» Articles » PMID: 18455441

Estimating Granger Causality After Stimulus Onset: a Cautionary Note

Overview
Journal Neuroimage
Specialty Radiology
Date 2008 May 6
PMID 18455441
Citations 44
Authors
Affiliations
Soon will be listed here.
Abstract

How the brain processes sensory input to produce goal-oriented behavior is not well-understood. Advanced data acquisition technology in conjunction with novel statistical methods holds the key to future progress in this area. Recent studies have applied Granger causality to multivariate population recordings such as local field potential (LFP) or electroencephalography (EEG) in event-related paradigms. The aim is to reveal the detailed time course of stimulus-elicited information transaction among various sensory and motor cortices. Presently, interdependency measures like coherence and Granger causality are calculated on ongoing brain activity obtained by removing the average event-related potential (AERP) from each trial. In this paper we point out the pitfalls of this approach in light of the inevitable occurrence of trial-to-trial variability of event-related potentials in both amplitudes and latencies. Numerical simulations and experimental examples are used to illustrate the ideas. Special emphasis is placed on the important role played by single trial analysis of event-related potentials in experimentally establishing the main conclusion.

Citing Articles

Identification of interacting neural populations: methods and statistical considerations.

Kass R, Bong H, Olarinre M, Xin Q, Urban K J Neurophysiol. 2023; 130(3):475-496.

PMID: 37465897 PMC: 10642974. DOI: 10.1152/jn.00131.2023.


Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements.

Borra D, Fantozzi S, Bisi M, Magosso E Sensors (Basel). 2023; 23(7).

PMID: 37050590 PMC: 10099070. DOI: 10.3390/s23073530.


Distinct cortico-muscular coupling between step and stance leg during reactive stepping responses.

Stokkermans M, Solis-Escalante T, Cohen M, Weerdesteyn V Front Neurol. 2023; 14:1124773.

PMID: 36998772 PMC: 10043329. DOI: 10.3389/fneur.2023.1124773.


Deep-layer motif method for estimating information flow between EEG signals.

Fan D, Wang H, Wang J Cogn Neurodyn. 2022; 16(4):819-831.

PMID: 35847539 PMC: 9279550. DOI: 10.1007/s11571-021-09759-x.


Grasp-squeeze adaptation to changes in object compliance leads to dynamic beta-band communication between primary somatosensory and motor cortices.

Cu H, Lynch L, Huang K, Truccolo W, Nurmikko A Sci Rep. 2022; 12(1):6776.

PMID: 35474117 PMC: 9042850. DOI: 10.1038/s41598-022-10871-z.


References
1.
Ding M, Bressler S, Yang W, Liang H . Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern. 2000; 83(1):35-45. DOI: 10.1007/s004229900137. View

2.
Kalcher J, Pfurtscheller G . Discrimination between phase-locked and non-phase-locked event-related EEG activity. Electroencephalogr Clin Neurophysiol. 1995; 94(5):381-4. DOI: 10.1016/0013-4694(95)00040-6. View

3.
Chen Y, Bressler S, Knuth K, Truccolo W, Ding M . Stochastic modeling of neurobiological time series: power, coherence, Granger causality, and separation of evoked responses from ongoing activity. Chaos. 2006; 16(2):026113. DOI: 10.1063/1.2208455. View

4.
Truccolo W, Knuth K, Shah A, Bressler S, Schroeder C, Ding M . Estimation of single-trial multicomponent ERPs: differentially variable component analysis (dVCA). Biol Cybern. 2003; 89(6):426-38. DOI: 10.1007/s00422-003-0433-7. View

5.
Lungarella M, Sporns O . Mapping information flow in sensorimotor networks. PLoS Comput Biol. 2006; 2(10):e144. PMC: 1626158. DOI: 10.1371/journal.pcbi.0020144. View