Multivariate autoregressive modeling of fMRI time series

Harrison, L.; Penny, Will D.; Friston, Karl J.

doi:10.1016/s1053-8119(03)00160-5

Cited by 325 publications

(234 citation statements)

References 22 publications

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“…The alternative to these mapping techniques is to use a model that attempts to describe the relationships between a number of selected regions of interest, wherein region-specific measurements such as MRI time series are extracted from whole-brain data prior to the connectivity modeling stage. This category includes structural equation modeling [5] as well as multivariate autoregressive modeling [6]. Correlation, principal components analysis, and partial least squares methods may be applied in this way as well, but these are less common approaches.…”

Section: Statistical Methods For the Analysis Of Connectivitymentioning

confidence: 99%

Assessing functional connectivity in the human brain by fMRI

Rogers

Morgan

Newton

et al. 2007

Magnetic Resonance Imaging

393

279

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Functional magnetic resonance imaging is widely used to detect and delineate regions of the brain that change their level of activation in response to specific stimuli and tasks. Simple activation maps depict only the average level of engagement of different regions within distributed systems. FMRI potentially can reveal additional information about the degree by which components of large-scale neural systems are functionally coupled together to achieve specific tasks. In order to better understand how brain regions contribute to functionally connected circuits, it is necessary to record activation maps either as a function of different conditions, at different times or in different subjects. Data obtained under different conditions may then be analyzed by a variety of techniques to infer correlations and couplings between nodes in networks. Several different multivariate statistical methods have been adapted and applied to analyze the variations within such data. An approach of particular interest that is suited to studies of connectivity within single subjects makes use of acquisitions of runs of MRI images obtained while the brain is in a so-called steady state, either at rest (i.e. without any specific stimulus or task) or in a condition of continuous activation. The interregional correlations between fluctuations of MRI signal potentially reveal functional connectivity. Recent studies have established that interregional correlations between different components of circuits in each of the visual, language, motor and working memory systems can be detected in the resting state. The correlations at baseline are changed during the performance of a continuous task. In this review the various methods available for assessing connectivity are described and evaluated.

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Section: Statistical Methods For the Analysis Of Connectivitymentioning

confidence: 99%

Assessing functional connectivity in the human brain by fMRI

Rogers

Morgan

Newton

et al. 2007

Magnetic Resonance Imaging

393

279

View full text Add to dashboard Cite

show abstract

“…These data were acquired during an attention to visual motion paradigm and have been used to illustrate psychophysiological interactions, structural equation modelling, multivariate autoregressive models, Kalman filtering, variational filtering, EM and DEM Friston, 1997, 1998;Friston et al, 2003Friston et al, , 2008Harrison et al, 2003;Stephan et al, 2008). Here, we revisit questions about the generation of distributed responses by analysing the data using conventional deterministic DCMs (EM), stochastic DCMs under the mean-field approximation (DEM) and generalised filtering (GF).…”

Section: Stochastic Dcmmentioning

confidence: 99%

Generalised filtering and stochastic DCM for fMRI

et al. 2011

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“…In functional neuroimaging, brain networks are primarily studied in terms of functional connectivity (defined as temporal correlations between remote neurophysiologic events) and effective connectivity (defined as the causal influence one neuronal system exerts over another) [Friston, 1995]. Though the two prominent approaches to characterizing effective connectivity-structural equation modeling [McIntosh et al, 1994) and dynamic causal modeling [Friston et al, 2003]-have their advantages and disadvantages, neither of them incorporate information on temporal precedence, which may be considered as a necessary condition for causality. Also, these techniques require an a priori specification of an anatomical network model and are therefore best suited to making inferences on a limited number of possible networks.complexity becomes intractable and the numerical procedure becomes unstable.…”

Section: Introductionmentioning

confidence: 99%

“…Simulations by Kus et al [2004] have shown that a complete set of observations from a process have to be used to obtain causal relationships between them and that pair-wise estimates may yield incorrect results. To date, multivariate measures of Granger causality have been largely limited to electrophysiological data Ding et al, 2000;Kaminski et al, 2001;Kus et al, 2004] although multivariate autoregressive models have been used to infer functional connectivity from fMRI data [Harrison et al, 2003]. We have previously presented preliminary forms of the study described here [Deshpande et al, 2006a,b].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Granger causality analysis of fMRI data

Deshpande

LaConte

James

et al. 2008

Human Brain Mapping

216

214

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This article describes the combination of multivariate Ganger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network.

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Multivariate autoregressive modeling of fMRI time series

Cited by 325 publications

References 22 publications

Assessing functional connectivity in the human brain by fMRI

Assessing functional connectivity in the human brain by fMRI

Generalised filtering and stochastic DCM for fMRI

Multivariate Granger causality analysis of fMRI data

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