shown increased similarity across participants' cortical responses, especially those evoked by movie clips with complex narratives and emotional content (Kröll et al., 2023; Mochalski et al., 2024), FC profiles tend to be highly dynamic. It remains largely unknown how the similarity in FC profiles between participants and the shared brain states change over time and across movie clips. In this study we aim to identify prominent brain states evoked during NV.
Methods:
For this analysis we used a 7T fMRI dataset from Human Connectome Project (Van Essen et al., 2013), containing 178 participants watching 14 movie clips. We used a mean BOLD signal of each parcel defined by a whole-brain atlas containing 400 nodes (Schaefer et al., 2018) to calculate the edge time series (ETS, Faskowitz et al., 2020). ETS reflects co-fluctuation between pairs of nodes at each time point, providing a moment-wise measure of FC. Next, to identify FC patterns 'shared' across participants, we applied the TOPF method (Li et al., 2023). In this case, the shared response reflects the whole-brain FC profile consistently evoked across participants by the presented movie stimuli. Specifically, the shared FC pattern was derived as the first principal component (PC1) of the co-fluctuation profiles across subjects for each TR. For the purpose of this study we define brain states as shared whole-brain FC configurations that tend to recur over time and movie clips. To identify shared brain states we only selected TRs that showed high subject similarity, measured as inter-subject synchrony (ISS). ISS values were calculated for each TR as variance explained by the PC1 and compared to null-distribution from the resting state to determine statistical significance. FC profiles from selected TRs were used in the hierarchical clustering, resulting in 4 clusters (brain states). PC1 for each edge was averaged across TRs for each cluster and thresholded to the top and bottom 1 percentile and averaged within the nodes belonging to the same subnetwork.
Results:
Firstly, we have identified 38.4% of total TRs across 14 movie clips with ISS values reaching statistical significance (p<0.005). Next, from the selected TRs we identified 4 brain states, represented by 4 clusters of whole-brain network configurations, shared between subjects (Fig 1). Namely, State 1, containing 154 TRs, showed specific configurations of positive and negative interactions between subnetworks of frontoparietal control (FPC), attentional and default mode networks (DMN). The 2nd state (305 TRs) displayed negative FC between Somatomotor (SM) and the rest of the brain and positive between Visual and attentional networks. Interestingly, 3rd (295 TRs) and 4th (326 TRs) showed the inverse FC profiles between Dorsal attentional and DMN (negative and positive respectively); and between Visual and Attentional systems (positive and negative respectively).
Conclusions:
Overall, our findings demonstrate that the dynamic nature of movie watching can elicit diverse shared neural interactions across movie presentations, suggesting that NV evokes specific and stable whole-brain FC profiles. We identified an intricate interplay between specific subnetworks involved in complex multi-level stimulus processing. Given the dynamic changes of the cognitive demand and the attentional requirements of the segments presented in the movie clips, this study provides insights for prospective research of how specific movie features elicit observed brain states.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Cortex
Data analysis
FUNCTIONAL MRI
Other - Naturalistic Viewing
1|2Indicates the priority used for review
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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.
Kröll, J. P., Friedrich, P., Li, X., Patil, K. R., Mochalski, L., Waite, L., ... & Weis, S. (2023). Naturalistic viewing increases individual identifiability based on connectivity within functional brain networks. NeuroImage, 273, 120083.
Li, X., Friedrich, P., Patil, K. R., Eickhoff, S. B., & Weis, S. (2023). A topography-based predictive framework for naturalistic viewing fMRI. NeuroImage, 120245.
Mochalski, L. N., Friedrich, P., Li, X., Kröll, J. P., Eickhoff, S. B., & Weis, S. (2024). Inter‐and intra‐subject similarity in network functional connectivity across a full narrative movie. Human brain mapping, 45(11), e26802.
Schaefer, A., Kong, R., Gordon, E.M., Laumann, T.O., Zuo, X.N., Holmes, A.J., Eickhoff, S.B. and Yeo, B.T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.
Van Essen, D.C. (2013), The WU-Minn Human Connectome Project: An overview, Neuroimage 80, 62–79.