• Corpus ID: 222140861

Multi-scale graph principal component analysis for connectomics

@article{Winter2020MultiscaleGP,
  title={Multi-scale graph principal component analysis for connectomics},
  author={Steven N Winter and Zhengwu Zhang and David B. Dunson},
  journal={arXiv: Methodology},
  year={2020}
}
In brain connectomics, the cortical surface is parcellated into different regions of interest (ROIs) prior to statistical analysis. The brain connectome for each individual can then be represented as a graph, with the nodes corresponding to ROIs and edges to connections between ROIs. Such a graph can be summarized as an adjacency matrix, with each cell containing the strength of connection between a pair of ROIs. These matrices are symmetric with the diagonal elements corresponding to self… 

Figures from this paper

PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into

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