Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment
Abstract Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node‐centric network model, which only calculates correlations between brain regions. Considering the interaction of low‐order correlations between pairs of brain regions, we use an edge‐centri...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
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| Series: | Brain-X |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/brx2.35 |
| _version_ | 1797231268811767808 |
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| author | Weiping Wang Ruiying Du Zhen Wang Xiong Luo Haiyan Zhao Ping Luan Jipeng Ouyang Song Liu |
| author_facet | Weiping Wang Ruiying Du Zhen Wang Xiong Luo Haiyan Zhao Ping Luan Jipeng Ouyang Song Liu |
| author_sort | Weiping Wang |
| collection | DOAJ |
| description | Abstract Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node‐centric network model, which only calculates correlations between brain regions. Considering the interaction of low‐order correlations between pairs of brain regions, we use an edge‐centric network model to study high‐order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high‐amplitude co‐fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co‐fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high‐order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI. |
| first_indexed | 2024-04-24T15:41:41Z |
| format | Article |
| id | doaj.art-5afa18b59e71447aaf0ee30a4f0b5621 |
| institution | Directory Open Access Journal |
| issn | 2835-3153 |
| language | English |
| last_indexed | 2024-04-24T15:41:41Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain-X |
| spelling | doaj.art-5afa18b59e71447aaf0ee30a4f0b56212024-04-01T19:00:07ZengWileyBrain-X2835-31532023-09-0113n/an/a10.1002/brx2.35Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairmentWeiping Wang0Ruiying Du1Zhen Wang2Xiong Luo3Haiyan Zhao4Ping Luan5Jipeng Ouyang6Song Liu7School of Computer and Communication Engineering University of Science and Technology Beijing Beijing ChinaSchool of Computer and Communication Engineering University of Science and Technology Beijing Beijing ChinaThe Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi ChinaSchool of Computer and Communication Engineering University of Science and Technology Beijing Beijing ChinaDepartment of Neurology Peking University Third Hospital Beijing ChinaGuangdong Second People's Hospital Guangzhou Guangdong ChinaDepartment of Neurology Shunde Hospital Southern Medical University Shunde Guangdong ChinaSoutheast Asia Project Manager Department of China Petroleum Pipeline Engineering Co., Ltd. Langfang Hebei ChinaAbstract Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI) rely on a node‐centric network model, which only calculates correlations between brain regions. Considering the interaction of low‐order correlations between pairs of brain regions, we use an edge‐centric network model to study high‐order functional network correlations. Here, we compute edge time series (eTS) to obtain overlapping communities and study the relationship between subnetworks and communities in space. Then, based on the overlapping communities, we calculate the normalized entropy to measure the diversity of each node. Next, we compute the high‐amplitude co‐fluctuation of the eTS to explore the pattern of brain activity with temporal precision. Our results show that the normal control and EMCI patients differ in brain regions, subnetworks, and the whole brain. In particular, entropy values show a gradual decrease, and brain network co‐fluctuation increases with disease progression. Our study is the first to investigate the pathogenesis of EMCI from the perspective of spatiotemporal flexibility and cognitive diversity based on high‐order edge connectivity, further characterizing brain dynamics and providing new insights into the search for biomarkers of EMCI.https://doi.org/10.1002/brx2.35co‐fluctuationedge‐centricfunctional flexibilityoverlapping communityspatiotemporal |
| spellingShingle | Weiping Wang Ruiying Du Zhen Wang Xiong Luo Haiyan Zhao Ping Luan Jipeng Ouyang Song Liu Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment Brain-X co‐fluctuation edge‐centric functional flexibility overlapping community spatiotemporal |
| title | Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment |
| title_full | Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment |
| title_fullStr | Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment |
| title_full_unstemmed | Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment |
| title_short | Edge‐centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment |
| title_sort | edge centric functional network reveals new spatiotemporal biomarkers of early mild cognitive impairment |
| topic | co‐fluctuation edge‐centric functional flexibility overlapping community spatiotemporal |
| url | https://doi.org/10.1002/brx2.35 |
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