Pervasive false brain connectivity from electrophysiological signals

Pascual-Marqui, Roberto D.; Achermann, Peter; Faber, Pascal L.; Kinoshita, Toshihiko; Kochi, Kieko; Nishida, Keiichiro; Yoshimura, Masafumi

doi:10.1101/2021.01.28.428625

Cited by 2 publications

(2 citation statements)

References 24 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other estimation techniques need to be used to obtain non-biased estimators (see e.g. Pascual-Marqui et al 2021, and references therein). Simple measurement noise can create false Granger causality, when using estimation technique that do not correctly account for measurement noise.…”

Section: Discussionmentioning

confidence: 99%

Linear causal filtering: definition and theory

Pascual-Marqui

Bosch-Bayard

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This work provides a framework based on multivariate autoregressive modeling for linear causal filtering in the sense of Granger. In its bivariate form, the linear causal filter defined here takes as input signals A and B, and it filters out the causal effect of B on A, thus yielding two new signals only containing the Granger-causal effect of A on B. In its general multivariate form for more than two signals, the effect of all indirect causal connections between A and B, mediated by all other signals, are accounted for, partialled out, and filtered out also. The importance of this filter is that it enables the estimation of directional measures of causal information flow from any non-causal, non-directional measure of association. For instance, based on the classic coherence, a directional measure of strength of information flow from A to B is obtained when applied to the linear causal filtered pair containing only A to B connectivity information. This particular case is equivalent to the isolated effective coherence (doi.org/10.3389/fnhum.2014.00448). Of more recent interest are the large family of phase-phase, phase-amplitude, and amplitude-amplitude cross-frequency coupling measures which are non-directional. The linear causal filter makes it now possible to estimate the directional causal versions these measures of association. One important field of application is in brain connectivity analysis based on cortical signals of electric neuronal activity (e.g. estimated sources of EEG and MEG, and invasive intracranial ECoG recordings). The linear causal filter introduced here provides a novel solution to the problem of estimating the direction of information flow from any non-directional measure of association. This work provides definitions, non-ambiguous equations, and clear prescriptions for implementing the linear causal filter in diverse settings.

show abstract

Section: Discussionmentioning

confidence: 99%

Linear causal filtering: definition and theory

Pascual-Marqui

Bosch-Bayard

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Granger causality has many successful applications, but it has limitations in recovering causal relationships in complex networks (see Spirtes et al, 2000;Pearl, 2009). This is especially true in highly convoluted brain networks with circular causality interactions, in the presence of common-mode observation noise, field effects, and volume conduction (see Friston et al, 2014;Pesaran et al, 2018;Pascual-Marqui et al, 2021). To address some of the limitations of Granger causality, Hu et al (2011) proposed the New Causality (NC) method, which considers the proportion that Y occupies among all contributions to predict X.…”

Section: Introductionmentioning

confidence: 99%

State Transitions During Discrimination Learning in the Gerbil Auditory Cortex Analyzed by Network Causality Metrics

Kozma

Sokolov

et al. 2021

Front. Syst. Neurosci.

View full text Add to dashboard Cite

This work studies the evolution of cortical networks during the transition from escape strategy to avoidance strategy in auditory discrimination learning in Mongolian gerbils trained by the well-established two-way active avoidance learning paradigm. The animals were implanted with electrode arrays centered on the surface of the primary auditory cortex and electrocorticogram (ECoG) recordings were made during performance of an auditory Go/NoGo discrimination task. Our experiments confirm previous results on a sudden behavioral change from the initial naïve state to an avoidance strategy as learning progresses. We employed two causality metrics using Granger Causality (GC) and New Causality (NC) to quantify changes in the causality flow between ECoG channels as the animals switched to avoidance strategy. We found that the number of channel pairs with inverse causal interaction significantly increased after the animal acquired successful discrimination, which indicates structural changes in the cortical networks as a result of learning. A suitable graph-theoretical model is developed to interpret the findings in terms of cortical networks evolving during cognitive state transitions. Structural changes lead to changes in the dynamics of neural populations, which are described as phase transitions in the network graph model with small-world connections. Overall, our findings underscore the importance of functional reorganization in sensory cortical areas as a possible neural contributor to behavioral changes.

show abstract

Pervasive false brain connectivity from electrophysiological signals

Cited by 2 publications

References 24 publications

Linear causal filtering: definition and theory

Linear causal filtering: definition and theory

State Transitions During Discrimination Learning in the Gerbil Auditory Cortex Analyzed by Network Causality Metrics

Contact Info

Product

Resources

About