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On characterizing population commonalities and subject variations in brain networks
Yasser Ghanbari1, Luke Bloy2, Birkan Tunc3
1Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States .
Medical Image Analysis
|December 18, 2015
Summary
This study introduces a novel multi-layer graph clustering method to identify brain network hubs. This approach accurately captures population-level connectivity patterns and individual variations, aiding in the study of neurodevelopmental disorders like autism spectrum disorder (ASD).
Area of Science:
- Neuroscience
- Network Science
- Computational Biology
Background:
- Brain network analysis using resting-state connectivity and diffusion imaging reveals insights into neurodevelopment and pathology.
- Existing methods struggle to simultaneously capture population-level connectivity patterns and individual subject variations in brain sub-networks.
Purpose of the Study:
- To develop and validate a novel multi-layer graph clustering method for extracting brain network hubs.
- To characterize population-level atlases of network hubs and individual variations in connectivity.
- To apply this method to compare typically developing controls (TDCs) and children with autism spectrum disorder (ASD).
Main Methods:
- Designed a multi-layer graph clustering technique to identify 'network hubs'—highly interconnected node clusters.
- Determined population-level network hub atlases and subject-specific connectivity weights.
- Applied the method to structural and functional brain networks of TDC and ASD populations.
Main Results:
- Successfully extracted a network hub atlas applicable to both structural and functional brain connectivity.
- Quantified subject-wise variations in within- and between-hub connectivity.
- Demonstrated the method's utility in differentiating between ASD and TDC populations based on network hub characteristics.
Conclusions:
- The proposed multi-layer graph clustering method effectively reduces brain network dimensionality for statistical analysis.
- This technique provides a robust framework for studying population and individual differences in brain connectivity, applicable to various neurodevelopmental conditions.
- The identified network hubs offer valuable insights into the neurobiology of autism spectrum disorder.
