Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Groß, Joachim; Kluger, Daniel S.; Abbasi, Omid; Chalas, Nikolas; Steingräber, Nadine; Daube, Christoph; Schoffelen, Jan‐Mathijs

doi:10.1016/j.neuroimage.2021.118660

Cited by 12 publications

(13 citation statements)

References 37 publications

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“…Comparison of the Listening and Self-listening conditions can thereby assess latency shifts and other changes associated with increased predictability. Mutual information measures based on the Gaussian copula ( Ince et al, 2017 ) are used to quantify the amount of information obtained about brain activity through observing the speech envelope ( Gross et al, 2021 ). We analysed the peak of lagged Gaussian Copula Mutual Information (GCMI) to distinguish the timing of maximal brain entrainment to speech.…”

Section: Introductionmentioning

confidence: 99%

Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

et al. 2022

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“…Directed dependency measures per se thus never warrant the inference of causality. This problem is however alleviated in cerebro-peripheral settings, where the dependency of neuroimaging signals on external signals is studied (Gross et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Over the last decades, the description of statistical dependencies in cerebro-cerebral and cerebro-peripheral pairs of time series has witnessed a surge of interest (Bassett & Bullmore, 2006;Brookes et al, 2011;Naselaris et al, 2011;Crosse et al, 2016;Mell et al, 2021;Gross et al, 2021). In these fields, the general idea is to gain insight into the workings of the brain by either studying how time series of neuronal activity relate to other neuronal activity or to external signals such as auditory or visual stimuli as well as the activity of other organs.…”

Section: Introductionmentioning

confidence: 99%

A whitening approach for Transfer Entropy permits the application to narrow-band signals

Daube¹,

Groß²,

Ince³

2022

Preprint

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Transfer Entropy, a generalisation of Granger Causality, promises to measure "information transfer" from a source to a target signal by ignoring self-predictability of a target signal when quantifying the source-target relationship. A simple example for signals with such selfpredictability are narrowband signals. These are both thought to be intrinsically generated by the brain as well as commonly dealt with in analyses of brain signals, where band-pass filters are used to separate responses from noise. However, the use of Transfer Entropy is usually discouraged in such cases. We simulate simplistic examples where we confirm the failure of classic implementations of Transfer Entropy when applied to narrow-band signals, as made evident by a flawed recovery of effect sizes and interaction delays. We propose an alternative approach based on a whitening of the input signals before computing a bivariate measure of directional time-lagged dependency. This approach solves the problems found in the simple simulated systems. Finally, we explore the behaviour of our measure when applied to delta and theta response components in Magnetoencephalography (MEG) responses to continuous speech. The small effects that our measure attributes to a directed interaction from the stimulus to the neuronal responses are stronger in the theta than in the delta band. This suggests that the delta band reflects a more predictive coupling, while the theta band is stronger involved in bottom-up, reactive processing. Taken together, we hope to increase the interest in directed perspectives on frequency-specific dependencies.

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“…The monotonicity assumption for applying the GCMI between two univariate continuous time-series raises the question of the potential application to resting-state and long time-series. While this is still an openquestion, it was recently shown that the GCMI and the weighted pairwise phase consistency outperform compared to other metrics for measuring the connectivity between brain and peripheral signals (Gross et al, 2021). This study used relatively long time-series, between 1 to 9 minutes and also showed that with longer time-series the mean distance between the connectivity measure and the surrogate distribution also increased.…”

Section: Discussionmentioning

confidence: 99%

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

Combrisson

Allegra

Basanisi

et al. 2021

Preprint

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The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuity in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present a open-source Python toolbox called Frites that includes all of the methods used here, from functional connectivity estimations to the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

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Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Cited by 12 publications

References 37 publications

Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

Timing of brain entrainment to the speech envelope during speaking, listening and self-listening

A whitening approach for Transfer Entropy permits the application to narrow-band signals

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

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