Primate cortical organization revealed by concordance of fMRI timespace series
Poster No:
1230
Submission Type:
Abstract Submission
Authors:
Ping Wang1, Xi-Nian Zuo2
Institutions:
1Beijing Normal University, BEIJING, BEIJING, 2State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing
First Author:
Co-Author:
Xi-Nian Zuo
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Introduction:
Local functional homogeneity, as a network centrality measure that characterizes multimodal local features of the brain connectome, provides valuable insights into the neural correlates of normal cognition and behavior, as well as the neural underpinnings of various diseases (Jiang & Zuo, 2015). However, despite the crucial role of geometric constraints (spatial property) and homophilic attachment (topological property) in shaping the human connectome (Zuo et al., 2017), traditional methods for mapping local functional homogeneity have typically focused only on the temporal correlations of functional timeseries between a node and its neighbors, while neglecting the homophilic factors in generative connectivity models. This limitation hampers their ability to precisely quantify local brain functions. Here, we proposed a novel method, Regional Functional Affinity (RFA), based on concordance of fMRI timespace series, with the aim of quantifying local functional homogeneity more precisely. The current study validated the reliability of RFA.
Methods:
The RFA is defined as Kendall's coefficient of concordance (KCC) (Kendall & Gibbons, 1990) among the whole-brain functional connectivity (FC) profiles of a node or vertex and its neighbors (6 neighbors for neighbor size of 1 and 18 neighbors for neighbor size of 2). Specifically, the whole-brain FC profiles of a node and its neighbors were first calculated, and the KCC among these profiles was then computed to derive the RFA value, which quantifies local functional homogeneity (Figure 1). By leveraging the resting-state functional magnetic resonance imaging (rfMRI) data from the Human Connectome Project (HCP) and the Chinese HCP (CHCP), we mapped the high spatiotemporal resolution RFA to evaluate its reliability.
·Schematic diagram of the regional functional affinity calculation
Results:
Consistent with the study of Gordon et al. (2024), the effector-specific areas (foot, hand and mouth) in the motor cortex (M1) were been seen interrupted by inter-effector regions in both the RFA maps of HCP and CHCP, suggesting that the RFA can effectively capture normal human brain functions. With low RFA values primarily located in the higher-order associative cortex and high RFA values concentrated in the lower-level primary cortex, the RFA maps of HCP and CHCP were distributed highly uniformly, indicating the reliability of RFA across different populations. Furthermore, despite the overall uniformity between the RFA maps of HCP and CHCP, systemic differences were observed, particularly in regions associated with social functions. Specifically, The RFA values in HCP was notably lower than those in CHCP in these regions, demonstrating that RFA can effectively capture cultural differences.
·Regional functional affinity maps of HCP (A) and CHCP (B)
Conclusions:
The RFA, which utilizes the whole-brain FC profiles instead of traditional time series to represent brain function, effectively captures both spatial geometric and topological properties of the brain connectome. Validation of the RFA with rfMRI data from both the HCP and CHCP datasets demonstrates its reliability and effectiveness in mapping cortical organization. The current findings suggested that the RFA could serve as an effective tool for precisely quantifying local functional homogeneity and provide new insights into the dynamic interactions that shape the brain's functional architecture.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Methods Development 2
Task-Independent and Resting-State Analysis
Other Methods
Keywords:
Cortex
Data analysis
FUNCTIONAL MRI
Other - Local Functional Homogeneity
1|2Indicates the priority used for review
Abstract Information
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2. Zuo, X.-N., He, Y., Betzel, R. F., Colcombe, S., Sporns, O., & Milham, M. P. (2017). Human Connectomics across the life span. Trends in Cognitive Sciences, 21(1), 32-45.
3. Kendall M, & Gibbons JD. (1990). Rank Correlation Method. Oxford, England: Oxford University Press.
4. Gordon, E. M., Chauvin, R. J., Van, A. N., Rajesh, A., Nielsen, A., Newbold, D. J., … Dosenbach, N. U. F. (2023). A somato-cognitive action network alternates with effector regions in motor cortex. Nature, 617(7960), 351–359.