Granger causal influence predicts BOLD activity levels in the default mode network
Identifieur interne : 003113 ( Main/Merge ); précédent : 003112; suivant : 003114Granger causal influence predicts BOLD activity levels in the default mode network
Auteurs : Qing Jiao [République populaire de Chine] ; Guangming Lu [République populaire de Chine] ; Zhiqiang Zhang [République populaire de Chine] ; Yuan Zhong [République populaire de Chine] ; Zhengge Wang [République populaire de Chine] ; Yongxin Guo [République populaire de Chine] ; Kai Li [République populaire de Chine] ; Mingzhou Ding [États-Unis] ; Yijun Liu [États-Unis]Source :
- Human Brain Mapping [ 1065-9471 ] ; 2011-01.
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Abstract
Although the brain areas in the default‐mode network (DMN) act in a coordinated way during rest, the activity levels in the individual areas of the DMN are highly heterogeneous. The relation between the activity levels and the pattern of causal interaction among the DMN areas remains unknown. In the present fMRI study, seven nodes of the DMN were identified and their activity levels were rank‐ordered based on a power spectral analysis of the resting blood oxygenation level‐dependent (BOLD) signals. Furthermore, the direction of information flow among these DMN nodes was determined using Granger causality analysis and graph‐theoretic methods. We found that the activity levels in these seven DMN nodes had a highly consistent hierarchical distribution, with the highest activity level in the posterior cingulate/precuneus cortices, followed by ventral medial prefrontal cortex and dorsal medial prefrontal cortex, and with the lowest level in the left inferior temporal gyrus. Importantly, a significant correlation was found between the activity levels and the In‐Out degrees of information flow across the DMN nodes, suggesting that Granger causal influences can be used to predict BOLD activity levels. These findings shed light on the dynamical organization of cortical neuronal networks and may provide the basis for characterizing network disruption by brain disorders. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.
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DOI: 10.1002/hbm.21065
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level="a" type="main" xml:lang="en">Granger causal influence predicts BOLD activity levels in the default mode network</title><author><name sortKey="Jiao, Qing" sort="Jiao, Qing" uniqKey="Jiao Q" first="Qing" last="Jiao">Qing Jiao</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Imaging, Nanjing Jinling Hospital, Medical School of Nanjing University, Nanjing</wicri:regionArea><wicri:noRegion>Nanjing</wicri:noRegion></affiliation><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Radiology, Taishan Medical University, Taian</wicri:regionArea><wicri:noRegion>Taian</wicri:noRegion></affiliation></author><author><name sortKey="Lu, Guangming" sort="Lu, Guangming" uniqKey="Lu G" first="Guangming" last="Lu">Guangming Lu</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Imaging, Nanjing Jinling Hospital, Medical School of Nanjing University, Nanjing</wicri:regionArea><wicri:noRegion>Nanjing</wicri:noRegion></affiliation></author><author><name sortKey="Zhang, Zhiqiang" sort="Zhang, Zhiqiang" uniqKey="Zhang Z" first="Zhiqiang" last="Zhang">Zhiqiang Zhang</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Imaging, Nanjing Jinling Hospital, Medical School of Nanjing University, Nanjing</wicri:regionArea><wicri:noRegion>Nanjing</wicri:noRegion></affiliation></author><author><name sortKey="Zhong, Yuan" sort="Zhong, Yuan" uniqKey="Zhong Y" first="Yuan" last="Zhong">Yuan Zhong</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Imaging, Nanjing Jinling Hospital, Medical School of Nanjing University, Nanjing</wicri:regionArea><wicri:noRegion>Nanjing</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Zhengge" sort="Wang, Zhengge" uniqKey="Wang Z" first="Zhengge" last="Wang">Zhengge Wang</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Medical Imaging, Nanjing Jinling Hospital, Medical School of Nanjing University, Nanjing</wicri:regionArea><wicri:noRegion>Nanjing</wicri:noRegion></affiliation></author><author><name sortKey="Guo, Yongxin" sort="Guo, Yongxin" uniqKey="Guo Y" first="Yongxin" last="Guo">Yongxin Guo</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Radiology, Taishan Medical University, Taian</wicri:regionArea><wicri:noRegion>Taian</wicri:noRegion></affiliation></author><author><name sortKey="Li, Kai" sort="Li, Kai" uniqKey="Li K" first="Kai" last="Li">Kai Li</name><affiliation wicri:level="1"><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Department of Pharmacology, Suzhou University, Suzhou</wicri:regionArea><wicri:noRegion>Suzhou</wicri:noRegion></affiliation></author><author><name sortKey="Ding, Mingzhou" sort="Ding, Mingzhou" uniqKey="Ding M" first="Mingzhou" last="Ding">Mingzhou Ding</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><placeName><region type="state">Floride</region></placeName><wicri:cityArea>The J. 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Brain Mapp.</title><idno type="ISSN">1065-9471</idno><idno type="eISSN">1097-0193</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2011-01">2011-01</date><biblScope unit="volume">32</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="154">154</biblScope><biblScope unit="page" to="161">161</biblScope></imprint><idno type="ISSN">1065-9471</idno></series><idno type="istex">B4215C541FFA6287C6E800074031DA583DAED741</idno><idno type="DOI">10.1002/hbm.21065</idno><idno type="ArticleID">HBM21065</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1065-9471</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Granger causality analysis</term><term>default‐mode network</term><term>information flow</term><term>power</term><term>spontaneous neuronal activity</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Although the brain areas in the default‐mode network (DMN) act in a coordinated way during rest, the activity levels in the individual areas of the DMN are highly heterogeneous. The relation between the activity levels and the pattern of causal interaction among the DMN areas remains unknown. In the present fMRI study, seven nodes of the DMN were identified and their activity levels were rank‐ordered based on a power spectral analysis of the resting blood oxygenation level‐dependent (BOLD) signals. Furthermore, the direction of information flow among these DMN nodes was determined using Granger causality analysis and graph‐theoretic methods. We found that the activity levels in these seven DMN nodes had a highly consistent hierarchical distribution, with the highest activity level in the posterior cingulate/precuneus cortices, followed by ventral medial prefrontal cortex and dorsal medial prefrontal cortex, and with the lowest level in the left inferior temporal gyrus. Importantly, a significant correlation was found between the activity levels and the In‐Out degrees of information flow across the DMN nodes, suggesting that Granger causal influences can be used to predict BOLD activity levels. These findings shed light on the dynamical organization of cortical neuronal networks and may provide the basis for characterizing network disruption by brain disorders. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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