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Exact Topological Inference for Paired Brain Networks via Persistent Homology
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chung, Moo K. Villalta-Gil, Victoria Lee, Hyekyoung Rathouz, Paul J. Lahey, Benjamin B. Zald, David H. |
| Copyright Year | 2017 |
| 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. |
| Starting Page | 299 |
| Ending Page | 310 |
| Page Count | 12 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://pages.stat.wisc.edu/~mchung/papers/chung.2017.IPMI.pdf |
| Alternate Webpage(s) | http://www.stat.wisc.edu/~mchung/papers/chung.2017.IPMI.pdf |
| Alternate Webpage(s) | http://www.biorxiv.org/content/biorxiv/early/2017/05/22/140533.full.pdf |
| Alternate Webpage(s) | https://www.wikidata.org/entity/Q42641278 |
| PubMed reference number | 29075089v1 |
| Alternate Webpage(s) | https://doi.org/10.1007/978-3-319-59050-9_24 |
| DOI | 10.1007/978-3-319-59050-9_24 |
| Journal | IPMI |
| Volume Number | 2017 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Graph theory Homologous Gene Homology (biology) Homology modeling Hyperactive behavior Inference Multimodal interaction Node - plant part P-Value Persistent homology Resampling (statistics) Sparse matrix Time complexity Voxel exponential fMRI |
| Content Type | Text |
| Resource Type | Article |
