A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

Kim, Sanggyun; Putrino, David; Ghosh, Soumya; Brown, Emery N.

doi:10.1371/journal.pcbi.1001110

Cited by 203 publications

(226 citation statements)

References 44 publications

(63 reference statements)

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“…For two simultaneously measured time series, one is called causal to the other if the predictability of the second process at a given time point is improved by including measurements from the immediate past of the first. GC has been shown to be suitable for probing directionality in neuronal interactions for both continuous signals (26)(27)(28)(29) and spike trains (30)(31)(32). However, the pairwise approach to GC analysis may not clearly distinguish causal influences from different sources.…”

Section: Discussionmentioning

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: Discussionmentioning

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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“…The main challenge is that point process spike train data is not suitable for modelling by linear VAR models. A theoretically principled approach is to replace the VAR modelling step with fitting of full and reduced point process models, within a framework of maximum likelihood estimation (Okatan et al, 2005;Truccolo et al, 2005;Kim et al, 2011;Gerhard et al, 2013). The MVGC toolbox does not support this in its current version.…”

Section: Application To Spiking (Ie Point Process) Datamentioning

confidence: 99%

The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference

Barnett

Seth

2014

Journal of Neuroscience Methods

787

805

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Background: Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling.New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy.Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s):The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions:The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference.

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“…Hypothesis tests for such connections have been described in the time series analysis literature (30,46). This approach is analogous to that proposed by Kim et al (1) for point process models of neural spiking data. To our knowledge, the VAR versions (30,46) of this approach have not been used in neuroscience applications, but may offer potential advantages in computational and statistical efficiency compared with more widely applied permutation-based tests on causality measures, such as those outlined in ref.…”

Section: Resultsmentioning

confidence: 95%

A study of problems encountered in Granger causality analysis from a neuroscience perspective

Stokes

Purdon

2017

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

236

176

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, we misunderstood the method described in Barnett and Seth (25) to compute the conditional Granger causality. We realize now that the spectral factorization method they describe can be used to obtain the conditional Granger causality with a single model fit, which would avoid the computational problems associated with separate model fits. We apologize for this error." Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger-Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system's dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.Granger causality | time series analysis | neural oscillations | connectivity | system identification G ranger causality is a statistical tool developed to analyze the flow of information between time series. Neuroscientists have applied Granger causality methods to diverse sources of data, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and local field potentials (LFP). These studies have investigated functional neural systems at scales of organization from the cellular level (1-3) to whole-brain network activity (4), under a range of conditions, including sensory stimuli (5-7), varying levels of consciousness (8-10), cognitive tasks (11), and pathological states (12, 13). In such analyses, the time series data are interpreted to reflect neural activity from a particular source, and Granger causality is used to characterize the directionality, directness, and dynamics of influence between sources.Oscillations are a ...

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A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

Cited by 203 publications

References 44 publications

Interactions between feedback and lateral connections in the primary visual cortex

Interactions between feedback and lateral connections in the primary visual cortex

The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference

A study of problems encountered in Granger causality analysis from a neuroscience perspective

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