Chapter title |
Topological Distances Between Brain Networks
|
---|---|
Chapter number | 19 |
Book title |
Connectomics in NeuroImaging
|
Published in |
Connectomics in neuroimaging : first International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec), September 2017
|
DOI | 10.1007/978-3-319-67159-8_19 |
Pubmed ID | |
Book ISBNs |
978-3-31-967158-1, 978-3-31-967159-8
|
Authors |
Moo K. Chung, Hyekyoung Lee, Victor Solo, Richard J. Davidson, Seth D. Pollak |
Abstract |
Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 19 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 37% |
Student > Master | 5 | 26% |
Professor | 1 | 5% |
Researcher | 1 | 5% |
Student > Postgraduate | 1 | 5% |
Other | 0 | 0% |
Unknown | 4 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Mathematics | 4 | 21% |
Engineering | 3 | 16% |
Neuroscience | 2 | 11% |
Psychology | 1 | 5% |
Computer Science | 1 | 5% |
Other | 1 | 5% |
Unknown | 7 | 37% |