Inter-subject synchrony of edge centric connectivity during naturalistic viewing
Poster No:
1547
Submission Type:
Abstract Submission
Authors:
Yulia Nurislamova1, Susanne Weis2, Richard Betzel3, Simon Eickhoff1, Xuan Li2
Institutions:
1Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 2Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany, 3Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
First Author:
Yulia Nurislamova
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf
Düsseldorf, Germany
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf
Düsseldorf, Germany
Co-Author(s):
Richard Betzel
Department of Psychological and Brain Sciences, Indiana University
Bloomington, IN, United States
Department of Psychological and Brain Sciences, Indiana University
Bloomington, IN, United States
Simon Eickhoff
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf
Düsseldorf, Germany
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf
Düsseldorf, Germany
Introduction:
Naturalistic viewing (NV) paradigms, namely movie watching, have shown great promise for facilitating our understanding of brain functions [1]. Inter-subject synchrony (ISS) is a commonly used approach to study evoked brain activity in NV studies [2]. Previous studies have observed ISS in not only brain activity [2] but also functional connectivity (FC) [3], suggesting that processing of complex naturalistic stimuli requires integration of information over different networks. However, it remains largely unknown to what degree shared NV stimuli induce coherent network dynamics over subjects. In this study, we investigated changes in ISS of FC patterns over time during NV.
Methods:
Data for this analysis was obtained from the Human Connectome Project [4], specifically the 7T fMRI dataset, containing recordings of 178 subjects watching 14 different movie clips. The mean signal was derived for each parcel (node) as defined by the Schaefer 400 parcellation [5] and z-scored within each subject (Fig. 1A). For each participant, dynamic FC patterns were computed as edge time series [6], reflecting the co-fluctuation of signals between all pairs of parcels (nodes) at each time point. (Fig. 1B). Higher ISS indicates that the dynamic FC at the given TR is more consistent across subjects. Here, ISS was measured by the variance explained by the first principal component (PC1) [7], with PC1 reflecting the edge co-fluctuation pattern shared across subjects. For the whole-brain level, principal component analysis was performed on all z-scored ETS across all subjects for each time point separately (Fig. 1C). The analysis was repeated for each TR of all movie clips.
Next, we investigated how the ISS changes at a finer spatial scale. Specifically, whole-brain ETS were assigned according to the functional 7-network atlas [8], resulting in 28 pairs: 7 within- and 21 between-network. ISS was calculated for each TR and pair of networks independently. We repeated the entire procedure on 900 TRs of the resting state data, generating a null-distributions to determine the statistical significance of each ISS value to identify TRs where brain states are reliably shared between subjects.
Next, we investigated how the ISS changes at a finer spatial scale. Specifically, whole-brain ETS were assigned according to the functional 7-network atlas [8], resulting in 28 pairs: 7 within- and 21 between-network. ISS was calculated for each TR and pair of networks independently. We repeated the entire procedure on 900 TRs of the resting state data, generating a null-distributions to determine the statistical significance of each ISS value to identify TRs where brain states are reliably shared between subjects.
Results:
Firstly, ISS values were not correlated to the subject's head motion (r=-0.05, p=0.54). Further, our results indicate that the ISS of the whole-brain connectivity patterns varies at a single TR resolution through the movie presentations (Fig. 2A-B). Here, we will focus on two clips, illustrating extreme cases of unique ISS profiles, possibly due to different movie content. 'Inception' showed the largest mean-ISS (0.066±0.03; Fig. 2C), while 'Dreary' achieved the lowest (0.033±0.004; Fig. 2C).
At a finer spatial scale, our network analysis revealed TRs with significant ISS fluctuations that were not observed at the whole-brain level. For instance, ISS within the visual network in response to 'Dreary' displays periods of significant fluctuations over the course of the movie. As expected for a NV, the most prominent ISS fluctuations were observed between visual and all other networks for all movie clips (Fig 2D). Interestingly, in the case of 'Inception', ISS fluctuations were not limited to the interactions with the visual network: dorsal attentional network interactions exhibited prominent ISS dynamics with somatomotor, default mode, and other networks (Fig 2E).
At a finer spatial scale, our network analysis revealed TRs with significant ISS fluctuations that were not observed at the whole-brain level. For instance, ISS within the visual network in response to 'Dreary' displays periods of significant fluctuations over the course of the movie. As expected for a NV, the most prominent ISS fluctuations were observed between visual and all other networks for all movie clips (Fig 2D). Interestingly, in the case of 'Inception', ISS fluctuations were not limited to the interactions with the visual network: dorsal attentional network interactions exhibited prominent ISS dynamics with somatomotor, default mode, and other networks (Fig 2E).
Conclusions:
Overall, we observed highly dynamic fluctuations of inter-subject synchrony of FC patterns over time for all movie clips, suggesting that rapidly changing movie content heavily influences brain connectivity at a fine temporal scale. Furthermore, network-level analysis revealed nuanced influence of the movie content on FC, where ISS is observed beyond sensory areas, including dorsal attentional, somatomotor, and default networks related to the processing complex movie stimuli. This study provides fundamental insights for future research into complex brain activity induced by specific features of movie stimuli.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cortex
Data analysis
FUNCTIONAL MRI
Other - Functional connectivity; Naturalistic viewing
1|2Indicates the priority used for review
Provide references using author date format
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