Literature DB >> 17826182

Single trial variability of EEG and fMRI responses to visual stimuli.

Andrew P Bagshaw1, Tracy Warbrick.   

Abstract

Recent EEG-fMRI studies have suggested a novel method of data fusion which uses single trial (ST) estimates of event-related potentials in the fMRI analysis. This is potentially very powerful, but rests on the assumption that the ST variability observed in EEG is reflected in the fMRI signal. The current study investigated this assumption and compared two different data processing strategies for each modality. Five subjects underwent separate EEG and fMRI sessions with checkerboard stimuli at two contrasts. EEG data were preprocessed using wavelet denoising and independent component analysis (ICA), whilst the general linear model and ICA were used for fMRI. Amplitudes and latencies of the P1 and N2 components of the visual evoked potential (VEP) were calculated for each trial. For fMRI, the amplitudes and latencies of the ST haemodynamic responses (HR) were calculated. Within modality, the results for the two processing methods were significantly correlated in the majority of data sets. Across modality, the average amplitudes of the VEPs and HRs were also significantly correlated. Examination of ST variability demonstrated that the amplitudes of the mean VEPs and HRs are both influenced by the latency variability of the ST responses to a greater extent than the amplitude variability. For high contrast stimuli the latency variability in EEG and fMRI was significantly correlated, with a similar trend seen for the low contrast stimuli. The results confirm the validity of examining both the EEG and fMRI signals on an ST basis and suggest an underlying neuronal origin in both modalities.

Mesh:

Year:  2007        PMID: 17826182     DOI: 10.1016/j.neuroimage.2007.07.042

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  5 in total

1.  Recording visual evoked potentials and auditory evoked P300 at 9.4T static magnetic field.

Authors:  Jorge Arrubla; Irene Neuner; David Hahn; Frank Boers; N Jon Shah
Journal:  PLoS One       Date:  2013-05-01       Impact factor: 3.240

Review 2.  Mining EEG-fMRI using independent component analysis.

Authors:  Tom Eichele; Vince D Calhoun; Stefan Debener
Journal:  Int J Psychophysiol       Date:  2009-02-15       Impact factor: 2.997

Review 3.  When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI.

Authors:  Catriona L Scrivener
Journal:  Front Neurosci       Date:  2021-06-29       Impact factor: 4.677

4.  Data-driven analysis of simultaneous EEG/fMRI using an ICA approach.

Authors:  Lena Schmüser; Alexandra Sebastian; Arian Mobascher; Klaus Lieb; Oliver Tüscher; Bernd Feige
Journal:  Front Neurosci       Date:  2014-07-01       Impact factor: 4.677

5.  Maximum-likelihood estimation of channel-dependent trial-to-trial variability of auditory evoked brain responses in MEG.

Authors:  Cezary Sielużycki; Paweł Kordowski
Journal:  Biomed Eng Online       Date:  2014-06-16       Impact factor: 2.819

  5 in total

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