Topographic, cognitive, and neurobiological profiling of the interdependent SC-FC connectome
Poster No:
1366
Submission Type:
Abstract Submission
Authors:
Xiaoyue Wang1, Lianglong Sun2, Xinyuan Liang2, Tengda Zhao2, Mingrui Xia2, Xuhong Liao3, Yong He2
Institutions:
1Beijing Institute of Technology, Beijing, China, 2Beijing Normal University, Beijing, China, 3School of Systems Science, Beijing Normal University, Beijing, China
First Author:
Co-Author(s):
Introduction:
Structural connectome (SC) and functional connectome (FC) interact and depend on each other to maintain brain function and support cognitive processing. While previous studies have reported SC-FC coupling, most of these studies analyzed them as isolated networks, ignoring their interdependent nature [1, 2]. Understanding how SC and FC connectome are topographically coordinated and contribute to cognition, as well as their neurobiological basis, remains crucial for comprehending brain organization principles.
Methods:
Using multimodal MRI data from S1200 dataset released by the Human Connectome Project (HCP) [3], we constructed individual-level SC (from diffusion MRI) and FC (from resting-state fMRI) connectomes based on the Glasser360 parcellation [4] for each of 1012 healthy young adult participants (aged 28.73 ± 3.71 years, 543 females). We modeled SC-FC interplay in a multiplex framework, establishing interlayer connections between identical nodes [5]. The multilayer modularity detection [6] was applied to characterize the interdependent connectome and computed multilayer modular variability [7] to quantify module assignment differences across layers. The multilayer modular variability was further assessed for its test-retest reliability, reproducibility, heritability, cognitive associations, and relationships with neurotransmitter systems and gene expression.
Results:
The multilayer modular variability exhibited substantial spatial heterogeneity across the cortex, with greater variability predominantly in the lateral prefrontal and parietal regions, dorsal medial prefrontal cortex and lateral temporal regions and less variability in the sensorimotor, visual, and ventral medial prefrontal cortex. This variability aligned with cortical hierarchical organization from primary to transmodal areas (Pearson's r = 0.56, Pspin < 0.0001), and cortical evolutionary expansion (Pearson's r = 0.51, Pspin < 0.001). The topology showed high test-retest reliability, reproducibility, and heritability. Higher modular variability in transmodal regions correlated with cognitive flexibility and better abstract cognitive performance. Additionally, we found that the spatial pattern of multilayer modular variability could be predicted by the density distributions of neurotransmitter receptors and transporters (Pearson's r = 0.59, Pspin < 0.0001), and exhibited significant correlation with the gene expression profiles (Pearson's r = 0.46, Pspin = 0.02). The associated genes were primarily involved in biological processes related to chemical synaptic transmission, and cellular components associated with synaptic parts, transport vesicles, and secretory vesicles.
Conclusions:
This study elucidates for the first time the nontrivial topographic, cognitive, and neurobiological profiles of the interdependent SC-FC connectome. The findings have implications for understanding brain organization and potential clinical applications in studying brain disorders.
Genetics:
Transcriptomics
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Multivariate Approaches 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Keywords:
Cognition
Computational Neuroscience
Modeling
MRI
Neurotransmitter
NORMAL HUMAN
RECEPTORS
Other - connectomics; gene expression; interdependent network; neurotransmitter; multilayer
1|2Indicates the priority used for review
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2 Grefkes, C. (2014). Connectivity-based approaches in stroke and recovery of function. The Lancet Neurology, 13(2), 206-216.
3. Van Essen, D. C. (2013). The WU-Minn Human Connectome Project: an overview. Neuroimage, 80, 62-79.
4. Glasser, M. F. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
5. Kivelä, M. (2014). Multilayer networks. Journal of complex networks, 2(3), 203-271.
6. Mucha, P. J. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876-878.
7. Liao, X. (2017). Individual differences and time-varying features of modular brain architecture. Neuroimage, 152, 94-107.