Localizing and estimating causal relations of interacting brain rhythms

Guido Nolte, Klaus Robert Müller

Research output: Contribution to journalReview articlepeer-review

32 Citations (Scopus)

Abstract

Estimating brain connectivity and especially causality between different brain regions from EEG or MEG is limited by the fact that the data are a largely unknown superposition of the actual brain activities. Any method, which is not robust to mixing artifacts, is prone to yield false positive results. We here review a number of methods that allow for addressing this problem. They are all based on the insight that the imaginary part of the cross-spectra cannot be explained as a mixing artifact. First, a joined decomposition of these imaginary parts into pairwise activities separates subsystems containing different rhythmic activities. Second, assuming that the respective source estimates are least overlapping, yields a separation of the rhythmic interacting subsystem into the source topographies themselves. Finally, a causal relation between these sources can be estimated using the newly proposed measure Phase Slope Index (PSI). This work, for the first time, presents the above methods in combination; all illustrated using a single, simulated data set.

Original languageEnglish
JournalFrontiers in Human Neuroscience
Volume4
DOIs
Publication statusPublished - 2010

Keywords

  • Causality
  • EEG
  • Interaction
  • MOCA
  • PSI
  • Pisa
  • Volume conduction

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Neurology
  • Psychiatry and Mental health
  • Biological Psychiatry
  • Behavioral Neuroscience

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