Concurrent EEG/fMRI analysis by multiway Partial Least Squares

Martı́nez-Montes, Eduardo; Valdés‐Sosa, Pedro A.; Miwakeichi, Fumikazu; Goldman, Robin I.; Cohen, Mark S.

doi:10.1016/j.neuroimage.2004.03.038

Cited by 280 publications

(207 citation statements)

References 33 publications

Supporting

Mentioning

201

Contrasting

Order By: Relevance

“…Except for a close spatial relationship between P2-and P3-related regions in the left supramarginal gyrus (mm distance X 0-3, y, 9; and z, 8), there was no considerable overlap among the AM activations. There was also no direct match between any of the AMand target͞response-related local maxima.…”

Section: Resultsmentioning

confidence: 86%

“…There are basically three approaches to multimodal integration: (i) through fusion, usually referring to the use of a common forward or generative model that can explain both the electroencephalogram (EEG) and fMRI data (8,9); (ii) through constraints, where spatial information from the fMRI is used for a (spatiotemporal) source reconstruction of the EEG (10-12); and (iii) through prediction, where the fMRI signal is modeled as some measure of the EEG convolved with a hemodynamic response function, a principle used in our study.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

Eichele

Specht

Moosmann

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

279

250

View full text Add to dashboard Cite

The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (oddball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.multimodal imaging ͉ P3 pattern learning ͉ target detection F unctional MRI (fMRI) of the blood oxygenation leveldependent (BOLD) response (BOLD-fMRI) measures local changes in brain hemodynamics associated with a cognitive process noninvasively with a high spatial resolution. However, an unsolved issue in fMRI research is the insufficient temporal resolution of the BOLD response. In contrast to the spatial resolution of BOLDfMRI, event-related potentials (ERP) access the current induced by synaptic activity instantaneously, with an effective temporal resolution on the order of tens to hundreds of milliseconds in case of long-latency cortical responses. However, the location of underlying generators cannot be inferred with certainty. In combination, these two complementary noninvasive methods would allow for joint high-resolution spatial and temporal mapping of the mental process under investigation and add to a more complete understanding of the neural correlates of perception and cognition (1-3). In humans, this integrated spatial and temporal precision could so far be obtained only in direct intracranial recordings, usually performed in patients receiving brain surgery for treatment of epilepsy (4-7).There are basically three approaches to multimodal integration: (i) through fusion, usually referring to the use of a common forward or generative model that can explain both the electroencephalogram (EEG) and fMRI data (8, 9); (ii) through constraints, where spatial information from the fMRI is used for a (spatiotemporal) source reconstruction of the EEG (10-12); and (iii) through prediction, where the fMRI signal is modeled as some measure of the EEG convolved with a hemodynamic response function, a principle used in our study.Invasive r...

show abstract

Section: Resultsmentioning

confidence: 86%

mentioning

confidence: 99%

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

Eichele

Specht

Moosmann

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

279

250

View full text Add to dashboard Cite

show abstract

“…Whether these fluctuations are of neuronal origin or reflect changes in local physiology remains unclear. The most direct evidence of an association between some of these fluctuations and neuronal activity comes from the observed correlation between the BOLD signal and cortical electrical activity in studies employing simultaneous fMRI and electroencephalograms (19,24) and the observation of change in these networks resulting from neurological disease (22). However, other studies suggest that these fluctuations in the BOLD signal primarily reflect changes in underlying brain physiology independent of neuronal function (4)(5)(6).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Third, whereas the trilinear decomposition gives interpretable discrimination across the spatial modes, the estimates across the frequency domain appear less informative. To better understand the temporal dynamics of these effects, the combination of fMRI with other imaging techniques such as electroencephalograms (19,24) shows great promise. Finally, note that bootstrapping methods like those used here can underestimate variances.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Consistent resting-state networks across healthy subjects

Damoiseaux¹,

Rombouts

Barkhof

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

3,970

288

3,487

View full text Add to dashboard Cite

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the socalled default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.functional connectivity ͉ functional MRI ͉ resting fluctuations T ypical functional MRI (fMRI) research focuses on the change in blood oxygenation level-dependent (BOLD) signal caused by the neural response to an externally controlled stimulus͞task. The fMRI signal during ''on'' periods is contrasted with recordings during a baseline or control condition, resulting in the relative signal change because of the specific process being studied. Recently, increased attention has been directed at investigating the features of the baseline state of the brain. Of particular interest are low-frequency fluctuations (Ϸ0.01-0.1 Hz) observed in the BOLD signal, which have been found to display spatial structure comparable to task-related activation (1-3). There is an ongoing discussion as to whether these fluctuations in the BOLD signal predominantly reflect changes of the underlying brain physiology independent of neuronal function (4-6), or instead reflect the neuronal baseline activity of the brain when goal-directed neuronal action and external input are absent (7,8). The view that coherencies in resting fluctuations represent functional resting-state networks linked to underlying neuronal modulations is consistent with the appearance of these coherencies within cortical gray matter areas of known functional relevance. For example, one of th...

show abstract

“…Several studies attest that fMRI signals correlate particularly well oscillating EEG activities in specific frequency domains (Kilner et al, 2005;Laufs et al, 2003;Martinez-Montes et al, 2004;Riera et al, 2010). In such cases, the hemodynamic activity depends linearly, not on the current intensities themselves, but rather on the power of the currents in these specific frequencies.…”

Section: Physiological Modelmentioning

confidence: 99%

EEG-fMRI Fusion: Adaptations of the Kalman Filter for Solving a High-Dimensional Spatio-Temporal Inverse Problem

Deneux¹

2011

Adaptive Filtering

View full text Add to dashboard Cite

Concurrent EEG/fMRI analysis by multiway Partial Least Squares

Cited by 280 publications

References 33 publications

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

Consistent resting-state networks across healthy subjects

EEG-fMRI Fusion: Adaptations of the Kalman Filter for Solving a High-Dimensional Spatio-Temporal Inverse Problem

Contact Info

Product

Resources

About