Capturing Cortical Functional Connectivity Shifts During Naturalistic Stimulation
Poster No:
1208
Submission Type:
Abstract Submission
Authors:
Yezhou Wang1, Raúl Rodríguez-Cruces1, Donna Gift Cabalo1, Alexander Ngo1, Meaghan Smith1, Youngeun Hwang1, Ilana Leppert1, Tamara Vanderwal2, Sofie Valk3, Alan Evans1, Boris Bernhardt1
Institutions:
1McGill University, Montreal, Quebec, 2University of British Columbia, Vancouver, British Columbia, 3Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Saxony
First Author:
Co-Author(s):
Introduction:
Cortical topographic organization supports complex cognitive processes across different functional states. Previous studies have shown that naturalistic stimuli, like movie-watching, enhance individual differences in behavior-related brain networks (1), and highlight the roles of sensory-driven cortical hierarchies that propagate signals from sensory systems towards uni- and transmodal association cortices (2). However, how cortical functional connectivity (FC) patterns, and hierarchical network organization, adapt to naturalistic stimuli remains largely unknown. Here, we leveraged high-resolution 7T MRI and effective connectivity modelling to investigate differences in sensory-driven hierarchical signal flow between resting and movie-watching states and its relationship to high-level semantic features.
Methods:
We analyzed resting state and movie-watching fMRI data from 93 healthy adults (mean age 29.4±3.3 years, 56 females) from the HCP 7T dataset (3). Timeseries data from four movie runs were concatenated and mapped to a multi-modal parcellation (4). Regression dynamic causal models (rDCM) (5) were applied to these timeseries, generating effective FC matrices for each participant (Figure 1.A). The same approach was applied to resting-state timeseries. To investigate signal flow differences between movie and rest conditions, we selected the primary visual cortex (V1) and primary auditory cortex (A1) as regions of interest (ROIs). All subsequent analyses were performed for both afferent and efferent connections of the ROIs to examine their relationships. We calculated the Rest-Movie shift for each region using group-averaged FC of the ROIs. This analysis was extended to estimate Rest-Movie shifts across twelve defined brain networks (6). To quantify the differences in effective FC, we estimated the Movie-Rest difference (MRD) using a linear mixed-effects model. To explore the relationship between MRD and visual-semantic content of the videos, we performed principal component analysis on the motion energy features (MEF) and the semantic category features (SCF) of the movies, computing the mean of the first principal component for each movie clip (Figure 2.A). MRD was also calculated for each clip, and Spearman's correlation coefficients were computed between MRD and MEF as well as SCF within each network.
Results:
By applying rDCM to the timeseries, we generated effective FC matrices for each participant. Using V1 as ROI, we observed a general increase in FC with primary sensory cortices during movie watching compared to rest (Figure 1.A). Using A1 as the ROI, enhanced FC was observed with auditory and language networks (Figure 1.B). To quantify the Movie-Rest Difference (MRD), a linear mixed-effects model revealed significantly increased FC between V1 and secondary visual and auditory networks (t>7.5, p<0.001; Figure 1.C). Similarly, heightened FC was detected between A1 and language/auditory networks (t>10.0, p<0.001). MRD patterns for afferent and efferent connections were similar, although efferent MRD was generally greater than afferent MRD, except within the frontoparietal network. To explore the associations between MRD and visual stimuli, we estimated the first principal component of MEF and SCF. Although no significant correlations were found between MRD and MEF, we identified significant associations between MRD and SCF within the somatomotor, auditory, and default mode networks (p<0.05, FDR correction; Figure 2.B).
Conclusions:
Leveraging high-resolution 7T MRI data, this study provides a framework for investigating cortical FC patterns in the human brain. Larger movie-rest differences were observed in FC from language and auditory networks to V1 and A1, reflecting their joint processing of visual and auditory signals during naturalistic stimulation. These differences were related to semantic but not lower level stimulus features, providing insights into cortical organization during multimodal cognitive engagement.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling 2
Keywords:
Cortex
FUNCTIONAL MRI
Modeling
MRI
NORMAL HUMAN
1|2Indicates the priority used for review
Abstract Information
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Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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