A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality

Sato, João Ricardo; Amaro, Edson; Takahashi, Daniel Yasumasa; Felix, Marcelo; Brammer, Michael; Morettin, Pedro A.

doi:10.1016/j.neuroimage.2005.11.039

Cited by 107 publications

(92 citation statements)

References 45 publications

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“…The influence of SMA on M1 has been consistently confirmed by many effective connectivity measurements such as Granger causality analysis (Chen et al, 2009;Gao et al, 2008Gao et al, , 2011Sato et al, 2006), structural equation modeling (Solodkin et al, 2004), and dynamic causal modeling (DCM) (Grefkes et al, 2008). These results demonstrated a predominantly facilitatory function of the SMA in the top-down processes during motor execution (Arai et al, 2012;Gao et al, 2011;Grefkes et al, 2008), and implied that ipsilateral and contralateral SMAs played different roles on contralateral M1 (Gao et al, 2011).…”

mentioning

confidence: 70%

Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

et al. 2014

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Functional imaging studies have indicated hemispheric asymmetry of activation in bilateral supplementary motor area (SMA) during unimanual motor tasks. However, the hemispherically special roles of bilateral SMAs on primary motor cortex (M1) in the effective connectivity networks (ECN) during lateralized tasks remain unclear. Aiming to study the differential contribution of bilateral SMAs during the motor execution and motor imagery tasks, and the hemispherically asymmetric patterns of ECN among regions involved, the present study used dynamic causal modeling to analyze the functional magnetic resonance imaging data of the unimanual motor execution/imagery tasks in 12 right-handed subjects. Our results demonstrated that distributions of network parameters underlying motor execution and motor imagery were significantly different. The variation was mainly induced by task condition modulations of intrinsic coupling. Particularly, regardless of the performing hand, the task input modulations of intrinsic coupling from the contralateral SMA to contralateral M1 were positive during motor execution, while varied to be negative during motor imagery. The results suggested that the inhibitive modulation suppressed the overt movement during motor imagery. In addition, the left SMA also helped accomplishing left hand tasks through task input modulation of left SMA/right SMA connection, implying that hemispheric recruitment occurred when performing nondominant hand tasks. The results specified differential and altered contributions of bilateral SMAs to the ECN during unimanual motor execution and motor imagery, and highlighted the contributions induced by the task input of motor execution/imagery.

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confidence: 70%

Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

et al. 2014

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“…This argument is the conceptual basis of Granger Causality [1,2] which is probably the most prominent method to estimate the direction of causal influence in time series analysis. Granger Causality was originally developed in econometry, but is applied to many different problems in physics, geosciences (cause of climate change), social sciences, and biology with special emphasis on neural system [3,4,5,6,7].…”

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confidence: 99%

Robustly Estimating the Flow Direction of Information in Complex Physical Systems

Nolte¹,

et al. 2008

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We propose a new measure to estimate the direction of information flux in multivariate time series from complex systems. This measure, based on the slope of the phase spectrum (Phase Slope Index) has invariance properties that are important for applications in real physical or biological systems: (a) it is strictly insensitive to mixtures of arbitrary independent sources, (b) it gives meaningful results even if the phase spectrum is not linear, and (c) it properly weights contributions from different frequencies. Simulations of a class of coupled multivariate random data show that for truly unidirectional information flow without additional noise contamination our measure detects the correct direction as good as the standard Granger causality. For random mixtures of independent sources Granger Causality erroneously yields highly significant results whereas our measure correctly becomes non-significant. An application of our novel method to EEG data (88 subjects in eyes-closed condition) reveals a strikingly clear front-to-back information flow in the vast majority of subjects and thus contributes to a better understanding of information processing in the brain.

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“…Although the choice between GCA and model-based method like DCM is currently debated in literature (David, 2009), (Roebroeck et al, 2009), at the state of art no theoretical reasons exist to exclude the effectiveness of GCA analysis to infer connectivity on BOLD signal (Roebroeck et al, 2009). GCA approach was indeed successfully applied to fMRI data for studying brain connectivity during cognitive (Roebroeck et al, 2005), (Demirci et al, 2009), (Sato et al, 2010), (Ide et al, 2011), (Shippers et al, 2011, (Seger et al, 2011), sensory , (Stilla et al, 2007), , (Havlicek et al, 2010), and motor tasks (Abler et al, 2006), (Sato et al, 2006), (Chen et al, 2009) and for investigating resting-state networks, (Liao et al, 2011), (Jiao et al, 2011) and in pathological conditions like epilepsy (Tana et al, submitted). One of the methodology more widely used to calculate Granger causality are is based on multivariate autoregressive models which are fitted to the signals (EEG or BOLD) of interest.…”

Section: Data-driven Effective Connectivity: Granger Causality Analysismentioning

confidence: 99%

EEG-fMRI Multimodal Integration for Epilepsy Research

Bianchi¹,

Franchin²,

Tana³

2011

Management of Epilepsy - Research, Results and Treatment

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A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality

Cited by 107 publications

References 45 publications

Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

Differential Contribution of Bilateral Supplementary Motor Area to the Effective Connectivity Networks Induced by Task Conditions Using Dynamic Causal Modeling

Robustly Estimating the Flow Direction of Information in Complex Physical Systems

EEG-fMRI Multimodal Integration for Epilepsy Research

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