File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains

TitleAnatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains
Authors
KeywordsFunctional connectivity
functional magnetic resonance imaging (fMRI)
gyri and sulci
spatio-temporal graph convolutional network (STGCN)
Issue Date2022
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2022 How to Cite?
AbstractThe cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.
Persistent Identifierhttp://hdl.handle.net/10722/330838
ISSN
2023 Impact Factor: 10.2
2023 SCImago Journal Rankings: 4.170
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, Mingxin-
dc.contributor.authorChen, Yuzhong-
dc.contributor.authorYan, Jiadong-
dc.contributor.authorXiao, Zhenxiang-
dc.contributor.authorMao, Wei-
dc.contributor.authorZhao, Boyu-
dc.contributor.authorYang, Shimin-
dc.contributor.authorZhao, Zhongbo-
dc.contributor.authorZhang, Tuo-
dc.contributor.authorGuo, Lei-
dc.contributor.authorBecker, Benjamin-
dc.contributor.authorYao, Dezhong-
dc.contributor.authorKendrick, Keith M.-
dc.contributor.authorJiang, Xi-
dc.date.accessioned2023-09-05T12:15:05Z-
dc.date.available2023-09-05T12:15:05Z-
dc.date.issued2022-
dc.identifier.citationIEEE Transactions on Neural Networks and Learning Systems, 2022-
dc.identifier.issn2162-237X-
dc.identifier.urihttp://hdl.handle.net/10722/330838-
dc.description.abstractThe cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Neural Networks and Learning Systems-
dc.subjectFunctional connectivity-
dc.subjectfunctional magnetic resonance imaging (fMRI)-
dc.subjectgyri and sulci-
dc.subjectspatio-temporal graph convolutional network (STGCN)-
dc.titleAnatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TNNLS.2022.3194733-
dc.identifier.scopuseid_2-s2.0-85135749804-
dc.identifier.eissn2162-2388-
dc.identifier.isiWOS:000840485100001-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats