Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

Martino, Federico De; Valente, Giancarlo; Borst, Aline W. de; Esposito, Fabrizio; Roebroeck, Alard; Goebel, Rainer; Formisano, Elia

doi:10.1016/j.mri.2009.12.026

Cited by 31 publications

(29 citation statements)

References 40 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9C, right) compared with measurements performed in a non-MR environment (e.g., Veniero et al 2009). However, note that the raw signal in the interval before the TMS pulse was highly similar to restingstate data acquired in previous simultaneous EEG-fMRI experiments (e.g., De Martino et al 2010) both in the time domain (Fig. 9C, top left) as well as in the frequency domain (Fig.…”

Section: Human Datasupporting

confidence: 76%

“…Moreover, the signal before the TMS pulse is similar to other concurrent EEG-fMRI studies ( Fig. 9; e.g., De Martino et al 2010) and could be used, for example, to measure state dependency of the effects elicited by TMS (Romei et al 2007). …”

Section: Feasibility Of Concurrent Tms-eeg-fmri Measurementsmentioning

confidence: 95%

“…The combined EEG-fMRI setup described above had already been established at our institute (see, e.g., De Martino et al 2010;Debener et al 2007;Strobel et al 2008). Similarly, the concurrent use of TMS and fMRI had been extensively tested beforehand in close collaboration with Magstim related to preparations for dedicated experiments.…”

Section: Performed Measurementsmentioning

confidence: 99%

See 2 more Smart Citations

On the feasibility of concurrent human TMS-EEG-fMRI measurements

Peters

Reithler

Schuhmann

et al. 2013

Journal of Neurophysiology

Self Cite

View full text Add to dashboard Cite

multaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEGfMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEGfMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEGfMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMSfMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware. TMS; EEG; fMRI; multimodal neuroimaging THE EMPIRICAL STUDY of the human brain has seen an unparalleled advance in recent decades. Using brain imaging methods such as functional magnetic resonance (MR) imaging (fMRI) and EEG, we can now noninvasively investigate the working human brain in a wide variety of experimental conditions. Because of their different advantages, drawbacks, and applications, EEG and fMRI are in many aspects complementary in the study of human neurocognition. fMRI measures vascular responses, most often using the blood oxygenation level-dependent (BOLD) signal, which has a relatively low temporal resolution in the order of seconds. Conversely, fMRI has a spatial resolution in the order of cubic millimeters and offers whole-brain coverage. In contrast, due to volume conductance and the inverse problem, EEG has low spatial resolution (in the order of centimeters) and spatial uncertainties. EEG mainly reflects activity on the surface of the brain and is not sensitive to some aspects of neuronal activity and certain populations. For example, some interneuron types (e.g., stellate cells) and many subcortical structures have a closed field structure in which...

show abstract

Section: Human Datasupporting

confidence: 76%

Section: Feasibility Of Concurrent Tms-eeg-fmri Measurementsmentioning

confidence: 95%

Section: Performed Measurementsmentioning

confidence: 99%

See 1 more Smart Citation

On the feasibility of concurrent human TMS-EEG-fMRI measurements

Peters

Reithler

Schuhmann

et al. 2013

Journal of Neurophysiology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nonetheless, these methods have proven to be of high utility by providing unbiased and multivariate analysis schemes for simultaneous EEG-fMRI. In addition, many of these approaches are, or could be, easily generalized to frameworks that include other modalities, such as diffusion tensor imaging or genetic data (for review, see De Martino et al, 2010;Sui et al, 2012). On the other hand, as there is already a variety of different mathematical approaches for multimodal data fusion, the selection of a fusion model tailored for a specific research question might sometimes be difficult to accomplish.…”

Section: Multimodal Data Fusionmentioning

confidence: 99%

Methods for Simultaneous EEG-fMRI: An Introductory Review

Huster

Debener²,

Eichele³

et al. 2012

J. Neurosci.

271

182

View full text Add to dashboard Cite

The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of human brain function. Due to recent developments in technical and algorithmic instrumentation, the rate-limiting step in multimodal studies has shifted from data acquisition to analytic aspects. Here, we introduce and compare different methods for data integration and identify the benefits that come with each approach, guiding the reader toward an understanding and informed selection of the integration approach most suitable for addressing a particular research question.

show abstract

“…The alpha state map used for the analysis was defined as the EEG independent component best representing the occipital alpha rhythm in terms of topographical distribution and its time course (De Martino et al, 2010). For the EEG analysis through TESS we added 9 independent components to the state design matrix to capture the eye blinking artifacts and physiological activity of the brain at rest, as it has been proposed for fMRI analysis in Chaudhary et al (2012).The power of time course of the same EEG alpha state map was used as a regressor for the BOLD data analysis, after convolving it with the HRF and down sampling it to the fMRI sampling rate (De Martino et al, 2010). Motion-related regressors were also added to the BOLD design matrix.…”

mentioning

confidence: 99%

EEG source imaging of brain states using spatiotemporal regression

Custo¹,

Vulliémoz²,

Grouiller

et al. 2014

NeuroImage

View full text Add to dashboard Cite

Relating measures of electroencephalography (EEG) back to the underlying sources is a long-standing inverse problem. Here we propose a new method to estimate the EEG sources of identified electrophysiological states that represent spontaneous activity, or are evoked by a stimulus, or caused by disease or disorder. Our method has the unique advantage of seamlessly integrating a statistical significance of the source estimate while efficiently eliminating artifacts (e.g., due to eye blinks, eye movements, bad electrodes). After determining the electrophysiological states in terms of stable topographies using established methods (e.g.: ICA, PCA, k-means, epoch average), we propose to estimate these states' time courses through spatial regression of a General Linear Model (GLM). These time courses are then used to find EEG sources that have a similar time-course (using temporal regression of a second GLM). We validate our method using both simulated and experimental data. Simulated data allows us to assess the difference between source maps obtained by the proposed method and those obtained by applying conventional source imaging of the state topographies. Moreover, we use data from 7 epileptic patients (9 distinct epileptic foci localized by intracranial EEG) and 2 healthy subjects performing an eyes-open/eyes-closed task to elicit activity in the alpha frequency range. Our results indicate that the proposed EEG source imaging method accurately localizes the sources for each of the electrical brain states. Furthermore, our method is particularly suited for estimating the sources of EEG resting states or otherwise weak spontaneous activity states, a problem not adequately solved before.© 2014 Elsevier Inc. All rights reserved. IntroductionThe study of brain function has benefited enormously from modern neuroimaging techniques to reveal localization and dynamics of neuronal activity during evoked and spontaneous states. One of the most widely used methodologies to analyze data from functional magnetic resonance imaging (fMRI), is the General Linear Model (GLM) where pre-defined hemodynamic responses are used in a linear regression model and contrasts of interest are evaluated by statistical hypothesis testing (Bandettini et al., 1992;Friston et al., 1995;Kwong et al., 1992;Ogawa et al., 1992). As the fMRI signal is related to neuronal activity via neurovascular coupling, it only provides a (slow) proxy for neuronal activity. Electroencephalography (EEG), on the other hand, directly records the fast changes of current potential related to neuronal activity. Recent advances in high-density recording and 3D source analysis have increased EEG accuracy as a brain imaging method with the inherent advantage of high temporal resolution (Michel and Murray, 2012) It is fairly natural to conceive the application of conventional GLM analysis as used for fMRI to the Electrical Source Images of the EEG (or ESI: with this general term we indicate any method mapping scalp measurements into source space), as some previous papers (Bro...

show abstract

Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

Cited by 31 publications

References 40 publications

On the feasibility of concurrent human TMS-EEG-fMRI measurements

On the feasibility of concurrent human TMS-EEG-fMRI measurements

Methods for Simultaneous EEG-fMRI: An Introductory Review

EEG source imaging of brain states using spatiotemporal regression

Contact Info

Product

Resources

About