Representation mapping network (RMN): individual-level structural similarity network
Poster No:
1211
Submission Type:
Abstract Submission
Authors:
Qing Lin1, Chao Xie1, Tianye Jia1
Institutions:
1Fudan University, Shanghai, China
First Author:
Co-Author(s):
Introduction:
The emergence of network neuroscience has allowed us to investigate the brain structure and function by mapping the elements of the brain and their interaction. Individual morphological brain connectome, like MSN, MIND have been constructed to provide insights into the brain organization principles. Here we propose a new similarity network, representation mapping network（RMN）to estimate the representation similarity between cortical areas and analyzed the network across a set of analysis.
Methods:
The research used T1-weigthed image derived structure metrics (gray matter concentration) of HCP-YA cohort and the term activation of Neurosynth datasets to construct RMN. We compared RMN to MIND network to evaluate their performance on known anatomical principles of cortical organization, the similarity with other multimodal networks and gradients of macroscale cortical organization.
·Figure1.The construction of representation mapping network (RMN)
Results:
-Cortical organization：RMN was substantially more consistent across subjects than MIND networks; RMN showed a larger fraction of bilaterally symmetric connections but a lower intraclass connectivity than MIND.
-Similarity with multimodal networks: We found a stronger correlation between RMN and other ten similarity networks in terms of edge weight than MIND. The top three networks with the highest correlations were structural connectivity, resting-state connectivity, and task activation similarity networks.
-Gradient analysis：We found a stronger correlation between RMN and other ten similarity networks gradients, especially with resting-state connectivity and task activation similarity networks. The principal gradient of RMN also unfolds along the sensorimotor-association (S-A) axis across the cortex.
-Similarity with multimodal networks: We found a stronger correlation between RMN and other ten similarity networks in terms of edge weight than MIND. The top three networks with the highest correlations were structural connectivity, resting-state connectivity, and task activation similarity networks.
-Gradient analysis：We found a stronger correlation between RMN and other ten similarity networks gradients, especially with resting-state connectivity and task activation similarity networks. The principal gradient of RMN also unfolds along the sensorimotor-association (S-A) axis across the cortex.
·Figure2. Comparison of RMN and MIND networks
Conclusions:
We introduced the representation mapping network（RMN）as a new approach to investigate the brain cortical organization. RMN captured the structural and functional features of the brain and enhanced our understanding of brain organization principles.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cortex
MRI
Statistical Methods
Structures
Other - Netwok；Gradient
1|2Indicates the priority used for review
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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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Provide references using APA citation style.
Seidlitz, J.(2018). Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron, 97(1), 231–247.e7.
Hansen, J. Y. (2023). Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease. PLoS biology, 21(9), e3002314.
Vos de Wael, R. (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Communications biology, 3(1), 103.
Margulies, D. S. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences of the United States of America, 113(44), 12574–12579.