Assessing and Compensating for Zero-lag Correlation Effects in Time-lagged Granger Causality Analysis of fMRI
Identifieur interne : 001F56 ( Pmc/Checkpoint ); précédent : 001F55; suivant : 001F57Assessing and Compensating for Zero-lag Correlation Effects in Time-lagged Granger Causality Analysis of fMRI
Auteurs : Gopikrishna Deshpande [États-Unis] ; K. Sathian [États-Unis] ; Xiaoping Hu [États-Unis]Source :
- IEEE transactions on bio-medical engineering [ 0018-9294 ] ; 2010.
Abstract
Effective connectivity in brain networks can be studied using Granger causality analysis which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this work showed that the zero-lag correlation does “leak” into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.
Url:
DOI: 10.1109/TBME.2009.2037808
PubMed: 20659822
PubMed Central: 3063610
Affiliations:
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Sathian</name><affiliation wicri:level="2"><nlm:aff id="A2">Departments of Neurology, Rehabilitation Medicine and Psychology, Emory University, Atlanta, GA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Departments of Neurology, Rehabilitation Medicine and Psychology, Emory University, Atlanta, GA</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation><affiliation wicri:level="2"><nlm:aff id="A3">Rehabilitation R&D Center of Excellence, Atlanta VAMC, Decatur, GA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Rehabilitation R&D Center of Excellence, Atlanta VAMC, Decatur, GA</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Hu, Xiaoping" sort="Hu, Xiaoping" uniqKey="Hu X" first="Xiaoping" last="Hu">Xiaoping Hu</name><affiliation wicri:level="2"><nlm:aff id="A1">Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author></analytic><series><title level="j">IEEE transactions on bio-medical engineering</title><idno type="ISSN">0018-9294</idno><idno type="eISSN">1558-2531</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">Effective connectivity in brain networks can be studied using Granger causality analysis which is based on temporal precedence, while functional connectivity is usually derived using zero-lag correlation. Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from fMRI data, may be contaminated by zero-lag correlation. Simulations performed in this work showed that the zero-lag correlation does “leak” into estimates of time-lagged causality. To eliminate this leak, we introduce a method in which the zero-lag influences are explicitly modeled in the vector autoregressive model but omitted while calculating Granger causality. The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.</p></div></front></TEI><pmc article-type="research-article" xml:lang="EN"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
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