Relationships between intersubject correlations and gradients are robust across viewing conditions
Poster No:
1367
Submission Type:
Abstract Submission
Authors:
Meaghan Smith1, Ahmad Samara2, Alexander Ngo1, Jeffrey Eilbott2, Hallee Shearer2, Tamara Vanderwal2, Boris Bernhardt1
Institutions:
1McGill University, Montreal, Quebec, 2University of British Columbia, Vancouver, British Columbia
First Author:
Co-Author(s):
Introduction:
Gradient analysis uses dimensionality reduction to identify organizational principles from functional connectivity (FC). In contrast, intersubject correlations (ISC) indicate how similarly a brain region responds to the same stimulus across participants. Despite describing brain organization from different perspectives, both measures capture core features of functional architecture by delineating default mode regions from task-positive brain regions (Hasson et al., 2004; Margulies et al., 2016). Here, we leverage movie-fMRI to generate movie gradients that have been shown to enhance brain-behavior associations (Samara et al., 2023). We then use ISC to map intersubject synchronization onto the functional hierarchy identified via gradient analyses. We compare these measurements across movies to investigate if the robustness of these relationships depends on movie content. We also explore which functional networks drive whole-brain correlations between these measures. We predict a significant correlation between gradient scores and ISCs, revealing a parallel organization between "task-evoked" BOLD-signal responses and the hierarchical organization of FC. We expect this relationship to be robust to movie content.
Methods:
Data. We used minimally preprocessed 7T movie-watching data from the Human Connectome Project (Van Essen et al., 2013). Ninety-five healthy adults (58 females, mean age 29.5 ± 3.3) from 64 families were selected based on head motion and data availability. One hour of movie data was collected over the course of four 15-minute runs across two sessions. Analyses were conducted in a discovery dataset of 46 subjects and replicated in the remaining 49.
Spatial correlations. Gradient analyses were performed using Schaefer-1000 (Schaefer et al., 2018). Diffusion embedding was performed on FC matrices using the BrainSpace toolbox (Vos de Wael et al., 2020), and gradients were aligned to a group-average template before being averaged. BOLD-signal timeseries data were used to compute ISCs using a group mean approach. Spin tests were used to assess the significance of correlations between ISCs and gradient scores (Alexander-Bloch et al., 2018).
Variability and reliability. Complete gradients were correlated with ISCs from 4-minute clips to assess the variability of observed correlations. To assess reliability, gradients were correlated with ISCs from a 1.5-minute clip watched four separate times.
Network-level correlations. Correlations were computed again using masks from the Kong 17-networks (Kong et al., 2021), and Neurosynth meta-analysis (Yarkoni et al., 2011) was used to determine the functional roles of each network.
Spatial correlations. Gradient analyses were performed using Schaefer-1000 (Schaefer et al., 2018). Diffusion embedding was performed on FC matrices using the BrainSpace toolbox (Vos de Wael et al., 2020), and gradients were aligned to a group-average template before being averaged. BOLD-signal timeseries data were used to compute ISCs using a group mean approach. Spin tests were used to assess the significance of correlations between ISCs and gradient scores (Alexander-Bloch et al., 2018).
Variability and reliability. Complete gradients were correlated with ISCs from 4-minute clips to assess the variability of observed correlations. To assess reliability, gradients were correlated with ISCs from a 1.5-minute clip watched four separate times.
Network-level correlations. Correlations were computed again using masks from the Kong 17-networks (Kong et al., 2021), and Neurosynth meta-analysis (Yarkoni et al., 2011) was used to determine the functional roles of each network.
Results:
As predicted, ISCs were high in superior temporal and occipital regions. The top three gradients had poles in the somatosensory, visual, and auditory cortices and radiated towards heteromodal association cortex. There was a significant spatial correlation between ISC scores and Gradient 2 scores (r=0.74, p<0.05), and this relationship was reproduced in the replication dataset. Gradient 2 correlations were high when computed across movie clips (r=0.72±0.0845), but they were most stable across repeated viewing of the same clip (r=0.74±0.0350). The network with the strongest gradient-ISC correlations was the dorsal attention network B, followed by Visual B and Default C in some gradients. The most highly correlated network varied across clips.
Conclusions:
These results provide further support for a macroscale processing hierarchy in the brain exemplified under naturalistic conditions, while strong within-network correlations point to functional sub-hierarchies. Robust relationships across movies suggest that we might understand movie-watching as a brain state that is independent of movie-content. Overall, these findings suggest that when the brain is processing complex stimuli, there is a strong correspondence between FC patterns at a whole-brain level, and stimulus-evoked BOLD signal responses across subjects at a parcel-level.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Other Methods
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Computational Neuroscience
Cortex
FUNCTIONAL MRI
HIGH FIELD MR
MRI
NORMAL HUMAN
Open Data
Open-Source Software
Statistical Methods
Other - naturalistic neuroimaging; intersubject correlation; functional gradients; movie-fMRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Glasser, M. F. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127
Hasson, U. (2004). Intersubject synchronization of cortical activity during natural vision. Science (New York, N.Y.), 303(5664), 1634–1640. https://doi.org/10.1126/science.1089506
Kong, R. (2021). Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior. Cerebral Cortex (New York, NY), 31(10), 4477. https://doi.org/10.1093/cercor/bhab101
Margulies, D. S. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574–12579. https://doi.org/10.1073/pnas.1608282113
Samara, A. (2023). Cortical gradients during naturalistic processing are hierarchical and modality-specific. NeuroImage, 271, 120023. https://doi.org/10.1016/j.neuroimage.2023.120023
Schaefer, A. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex (New York, N.Y.: 1991), 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179
Van Essen, D. C. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
Vos de Wael, R. (2020). BrainSpace: A toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications Biology, 3(1), Article 1. https://doi.org/10.1038/s42003-020-0794-7
Yarkoni, T. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. https://doi.org/10.1038/nmeth.1635