Modelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals

Hemmelmann, D.; Ungureanu, Mihaela; Hesse, Wolfram; Wüstenberg, Torsten; Reichenbach, Jürgen R.; Witte, Otto W.; Witte, Herbert; Leistritz, Lutz

doi:10.1016/j.neuroimage.2008.12.065

Cited by 32 publications

(34 citation statements)

References 38 publications

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“…It has been used to address control of the default-mode network [35,36] and the changing influences between networks with respect to age [37]. Nonetheless, due in part to temporal blurring induced by the hemodynamic response, the potential utility of effective connectivity to resting-state fMRI data, without experimental manipulation, remains a source of debate (see the section Correlation and Causality in [13]) and methodological innovation (e.g., [38]). The rest of the current review, however, will focus on methods for the analysis of functional connectivity.…”

Section: Techniquesmentioning

confidence: 99%

Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity

Margulies

Böttger

Long

et al. 2010

Magn Reson Mater Phy

217

164

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Analytic tools for addressing spontaneous brain activity, as acquired with fMRI during the "resting-state," have grown dramatically over the past decade. Along with each new technique, novel hypotheses about the functional organization of the brain are also available to researchers. We review six prominent categories of resting-state fMRI data analysis: seed-based functional connectivity, independent component analysis, clustering, pattern classification, graph theory, and two "local" methods. In surveying these methods, we address their underlying assumptions, methodologies, and novel applications.

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Section: Techniquesmentioning

confidence: 99%

Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity

Margulies

Böttger

Long

et al. 2010

Magn Reson Mater Phy

217

164

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“…An alternative would be to expand the model with terms allowing connectivity to change with context, as is done with dynamic causal modeling [6]. The assumption of stationarity may be relaxed, allowing the estimation of time-varying connectivity, using reasonable parameterizations [59], methods that require only local stationarity [60], adaptive MAR models [42] or other means to permit time variation of the model parameters [61]. …”

Section: Extensions and Refinementsmentioning

confidence: 99%

Functional MRI and multivariate autoregressive models

Rogers

Katwal

Morgan

et al. 2010

Magnetic Resonance Imaging

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Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays, and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series, and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.

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“…1 There are several applications for Granger causality analysis in different fields, such as: economics, 5,6 bioinformatics, 7,8 geophysics, 9,10 and neuroscience. [11][12][13] With fMRI data, recent studies have interested to derive Granger causality maps by applied Granger causality analysis among a target region of interest and all other voxels in the brain [14][15][16] and applied a conditional Granger causality analysis to evaluate the effective connectivity of resting state networks. 17,18 Two additional developments of Granger's causality idea are important.…”

Section: Granger Causalitymentioning

confidence: 99%

fMRI resting state time series causality: comparison of Granger causality and phase slope index

Saleh

2014

Int J Res Med Sci

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Modelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals

Cited by 32 publications

References 38 publications

Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity

Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity

Functional MRI and multivariate autoregressive models

fMRI resting state time series causality: comparison of Granger causality and phase slope index

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