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Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

Lionel Barnett1, Anil K Seth1

  • 1Sackler Centre for Consciousness Science and School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UK.

Journal of Neuroscience Methods
|November 10, 2016
PubMed
Summary
This summary is machine-generated.

Subsampling neurophysiological data can obscure true causal connections. This study reveals how sampling rates interact with neural signal time scales to affect Granger causality detection, offering insights for accurate functional connectivity analysis.

Keywords:
Continuous-time processDistributed lagsGranger causalitySubsampling

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Granger causality is widely used in neuroscience to infer directed functional connectivity from time-series data.
  • Subsampling continuous-time neurophysiological processes can introduce spurious causal links and obscure existing ones.
  • The impact of subsampling on detecting true causal connections remains less understood.

Purpose of the Study:

  • To theoretically analyze the effects of subsampling on Granger-causal inference in neurophysiological data.
  • To investigate the relationship between sampling frequency, underlying causal time scales, and the detectability of Granger causality.
  • To provide practical insights for improving the reliable detection of causal connectivity.

Main Methods:

  • Developed a theoretical analysis based on a distributed-lag, continuous-time stochastic model.
  • Employed exact analytical solutions to examine Granger causality at finite prediction horizons.
  • Investigated the interplay between signal propagation time scales and sampling frequency.

Main Results:

  • Detectability of causal connections decays exponentially as sampling intervals increase beyond causal delays.
  • Identified specific sampling frequencies that act as "black spots" and "sweet spots" for causality detection.
  • Demonstrated that downsampling can, in some cases, improve Granger-causal detectability.
  • Showed that Granger causality invariance under filtering fails at finite prediction horizons, impacting fMRI data analysis.

Conclusions:

  • Sampling rates for neurophysiological time-series analysis must be informed by domain-specific time scales.
  • State-space modeling is recommended over purely autoregressive modeling for Granger-causal inference.
  • The study offers practical guidance for identifying confounds and enhancing causal connectivity detection from neurophysiological recordings.