Journal article
Structure-Function Network Mapping and Its Assessment via Persistent Homology
PLoS computational biology, v 13(1), pp e1005325-e1005325
Jan 2017
PMID: 28046127
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways.
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Details
- Title
- Structure-Function Network Mapping and Its Assessment via Persistent Homology
- Creators
- Hualou Liang - School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA, United States of AmericaHongbin Wang - Center for Biomedical Informatics, Texas A&M University Health Science Center, Houston, TX, United States of America
- Publication Details
- PLoS computational biology, v 13(1), pp e1005325-e1005325
- Publisher
- Public LIbrary of Science (PLOS); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000394144400042
- Scopus ID
- 2-s2.0-85011385229
- Other Identifier
- 991014878476404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemical Research Methods
- Mathematical & Computational Biology