Hierarchical ordering with partial pairwise hierarchical relationships on the macaque brain data sets

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Hierarchical organizations of information processing in the brain networks have been known to exist and widely studied. To find proper hierarchical structures in the macaque brain, the traditional methods need the entire pairwise hierarchical relationships between cortical areas. In this paper, we present a new method that discovers hierarchical structures of macaque brain networks by using partial information of pairwise hierarchical relationships. Our method uses a graph-based manifold learning to exploit inherent relationship, and computes pseudo distances of hierarchical levels for every pair of cortical areas. Then, we compute hierarchy levels of all cortical areas by minimizing the sum of squared hierarchical distance errors with the hierarchical information of few cortical areas. We evaluate our method on the macaque brain data sets whose true hierarchical levels are known as the FV91 model. The experimental results show that hierarchy levels computed by our method are similar to the FV91 model, and its errors are much smaller than the errors of hierarchical clustering approaches.
Publisher
PUBLIC LIBRARY SCIENCE
Issue Date
2017-05
Language
English
Article Type
Article
Keywords

FUNCTIONAL CONNECTIVITY; NETWORKS; ORGANIZATION; CORTEX; SYSTEMS; AREAS

Citation

PLoS ONE, v.12, no.5, pp.1 - 15

ISSN
1932-6203
DOI
10.1371/journal.pone.0177373
URI
http://hdl.handle.net/10203/224060
Appears in Collection
CS-Journal Papers(저널논문)EE-Journal Papers(저널논문)
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