The influence of filtering and downsampling on the estimation of transfer entropy

Weber, Immo; Florin, Esther; Papen, Michael von; Timmermann, Lars

doi:10.1371/journal.pone.0188210

Cited by 25 publications

(25 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The functional interaction of neural assemblies distributed across different brain regions underlies many cognitive and perceptual processes (Bastos and Schoffelen, 2016). Therefore, understanding such processes, and brain function at large, requires identifying the flow of information within networks of connected neural assemblies, instead of solely focusing on the activity of specific brain regions in isolation (Sakkalis, 2011; Weber et al, 2017). The analysis of the interactions mentioned above is carried out through brain connectivity measures (Friston, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…While GC and DCM are widely used in neuroscience, TE has gained increasing attention in the literature (Timme and Lapish, 2018), because of the advantages it offers as compared with other effective connectivity measures. Unlike classic GC, TE can capture high order correlations, and it is well suited to detect purely nonlinear interactions in the data, which are believed to be part of brain activity on many spatial and temporal scales (Weber et al, 2017). Although DCM can capture nonlinear interactions too, it requires some a priori knowledge on the input of the system and on the target connectivity network, which is not always available (Vicente et al, 2011); in this sense, TE is model free.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy

2019

View full text Add to dashboard Cite

Transfer entropy (TE) is a model-free effective connectivity measure based on information theory. It has been increasingly used in neuroscience because of its ability to detect unknown non-linear interactions, which makes it well suited for exploratory brain effective connectivity analyses. Like all information theoretic quantities, TE is defined regarding the probability distributions of the system under study, which in practice are unknown and must be estimated from data. Commonly used methods for TE estimation rely on a local approximation of the probability distributions from nearest neighbor distances, or on symbolization schemes that then allow the probabilities to be estimated from the symbols' relative frequencies. However, probability estimation is a challenging problem, and avoiding this intermediate step in TE computation is desirable. In this work, we propose a novel TE estimator using functionals defined on positive definite and infinitely divisible kernels matrices that approximate Renyi's entropy measures of order α. Our data-driven approach estimates TE directly from data, sidestepping the need for probability distribution estimation. Also, the proposed estimator encompasses the well-known definition of TE as a sum of Shannon entropies in the limiting case when α → 1. We tested our proposal on a simulation framework consisting of two linear models, based on autoregressive approaches and a linear coupling function, respectively, and on the public electroencephalogram (EEG) database BCI Competition IV, obtained under a motor imagery paradigm. For the synthetic data, the proposed kernel-based TE estimation method satisfactorily identifies the causal interactions present in the data. Also, it displays robustness to varying noise levels and data sizes, and to the presence of multiple interaction delays in the same connected network. Obtained results for the motor imagery task show that our approach codes discriminant spatiotemporal patterns for the left and right-hand motor imagination tasks, with classification performances that compare favorably to the state-of-the-art.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy

2019

View full text Add to dashboard Cite

show abstract

“…However we did not explicitly monitor the difference in attentional level between the conditions so we cannot rule out that condition-specific differences in attention affected the results. Second, similar to Granger causality, the computation of TE on bandpass-filtered data is not without problems (Florin, Gross, Pfeifer, Fink, & Timmermann, 2010 ; Weber, Florin, von Papen, & Timmermann, 2017 ). Statistically contrasting two conditions will counteract these problems.…”

Section: Discussionmentioning

confidence: 99%

Predictive entrainment of natural speech through two fronto-motor top-down channels

Park

Thut

Groß

2018

Language, Cognition and Neuroscience

View full text Add to dashboard Cite

Natural communication between interlocutors is enabled by the ability to predict upcoming speech in a given context. Previously we showed that these predictions rely on a fronto-motor top-down control of low-frequency oscillations in auditory-temporal brain areas that track intelligible speech. However, a comprehensive spatio-temporal characterisation of this effect is still missing. Here, we applied transfer entropy to source-localised MEG data during continuous speech perception. First, at low frequencies (1-4 Hz, brain delta phase to speech delta phase), predictive effects start in left fronto-motor regions and progress to right temporal regions. Second, at higher frequencies (14-18 Hz, brain beta power to speech delta phase), predictive patterns show a transition from left inferior frontal gyrus via left precentral gyrus to left primary auditory areas. Our results suggest a progression of prediction processes from higher-order to early sensory areas in at least two different frequency channels.

show abstract

“…However, we acknowledge that backward played speech is not a perfect control condition due to differences in the finer temporal structure and attention to the stimulus. Second, similar to Granger causality, the computation of TE on bandpass-filtered data is not without problems (Florin, Gross, Pfeifer, Fink, & Timmermann, 2010;Weber, Florin, von Papen, & Timmermann, 2017). Statistically contrasting two conditions will counteract these problems.…”

Section: Discussionmentioning

confidence: 99%