Inferring neural connectivity via measured delay in directed information estimates

Soltani, Nima; Goldsmith, Andrea

doi:10.1109/isit.2013.6620677

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…DI plays a central role in causality analysis for two reasons. First, it is a universal method that does not have any modeling constraints on the sequences to be evaluated [29,30]. Second, DI serves as the pivot that links existing causality models GC [10,18], transfer entropy (TE) [9,31,32], and dynamic causal modeling (DCM) [33,34] through conditional equivalence between them.…”

Section: Geometric Illustration Of Convergent Cross Mappingmentioning

confidence: 99%

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Sun,

Deng,

Scheel

et al. 2024

Entropy

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Rooted in dynamic systems theory, convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting linear and nonlinear causal coupling in both random and deterministic settings. One limitation with CCM is that it uses both past and future values to predict the current value, which is inconsistent with the widely accepted definition of causality, where it is assumed that the future values of one process cannot influence the past of another. To overcome this obstacle, in our previous research, we introduced the concept of causalized convergent cross mapping (cCCM), where future values are no longer used to predict the current value. In this paper, we focus on the implementation of cCCM in causality analysis. More specifically, we demonstrate the effectiveness of cCCM in identifying both linear and nonlinear causal coupling in various settings through a large number of examples, including Gaussian random variables with additive noise, sinusoidal waveforms, autoregressive models, stochastic processes with a dominant spectral component embedded in noise, deterministic chaotic maps, and systems with memory, as well as experimental fMRI data. In particular, we analyze the impact of shadow manifold construction on the performance of cCCM and provide detailed guidelines on how to configure the key parameters of cCCM in different applications. Overall, our analysis indicates that cCCM is a promising and easy-to-implement tool for causality analysis in a wide spectrum of applications.

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Section: Geometric Illustration Of Convergent Cross Mappingmentioning

confidence: 99%

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Sun,

Deng,

Scheel

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

“…Conditional MI is zero if and only if X N and Y N are conditionally independent given Z N −1 . By comparing conditional mutual information (57) with the expression for CBI in (42), we notice that CBI uses causal conditioning as Z N −1 is replaced with Z n−1 .…”

Section: A Causal Bidirectional Information (Cbi)mentioning

confidence: 99%

Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality

Mehta

Kliewer

2017

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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In this paper, electroencephalography (EEG) measurements are used to infer change in cortical functional connectivity in response to change in audio stimulus. Experiments are conducted wherein the EEG activity of human subjects is recorded as they listen to audio sequences whose quality varies with time. A causal information theoretic framework is then proposed to measure the information flow between EEG sensors appropriately grouped into different regions of interest (ROI) over the cortex. A new causal bidirectional information (CBI) measure is defined as an improvement over standard directed information measures for the purposes of identifying connectivity between ROIs in a generalized cortical network setting. CBI can be intuitively interpreted as a causal bidirectional modification of directed information, and inherently calculates the divergence of the observed data from a multiple access channel with feedback. Further, we determine the analytical relationship between the different causal measures and compare how well they are able to distinguish between the perceived audio quality. The connectivity results inferred indicate a significant change in the rate of information flow between ROIs as the subjects listen to different audio qualities, with CBI being the best in discriminating between the perceived audio quality, compared to using standard directed information measures.

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A new EEG-based causal information measure for identifying brain connectivity in response to perceived audio quality

Mehta

Kliewer

2017

2017 IEEE International Conference on Communications (ICC)

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Inferring neural connectivity via measured delay in directed information estimates

Cited by 7 publications

References 10 publications

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Causalized Convergent Cross Mapping and Its Implementation in Causality Analysis

Directional and Causal Information Flow in EEG for Assessing Perceived Audio Quality

A new EEG-based causal information measure for identifying brain connectivity in response to perceived audio quality

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