Critical Comments on EEG Sensor Space Dynamical Connectivity Analysis

Luca Faes, Daniele Marinazzo, Esin Karahan, Luca Faes, Frederik Van De Steen, Pedro A. Valdes-Sosa, Jitkomut Songsiri

Risultato della ricerca: Article

31 Citazioni (Scopus)

Abstract

Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations cannot be seen as an approximation of a sourceâs anatomical location and (2) spurious connectivity can occur between sensors. Although many measures of causal connectivity derived from EEG sensor time series are affected by the latter, here we will focus on the well-known time domain index of Granger causality (GC) and on the frequency domain directed transfer function (DTF). Using the state-space framework and designing two simulation studies we show that mixing effects caused by volume conduction can lead to spurious connections, detected either by time domain GC or by DTF. Therefore, GC/DTF causal connectivity measures should be computed at the source level, or derived within analysis frameworks that model the effects of volume conduction. Since mixing effects can also occur in the source space, it is advised to combine source space analysis with connectivity measures that are robust to mixing.
Lingua originaleEnglish
pagine (da-a)643-654
Numero di pagine12
RivistaBrain Topography
Volume32
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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