Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network
Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-01-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2020.596109/full |
| _version_ | 1828939706627260416 |
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| author | Dongya Wu Xin Li Jun Feng Jun Feng |
| author_facet | Dongya Wu Xin Li Jun Feng Jun Feng |
| author_sort | Dongya Wu |
| collection | DOAJ |
| description | Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA. |
| first_indexed | 2024-12-14T03:06:26Z |
| format | Article |
| id | doaj.art-1e91e45d315f45ec8c3653af570e42a3 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-14T03:06:26Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-1e91e45d315f45ec8c3653af570e42a32022-12-21T23:19:23ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-01-011410.3389/fnins.2020.596109596109Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural NetworkDongya Wu0Xin Li1Jun Feng2Jun Feng3School of Information Science and Technology, Northwest University, Xi’an, ChinaSchool of Mathematics, Northwest University, Xi’an, ChinaSchool of Information Science and Technology, Northwest University, Xi’an, ChinaState-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, Xi’an, ChinaBrain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.https://www.frontiersin.org/articles/10.3389/fnins.2020.596109/fullmulti-hops connectivitygraph neural networkindividual predictionconnectivity–function relationshipfusiform face function |
| spellingShingle | Dongya Wu Xin Li Jun Feng Jun Feng Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network Frontiers in Neuroscience multi-hops connectivity graph neural network individual prediction connectivity–function relationship fusiform face function |
| title | Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network |
| title_full | Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network |
| title_fullStr | Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network |
| title_full_unstemmed | Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network |
| title_short | Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network |
| title_sort | multi hops functional connectivity improves individual prediction of fusiform face activation via a graph neural network |
| topic | multi-hops connectivity graph neural network individual prediction connectivity–function relationship fusiform face function |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2020.596109/full |
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