Magnetoencephalography (MEG) is an exciting brain imaging technology that allows real-time tracking of neural activity, making it an invaluable tool for advancing our understanding of brain function. In this comprehensive introduction to MEG, Peter Hansen, Morten Kringelbach, and Riitta Salmelin have brought together the leading researchers to provide the basic tools for planning and executing MEG experiments, as well as analyzing and interpreting the resulting data. Chapters on the basics describe the fundamentals of MEG and its instrumentation, and provide guidelines for designing experiments and performing successful measurements. Chapters on data analysis present it in detail, from general concepts and assumptions to analysis of evoked responses and oscillatory background activity. Chapters on solutions propose potential solutions to the inverse problem using techniques such as minimum norm estimates, spatial filters and beamformers. Chapters on combinations elucidate how MEG can be used to complement other neuroimaging techniques. Chapters on applications provide practical examples of how to use MEG to study sensory processing and cognitive tasks, and how MEG can be used in a clinical setting. These chapters form a complete basic reference source for those interested in exploring or already using MEG that will hopefully inspire them to try to develop new, exciting approaches to designing and analyzing their own studies. This book will be a valuable resource for researchers from diverse fields, including neuroimaging, cognitive neuroscience, medical imaging, computer modelling, as well as for clinical practitioners.

/pdf/meg-an-introduction-to-methods-3qkc4l9b7t.pdf

MEG: An introduction to methods.

The auditory frequency-following response (FFR) to complex periodic sounds is used to study the subcortical auditory system, and has been proposed as a biomarker for disorders that feature abnormal sound processing. Despite its value in fundamental and clinical research, the neural origins of the FFR are unclear. Using magnetoencephalography, we observe a strong, right-asymmetric contribution to the FFR from the human auditory cortex at the fundamental frequency of the stimulus, in addition to signal from cochlear nucleus, inferior colliculus and medial geniculate. This finding is highly relevant for our understanding of plasticity and pathology in the auditory system, as well as higher-level cognition such as speech and music processing. It suggests that previous interpretations of the FFR may need re-examination using methods that allow for source separation.

/pdf/cortical-contributions-to-the-auditory-frequency-following-9omo6m6m0g.pdf

Cortical contributions to the auditory frequency-following response revealed by MEG.

Patients with epilepsy often present in their electroencephalogram (EEG) short electrical potentials (spikes or spike-wave bursts) that are not accompanied by clinical manifestations but are of important diagnostic significance They result from a population of abnormally hyperactive and hypersynchronous neurons It is not easy to determine the location of the cerebral generators and the other brain regions that may be involved as a result of this abnormal activity The possibility to combine EEG recording with functional MRI (fMRI) scanning opens the opportunity to uncover the regions of the brain showing changes in the fMRI signal in response to epileptic spikes seen in the EEG These regions are presumably involved in the abnormal neuronal activity at the origin of epileptic discharges This paper reviews the methodology involved in performing such studies, particularly the challenge of recording a good quality EEG inside the MR scanner while scanning is taking place, and the methods required for the statistical analysis of the combined EEG and fMRI time series We review the results obtained in patients with different types of epileptic disorders and discuss the difficult theoretical problems raised by the interpretation of an increase (activation) and decrease (deactivation) in blood oxygen level dependent (BOLD) signal, both frequently seen in response to spikes

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.20577

Combining EEG and fMRI: A multimodal tool for epilepsy research

Evaluation of EEG localization methods using realistic simulations of interictal spikes.

We present a review of dynamic causal modeling (DCM) for magneto- and electroencephalography (M/EEG) data. DCM is based on a spatiotemporal model, where the temporal component is formulated in terms of neurobiologically plausible dynamics. Following an intuitive description of the model, we discuss six recent studies, which use DCM to analyze M/EEG and local field potentials. These studies illustrate how DCM can be used to analyze evoked responses (average response in time), induced responses (average response in time-frequency), and steady-state responses (average response in frequency). Bayesian model comparison plays a critical role in these analyses, by allowing one to compare equally plausible models in terms of their model evidence. This approach might be very useful in M/EEG research; where correlations among spatial and neuronal model parameter estimates can cause uncertainty about which model best explains the data. Bayesian model comparison resolves these uncertainties in a principled and formal way. We suggest that DCM and Bayesian model comparison provides a useful way to test hypotheses about distributed processing in the brain, using electromagnetic data.

/pdf/dynamic-causal-modeling-for-eeg-and-meg-45fa2x4xrw.pdf

Dynamic causal modeling for EEG and MEG.

Characterizing the cortical activity from electro- and magneto-encephalography (EEG/MEG) data requires solving an ill-posed inverse problem that does not admit a unique solution. As a consequence, the use of functional neuroimaging, for instance, functional Magnetic Resonance Imaging (fMRI), constitutes an appealing way of constraining the solution. However, the match between bioelectric and metabolic activities is desirable but not assured. Therefore, the introduction of spatial priors derived from other functional modalities in the EEG/MEG inverse problem should be considered with caution. In this paper, we propose a Bayesian characterization of the relevance of fMRI-derived prior information regarding the EEG/MEG data. This is done by quantifying the adequacy of this prior to the data, compared with that obtained using an noninformative prior instead. This quantitative comparison, using the so-called Bayes factor, allows us to decide whether the informative prior should (or not) be included in the inverse solution. We validate our approach using extensive simulations, where fMRI-derived priors are built as perturbed versions of the simulated EEG sources. Moreover, we show how this inference framework can be generalized to optimize the way we should incorporate the informative prior.

Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach

Characterizing the cortical activity sources of electroencephalography (EEG)/magnetoencephalography data is a critical issue since it requires solving an ill-posed inverse problem that does not admit a unique solution. Two main different and complementary source models have emerged: equivalent current dipoles (ECD) and distributed linear (DL) models. While ECD models remain highly popular since they provide an easy way to interpret the solutions, DL models (also referred to as imaging techniques) are known to be more realistic and flexible. In this paper, we show how those two representations of the brain electromagnetic activity can be cast into a common general framework yielding an optimal description and estimation of the EEG sources. From this extended source mixing model, we derive a hybrid approach whose key aspect is the separation between temporal and spatial characteristics of brain activity, which allows to dramatically reduce the number of DL model parameters. Furthermore, the spatial profile of the sources, as a temporal invariant map, is estimated using the entire time window data, allowing to significantly enhance the information available about the spatial aspect of the EEG inverse problem. A Bayesian framework is introduced to incorporate distinct temporal and spatial constraints on the solution and to estimate both parameters and hyperparameters of the model. Using simulated EEG data, the proposed inverse approach is evaluated and compared with standard distributed methods using both classical criteria and ROC curves.

Bayesian spatio-temporal approach for EEG source reconstruction: conciliating ECD and distributed models

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Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach

Bayesian spatio-temporal approach for EEG source reconstruction: conciliating ECD and distributed models