Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems

Winterhalder, Matthias; Schelter, Björn; Hesse, Wolfram; Schwab, Karin; Leistritz, Lutz; Klan, Daniel; Bauer, Reinhard; Timmer, Jens; Witte, Herbert

doi:10.1016/j.sigpro.2005.07.011

Cited by 165 publications

(133 citation statements)

References 37 publications

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“…Although PDC has proved to be an accurate tool for the detection of direct connectivities, both in cases of coupled oscillators (Baccalá and Sameshima 2001;Winterhalder et al 2005;Gourévitch et al 2006;Schelter et al 2006a) or simple neuronal models of interconnected neurons (Sameshima and Baccalá 1999;Kamiński et al 2001;Astolfi et al 2007), it has been shown that large differences in the variances of the modeled time series can yield distortions in the resulting PDC values (Winterhalder et al 2005;Baccala et al 2007). For example, a set of three uncorrelated white noise processes, where two of them have much larger variance than the third, will produce a distorted connectivity profile, since PDC wrongly detects connections from the low-variance process to the other two (Winterhalder et al 2005).…”

Section: Partial Directed Coherencementioning

confidence: 99%

“…For example, a set of three uncorrelated white noise processes, where two of them have much larger variance than the third, will produce a distorted connectivity profile, since PDC wrongly detects connections from the low-variance process to the other two (Winterhalder et al 2005). To avoid such distortions deriving from differences in time series scaling, a variation of the original PDC, the so called generalized PDC (gPDC) was introduced ).…”

Section: Partial Directed Coherencementioning

confidence: 99%

“…Among them, the closely related partial directed coherence (PDC) (Sameshima and Baccalá 1999;Baccalá and Sameshima 2001) and Directed Transfer Function (DTF) (Kamiński and Blinowska 1991;Kamiński et al 2001) have been applied in numerous studies of either simplified tests (Baccalá and Sameshima 2001;Kus et al 2004;Winterhalder et al 2005) or real neurophysiological data (reviewed by Pereda et al 2005). A variation of PDC, the generalized PDC, was also recently introduced to cope with time series with widely different variances ).…”

Section: Introductionmentioning

confidence: 99%

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Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence

et al. 2010

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A significant challenge in modern neuroscience lies in determining the functional connectivity between discrete populations of neurones and brain regions. In this study, a variation of partial directed coherence, the generalized partial directed coherence (gPDC), along with a newly proposed critical value for gPDC, were applied on recorded local field potentials (LFPs) and single-unit activity, in order to assess information flow between medial prefrontal cortex (mPFC) and hippocampus and within the hippocampus of the rat brain, under isoflurane anesthesia and kainic acid-induced enhanced neuronal activity. Our findings suggest that, under anesthesia, there exists a continuous information flow from hippocampus towards mPFC, reversed mostly during activity bursts occurring in the mPFC. Moreover, there was a clear directional connection from the lateral towards medial dorsal hippocampus, most prominent in the beta frequency band (10-30 Hz). Kainic acid resulted in partially disrupting the reciprocal cortico-hippocampal connectivity and reversing the intra-hippocampal one. The biological implications of these findings on the effects of anesthesia and kainic acid in brain connectivity, along with implementation issues of gPDC analysis on field potentials and spike trains, are extensively discussed.

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Section: Partial Directed Coherencementioning

confidence: 99%

Section: Partial Directed Coherencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence

et al. 2010

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“…In general, the advanced CCM method we applied provides the possibility to investigate nonlinear and timevariant directed interactions between time-series but is inflicted with the limitation of a bivariate implementation. Linear GC-based measures such as AR-based gPDC are time-variant and multivariate, and often also work in the presence of nonlinearity (for example see [35], comprehensive comparison of CCM and GC in the supplement of [13]). Because a time-varying investigation of TE is not trivial [12], TE was not adapted in our current study but should be investigated for these data in the future.…”

Section: Results Of Investigations Of Bivariate Ccm Are Given In Figumentioning

confidence: 99%

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

Schiecke

Pester

Piper

et al. 2017

EPJ Nonlinear Biomed. Phys.

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Abstract. Background. The quantification of directed interactions within the brain and in particular their time courses are of highest interest for the investigation of epilepsy. The underlying coordinated neuronal mass activities span functionally diverse and structurally widely distributed cortical and subcortical brain regions, i.e. dynamic, distributed epileptic network can be assumed possibly not fitting in the concept of linearity. Consequently, nonlinear, time-variant, and directed connectivity and synchronization analysis could be helpful to understand processes contributing to the seizure onset and propagation. Methods. The nonlinear convergent cross mapping (CCM) quantifies directed interactions between time series by using nonlinear state space reconstruction. CCM is applied to the EEG of 18 children with temporal lobe epilepsy (TLE), i.e. directed interactions within EEG activity and within specific components of EEG activity (d-activity and a-activity) are investigated. Linear time-variant multivariate AR modeling was performed for these data to test for subsequent applications of linear AR-based connectivity measures. Results. Linear MVAR models proved to be inappropriate for our data. Time-varying application of CCM revealed that statistically significant nonlinear interactions within the EEG activity and within specific components of the EEG exist in the preictal, ictal, and postictal periods. Distinct time courses of such interactions and differences in the time pattern of interactions occurring in the different components of EEG activity that we investigated discovered the high complexity of the underlying processes. No distinct results could be found concerning the presumed directionalities of interactions. Statistical relevant interactions were quantified by bootstrapping and surrogate data approach. Conclusion. Advanced nonlinear CCM approach was able to uncover time pattern of nonlinear interactions thereby possibly contributing to the further understanding of complex behavior of the brain during TLE. Our investigation may provide deeper insight into physiological state of complex networks, e.g. during the development of an epileptic seizure or the recovery in the postictal state.Keywords: convergent cross mapping / directed interactions / electroencephalogram / empirical mode decomposition / nonlinear time-variant analysis / temporal lobe epilepsy BackgroundThe detection and quantification of directed (causal) interactions within the brain and their time courses are of highest interest in neuroscience in general and in particular for the investigation of epilepsy. Temporal lobe epilepsy (TLE) is the most common medically intractable epilepsy in adults, which is frequently associated with mesial hippocampal sclerosis (mesial TLE), characterized by severe neuronal cell loss and change of glial cells in the hippocampus. The zone concept of epileptic seizure generation implies that the hippocampus and other areas of the temporal cortex can act as epileptic focus for seizureonset generation (fo...

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“…They thus allow for capturing the complex interplay such as feedbacks and spillover e¤ects, and are used to build directed and undirected, as well as weighted and binary networks here. Despite its apparent simplicity, it has been suggested by Zhou et al (2014) in integrate-and-…re neuronal systems (see also Winterhalder et al, 2005) that Granger non-causality can capture non-linear relationships.…”

Section: Introductionmentioning

confidence: 99%

Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone

et al. 2016

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In this paper, we analyze the dynamic relationships between ten stock exchanges of the euro zone using Granger causal networks. Considering returns for which we allow the variance to follow a Markov-Switching GARCH or a Changing-Point GARCH process, we …rst show that over di¤erent periods, the topology of the network is highly unstable. In particular dynamic relationships vanish over very recent years. Then, expandingon this idea, we analyze patterns of information transmission within the network. Using rolling windows to study networks' topology in terms of information clustering, we …nd that the nodes'state changes continually.Moreover, the system exhibits periods of ‡ickering in information transmission. During these periods of ‡ickering, the system also exhibits desynchronization in the information transmission process. These periods do precede tipping points or phase transitions on the market, especially before the global …nancial crisis, and can thus be used as early warnings. To our knowledge, this is the …rst time that ‡ickering in information transmission

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Comparison of linear signal processing techniques to infer directed interactions in multivariate neural systems

Cited by 165 publications

References 37 publications

Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence

Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone

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