Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks
Abstract We analyze networks of functional correlations between brain regions to identify changes in their structure caused by Attention Deficit Hyperactivity Disorder (adhd). We express the task for finding changes as a network anomaly detection problem on temporal networks. We propose the use of a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-87587-z |
| _version_ | 1818736125704929280 |
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| author | Tanima Chatterjee Réka Albert Stuti Thapliyal Nazanin Azarhooshang Bhaskar DasGupta |
| author_facet | Tanima Chatterjee Réka Albert Stuti Thapliyal Nazanin Azarhooshang Bhaskar DasGupta |
| author_sort | Tanima Chatterjee |
| collection | DOAJ |
| description | Abstract We analyze networks of functional correlations between brain regions to identify changes in their structure caused by Attention Deficit Hyperactivity Disorder (adhd). We express the task for finding changes as a network anomaly detection problem on temporal networks. We propose the use of a curvature measure based on the Forman–Ricci curvature, which expresses higher-order correlations among two connected nodes. Our theoretical result on comparing this Forman–Ricci curvature with another well-known notion of network curvature, namely the Ollivier–Ricci curvature, lends further justification to the assertions that these two notions of network curvatures are not well correlated and therefore one of these curvature measures cannot be used as an universal substitute for the other measure. Our experimental results indicate nine critical edges whose curvature differs dramatically in brains of adhd patients compared to healthy brains. The importance of these edges is supported by existing neuroscience evidence. We demonstrate that comparative analysis of curvature identifies changes that more traditional approaches, for example analysis of edge weights, would not be able to identify. |
| first_indexed | 2024-12-18T00:32:11Z |
| format | Article |
| id | doaj.art-d059dd5758ab48b8b4455c31f954b891 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-18T00:32:11Z |
| publishDate | 2021-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d059dd5758ab48b8b4455c31f954b8912022-12-21T21:27:06ZengNature PortfolioScientific Reports2045-23222021-04-0111111410.1038/s41598-021-87587-zDetecting network anomalies using Forman–Ricci curvature and a case study for human brain networksTanima Chatterjee0Réka Albert1Stuti Thapliyal2Nazanin Azarhooshang3Bhaskar DasGupta4Department of Computer Science, University of Illinois at ChicagoDepartment of Physics, Pennsylvania State UniversityDepartment of Computer Science, University of Illinois at ChicagoDepartment of Computer Science, University of Illinois at ChicagoDepartment of Computer Science, University of Illinois at ChicagoAbstract We analyze networks of functional correlations between brain regions to identify changes in their structure caused by Attention Deficit Hyperactivity Disorder (adhd). We express the task for finding changes as a network anomaly detection problem on temporal networks. We propose the use of a curvature measure based on the Forman–Ricci curvature, which expresses higher-order correlations among two connected nodes. Our theoretical result on comparing this Forman–Ricci curvature with another well-known notion of network curvature, namely the Ollivier–Ricci curvature, lends further justification to the assertions that these two notions of network curvatures are not well correlated and therefore one of these curvature measures cannot be used as an universal substitute for the other measure. Our experimental results indicate nine critical edges whose curvature differs dramatically in brains of adhd patients compared to healthy brains. The importance of these edges is supported by existing neuroscience evidence. We demonstrate that comparative analysis of curvature identifies changes that more traditional approaches, for example analysis of edge weights, would not be able to identify.https://doi.org/10.1038/s41598-021-87587-z |
| spellingShingle | Tanima Chatterjee Réka Albert Stuti Thapliyal Nazanin Azarhooshang Bhaskar DasGupta Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks Scientific Reports |
| title | Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks |
| title_full | Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks |
| title_fullStr | Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks |
| title_full_unstemmed | Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks |
| title_short | Detecting network anomalies using Forman–Ricci curvature and a case study for human brain networks |
| title_sort | detecting network anomalies using forman ricci curvature and a case study for human brain networks |
| url | https://doi.org/10.1038/s41598-021-87587-z |
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