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Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering
Hanbo Chen1, Kaiming Li, Dajiang Zhu
1Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
Summary
This study introduces a new method to find consistent brain sub-networks using diffusion tensor imaging (DTI) and functional MRI (fMRI) data. The approach improves the reliability of brain network analysis across different imaging types and individuals.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Network Science
Background:
- Quantitative analysis of structural and functional brain networks using diffusion tensor imaging (DTI) and functional MRI (fMRI) is a growing field.
- The consistency of brain networks across different neuroimaging modalities and individuals remains largely unexplored.
Purpose of the Study:
- To develop a novel approach for inferring group-wise consistent brain sub-networks from multimodal DTI/fMRI datasets.
- To enhance the regularity of structural and functional brain networks across modalities and subjects.
Main Methods:
- Utilized multi-view spectral clustering on cortical networks derived from large-scale cortical landmarks.
- Applied the algorithm to 80 multimodal structural and functional brain networks from 40 healthy subjects.
Main Results:
- Successfully inferred consistent multimodal brain sub-networks within the studied group.
- Demonstrated improved inter-modality and inter-subject consistency of the derived brain sub-networks.
Conclusions:
- The proposed method effectively identifies consistent brain sub-networks from multimodal neuroimaging data.
- This approach advances the understanding of brain network regularity and consistency in neuroscience research.
