Mapping directed influence over the brain using Granger causality and fMRI

被引:723
|
作者
Roebroeck, A [1 ]
Formisano, E [1 ]
Goebel, R [1 ]
机构
[1] Univ Maastricht, Fac Psychol, Dept Cognit Neurosci, NL-6200 MD Maastricht, Netherlands
关键词
effective connectivity; fMRE Granger causality; autoregressive models;
D O I
10.1016/j.neuroimage.2004.11.017
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity. (C) 2004 Elsevier Inc. All rights reserved.
引用
收藏
页码:230 / 242
页数:13
相关论文
共 50 条
  • [31] Estimating directed brain-brain and brain-heart connectivity through globally conditioned Granger causality approaches
    Duggento, A.
    Passamonti, L.
    Guerrisi, M.
    Valenza, G.
    Barbieri, R.
    Toschi, N.
    2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2017, : 4367 - 4370
  • [32] Probing brain effective connectivity in early MS patients with Granger causality analysis of task-fMRI
    Valente Duarte, J.
    Santos, J.
    Abreu, R.
    Soares, J.
    Batista, S.
    Lima, A. C.
    Sousa, L.
    Castelo-Branco, M.
    MULTIPLE SCLEROSIS JOURNAL, 2021, 27 (2_SUPPL) : 470 - 471
  • [33] Analyzing Coherent Brain Networks with Granger Causality
    Ding, Mingzhou
    Mo, Jue
    Schroeder, Charles E.
    Wen, Xiaotong
    2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2011, : 5916 - 5918
  • [34] Brain Entropy Mapping Using fMRI
    Wang, Ze
    Li, Yin
    Childress, Anna Rose
    Detre, John A.
    PLOS ONE, 2014, 9 (03):
  • [35] Directed Network Defects in Alzheimer's Disease Using Granger Causality and Graph Theory
    Sun, Man
    Xie, Hua
    Tang, Yan
    CURRENT ALZHEIMER RESEARCH, 2020, 17 (10) : 939 - 947
  • [36] The Relation between Granger Causality and Directed Information Theory: A Review
    Amblard, Pierre-Olivier
    Michel, Olivier J. J.
    ENTROPY, 2013, 15 (01) : 113 - 143
  • [37] Directed information graphs for the Granger causality of multivariate time series
    Gao, Wei
    Cui, Wanqi
    Ye, Wenna
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 486 : 701 - 710
  • [38] GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data
    Tana, Maria Gabriella
    Sclocco, Roberta
    Bianchi, Anna Maria
    COMPUTERS IN BIOLOGY AND MEDICINE, 2012, 42 (10) : 943 - 956
  • [39] Large-Scale Kernelized Granger Causality (lsKGC) for Inferring Topology of Directed Graphs in Brain Networks
    Vosoughi, M. Ali
    Wismueller, Axel
    MEDICAL IMAGING 2022: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2022, 12036
  • [40] Functional Connectivity Networks in the Autistic and Healthy Brain Assessed using Granger Causality
    Pollonini, Luca
    Patidar, Udit
    Situ, Ning
    Rezaie, Roozbeh
    Papanicolaou, Andrew C.
    Zouridakis, George
    2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2010, : 1730 - 1733