Analyzing information flow in brain networks with nonparametric Granger causality

Dhamala, Mukesh; Rangarajan, Govindan; Ding, Mingzhou

doi:10.1016/j.neuroimage.2008.02.020

Cited by 350 publications

(353 citation statements)

References 57 publications

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“…To make the conditional GC directly applicable to spike trains, we took a nonparametric approach (43). In our implementation, to construct the spectral density matrix, the spectral estimate of spike trains was directly applied to the neural point process itself (i.e., sequences of spike times rather than the spike counts), using the multitaper technique (44).…”

Section: Methodsmentioning

confidence: 99%

Interactions between feedback and lateral connections in the primary visual cortex

Liang

Gong

Chen

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Perceptual grouping of line segments into object contours has been thought to be mediated, in part, by long-range horizontal connectivity intrinsic to the primary visual cortex (V1), with a contribution by top-down feedback projections. To dissect the contributions of intraareal and interareal connections during contour integration, we applied conditional Granger causality analysis to assess directional influences among neural signals simultaneously recorded from visual cortical areas V1 and V4 of monkeys performing a contour detection task. Our results showed that discounting the influences from V4 markedly reduced V1 lateral interactions, indicating dependence on feedback signals of the effective connectivity within V1. On the other hand, the feedback influences were reciprocally dependent on V1 lateral interactions because the modulation strengths from V4 to V1 were greatly reduced after discounting the influences from other V1 neurons. Our findings suggest that feedback and lateral connections closely interact to mediate image grouping and segmentation.perceptual grouping | contour integration | Granger causality | horizontal connection | feedback connection A key step in the visual system's analysis of object shape is to group line segments into global contours and segregate these contours from background features. This process is critical to identifying object boundaries in complex visual scenes, and thus particularly important for performing shape discrimination; image segmentation; and, ultimately, object recognition.Contour integration follows the Gestalt principle of good continuation (1). The underlying neural underpinnings have been characterized as an association field (2), which links contour elements that are part of smooth contours. Neurophysiological studies in monkeys have identified that the primary visual cortex (V1) makes a fundamental contribution to contour integration (3-6), and anatomical studies have shown that the topology of horizontal connections in V1 is well suited for mediating interactions between neurons with a similar orientation preference (7-10). Such intracortical circuitry in V1 has been implemented in many computational models to account for the successful process of contour integration (11-13). Although many lines of converging evidence suggest that V1 is intimately involved in contour integration, circuitbased models have to take into account the findings that contour grouping is more than a bottom-up or hard-wired process, but that it is strongly dependent on top-down feedback influences (5, 14-17). Surface segmentation, another important intermediate stage in processing of visual images, is also mediated by interactions between feedforward and feedback connections (18).We have proposed a model whereby cortical feedback contributes to the effective connectivity of horizontal connections within V1 (13,19). A possible role of higher cortical areas in this process is to disambiguate local image components by creating a template that is fed back to V1, which then can select...

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Section: Methodsmentioning

confidence: 99%

Interactions between feedback and lateral connections in the primary visual cortex

Liang

Gong

Chen

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Factorization of this spectral density matrix gives the unique decomposition 28) where H(ω) is the transfer function matrix and Σ the noise covariance matrix for the subsystem of interest. Second, consider another spectral density matrix…”

Section: (D) Spectral Density Matrix Factorizationmentioning

confidence: 99%

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Wen

Rangarajan

Ding

2013

Phil. Trans. R. Soc. A.

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Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.

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“…For improvements of this method refer to [20]. A nonparametric alternative has been proposed by Dhamala et al [6] and consists in computing the spectral decomposition matrices used by the previous spectral GC formulations with the Wilson-Burg method for spectral factorization [21]. The basic principle of this method is the factorization of the spectral density matrix into a unique set of minimum phase functions Ψ.…”

Section: Granger Causalitymentioning

confidence: 99%

“…Recently, nonparametric methods for spectral GC have been developed allowing this measure and its variants that depend on the autoregressive (AR) model estimation to be computed based on Fourier transform (FT) or wavelet transform (WT) [6]. This methodology has evident advantages for the GC computation as it does not rely on the AR model estimation and therefore does not depend on estimating the correct model order or having appropriate model consistency.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Granger Causality with the Hilbert-Huang Transform

Rodrigues

Andrade

2013

ISRN Signal Processing

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Current measures of causality and temporal precedence have limited frequency and time resolution and therefore may not be viable in the detection of short periods of causality in specific frequencies. In addition, the presence of nonstationarities hinders the causality estimation of current techniques as they are based on Fourier transforms or autoregressive model estimation. In this work we present a combination of techniques to measure causality and temporal precedence between stationary and nonstationary time series, that is sensitive to frequency-specific short episodes of causality. This methodology provides a highly informative timefrequency representation of causality with existing causality measures. This is done by decomposing each time series into intrinsic oscillatory modes with an empirical mode decomposition algorithm and, subsequently, calculating their complex Hilbert spectrum. At each time point the cross-spectrum is calculated between time series and used to measure coherency and compute the transfer function and error covariance matrices using the Wilson-Burg method for spectral factorization. The imaginary part of coherency can then be computed as well as several Granger causality measures in the previous matrices. This work covers the most important theoretical background of these techniques and tries to prove the usefulness of this new approach while pointing out some of its qualities and drawbacks.

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Analyzing information flow in brain networks with nonparametric Granger causality

Cited by 350 publications

References 57 publications

Interactions between feedback and lateral connections in the primary visual cortex

Interactions between feedback and lateral connections in the primary visual cortex

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Instantaneous Granger Causality with the Hilbert-Huang Transform

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