Chapter title |
Exact Topological Inference for Paired Brain Networks via Persistent Homology
|
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Chapter number | 24 |
Book title |
Information Processing in Medical Imaging
|
Published in |
Information processing in medical imaging proceedings of the conference, June 2017
|
DOI | 10.1007/978-3-319-59050-9_24 |
Pubmed ID | |
Book ISBNs |
978-3-31-959049-3, 978-3-31-959050-9
|
Authors |
Moo K. Chung, Victoria Villalta-Gil, Hyekyoung Lee, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald |
Abstract |
We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric statistical inference procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time. As illustrations, we apply the method to simulated networks and a twin fMRI study. In case of the latter, we determine the statistical significance of the heritability index of the large-scale reward network where every voxel is a network node. |
X Demographics
Geographical breakdown
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 30 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 11 | 37% |
Student > Master | 5 | 17% |
Researcher | 4 | 13% |
Professor | 3 | 10% |
Student > Bachelor | 2 | 7% |
Other | 1 | 3% |
Unknown | 4 | 13% |
Readers by discipline | Count | As % |
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Neuroscience | 5 | 17% |
Mathematics | 4 | 13% |
Engineering | 4 | 13% |
Computer Science | 3 | 10% |
Physics and Astronomy | 2 | 7% |
Other | 6 | 20% |
Unknown | 6 | 20% |