Concurrent EEG/fMRI analysis by multiway Partial Least Squares

By Eduardo Martínez-Montes, Pedro A. Valdes-Sosa, F. Miwakeichi, Robin I. Goldman, Mark S. Cohen. NeuroImage, 2004

336 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3966b33f9bee0c7891f9f42c9b45bd3e404e8dd0

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In Cognitive Neuroscience

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Citations in the corpus

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, background): “Despite decades of study, the underlying relationship between EEG and fMRI is far from being understood [17], [29], [30], [32].”
A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data (2011, positive): “PLS was first applied to multimodal fusion by (Martinez-Montes et al., 2004), where the multi-way PLS (N-PLS) was proposed to find correlations between fMRI time courses (dependent variables) and the spectral components of the EEG data (independent variables) from a single subject, as seen in Fig.”
Effective connectivity: Influence, causality and biophysical modeling (2011, neutral): “058 (Martínez-Montes et al., 2004) the temporal scale of interesting phenomena (fluctuations of the EEG spectrum) is such that one may convolve the EEG signal and do away with the observation equation! This is exactly the opposite of the deconvolution approach mentioned above.”
Tensor Decompositions and Applications (2009, methodology): “Examples include signal processing [62, 196, 47, 43, 43, 68, 80, 173, 60, 61], numerical linear algebra [87, 63, 64, 132, 244, 133, 149], computer vision [229, 230, 231, 190, 236, 237, 193, 102, 235], numerical analysis [22, 108, 23, 89, 114, 88, 115], data mining [190, 4, 157, 211, 5, 209, 210, 44,”
Spontaneous fluctuations in posterior alpha-band EEG activity reflect variability in excitability of human visual areas. (2008, neutral): “…et al. 2005; Damoiseaux et al. 2006) and covary with spontaneous changes in EEG a-band activity (e.g., Goldman et al. 2002; Moosmann et al. 2003; Martinez-Montes et al. 2004), although at a slower rate (fluctuation of 0.1 Hz) than in the present study, due to the temporal resolution of fMRI.”
Unsupervised Multiway Data Analysis: A Literature Survey (2009, methodology): “In addition to these studies on EEG/ERP analysis, multiway methods have also been used in the analysis of fMRI data [68] and in extracting the connection between EEG and fMRI [69].”
Trial-by-trial coupling between EEG and BOLD identifies networks related to alpha and theta EEG power increases during working memory maintenance (2009, methodology): “Interestingly, Ishii et al. (1999), who applied a beamformer approach on MEG data, and Miwakeichi et al. (2004) and Martinez-Montes et al. (2004), who applied distributed source models on EEG data, located frontal theta activity in a region in the medial prefrontal cortex that overlaps considerably…”
BOLD correlates of EEG alpha phase-locking and the fMRI default mode network (2009, background): “…fMRI BOLD signals with EEG spectral power but have not taken into account the temporal relations of the signals recorded at different scalp sites (Feige et al., 2005; Goldman et al., 2002; Goncalves et al., 2006; Laufs et al., 2003a,b; Martinez-Montes et al., 2004; Miwakeichi et al., 2004).”
Estimating brain functional connectivity with sparse multivariate autoregression (2005, result): “Previous studies (Martinez-Montes et al. 2004; Valdes-Sosa 2004) have shown that first order MAR models fit fMRI data well (as indicated by the model selection criteria such as GCV, AIC or BIC).”