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MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices
Hongzhou Wu1, Zhenzhen Yang1, Qingquan Cao1
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan 611731, China.
This study introduces a new computational method, Multi-criteria Quantitative Graph Analysis (MQGA), to precisely identify connector and provincial hubs in brain networks. The MQGA method offers reliable and stable analysis, crucial for understanding cognitive functions and brain diseases.
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Connector and provincial hubs are crucial for cognitive tasks, coordinating information flow within and between brain modules.
- Current methods for identifying these hubs lack standardized criteria, limiting quantitative analysis and comparability.
Purpose of the Study:
- To develop and validate a novel computational method, Multi-criteria Quantitative Graph Analysis (MQGA), for quantitative analysis of hub attributes in brain networks.
- To enhance the accuracy, reliability, and stability of identifying connector (con) and provincial (pro) hub nodes.
Main Methods:
- Utilized multi-graph theoretical index calculations, including betweenness centrality, degree centrality, and participation coefficient.
- Introduced the Multi-criteria Quantitative Graph Analysis (MQGA) method to derive con and pro hub indices.
- Validated the method using benchmark networks, simulated networks, and resting-state fMRI data from ADHD and healthy control groups.
Main Results:
- MQGA demonstrated accuracy, reliability, and stability in identifying hub nodes.
- Network sparsity influenced hub indices: con hub index increased, pro hub index decreased, with optimal identification at 4% sparsity.
- Removing connector nodes significantly impacted network integrity more than removing provincial nodes.
- Hub score stability was lower in disease groups (ADHD) compared to healthy controls.
Conclusions:
- The MQGA method provides a precise and replicable approach for identifying brain network hubs.
- Findings advance the understanding of hub node roles in cognition and their alterations in brain diseases.
- The method's sensitivity and consistency are vital for future brain network research and clinical applications.
