Exponential Random Graph Modeling for Complex Brain Networks
Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especia...
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Public Library of Science
2011
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102079/ |
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pubmed-3102079 |
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pubmed-31020792011-06-06 Exponential Random Graph Modeling for Complex Brain Networks Simpson, Sean L. Hayasaka, Satoru Laurienti, Paul J. Research Article Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks. Public Library of Science 2011-05-25 /pmc/articles/PMC3102079/ /pubmed/21647450 http://dx.doi.org/10.1371/journal.pone.0020039 Text en Simpson et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Simpson, Sean L. Hayasaka, Satoru Laurienti, Paul J. |
| spellingShingle |
Simpson, Sean L. Hayasaka, Satoru Laurienti, Paul J. Exponential Random Graph Modeling for Complex Brain Networks |
| author_facet |
Simpson, Sean L. Hayasaka, Satoru Laurienti, Paul J. |
| author_sort |
Simpson, Sean L. |
| title |
Exponential Random Graph Modeling for Complex Brain Networks |
| title_short |
Exponential Random Graph Modeling for Complex Brain Networks |
| title_full |
Exponential Random Graph Modeling for Complex Brain Networks |
| title_fullStr |
Exponential Random Graph Modeling for Complex Brain Networks |
| title_full_unstemmed |
Exponential Random Graph Modeling for Complex Brain Networks |
| title_sort |
exponential random graph modeling for complex brain networks |
| description |
Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks. |
| publisher |
Public Library of Science |
| publishDate |
2011 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102079/ |
| _version_ |
1611455777415888896 |