EEG and MEG data analysis in SPM8.

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography.

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone

A Functional MRI and Magneto/Electro Source Imaging Procedure for Cognitive and Pre-surgical Evaluation

AbstractAnalysis of normal/pathological brain activity using neuroimaging methods is necessary to avoid operation risks, and the outcome serves as prior information for surgical neuronavigation. We present an fMRI/MEG/EEG-based methodology for tasks demanding mainly sensorimotor and visual/cognitive responses. This consists of carefully selected/designed stimulation paradigms and statistical parametric mapping methods that demonstrate the practicability of these techniques for clinical applications. The results replicate known findings in the brain-imaging field, with the improvement that our analyses are restricted to grey matter tissue. The latter enhance computations, which is advantageous for the massive data analyses that are typical of clinical and radiological functional brain “checkup” services

Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilize a recently introduced method for characterizing the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterize phase relationships within a Kuramoto model of oscillators in its sub-critical, critical, and super-critical synchronization states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state toward a more random state

The Magnetic Acoustic Change Complex and Mismatch Field:A Comparison of Neurophysiological Measures of Auditory Discrimination

The Acoustic Change Complex (ACC), a P1-N1-P2-like event-related response to changes in a continuous sound, has been suggested as a reliable, objective, and efficient test of auditory discrimination. We used magnetoencephalography to compare the magnetic ACC (mACC) to the more widely used mismatch field (MMF). Brain responses of 14 adults were recorded during mACC and MMF paradigms involving the same pitch and vowel changes in a synthetic vowel sound. Analyses of peak amplitudes revealed a significant interaction between stimulus and paradigm: for the MMF, the response was greater for vowel changes than for pitch changes, whereas, for the mACC, the pattern was reversed. A similar interaction was observed for the signal to noise ratio and single-trial analysis of individual participants’ responses showed that the MMF to Pitch changes was elicited less consistently than the other three responses. Results support the view that the ACC/mACC is a robust and efficient measure of simple auditory discrimination, particularly when researchers or clinicians are interested in the responses of individual listeners. However, the differential sensitivity of the two paradigms to the same acoustic changes indicates that the mACC and MMF are indices of different aspects of auditory processing and should, therefore, be seen as complementary rather than competing neurophysiological measures

Magnetoencephalographic Correlates of Perceptual State During Auditory Bistability

Bistability occurs when two alternative percepts can be derived from the same physical stimulus. To identify the neural correlates of specifc subjective experiences we used a bistable auditory stimulus and determined whether the two perceptual states could be distinguished electrophysiologically. Fourteen participants underwent magnetoencephalography while reporting their perceptual experience while listening to a continuous bistable stream of auditory tones. Participants reported bistability with a similar overall proportion of the two alternative percepts (52% vs 48%). At the individual level, sensor space electrophysiological discrimination between the percepts was possible in 9/14 participants with canonical variate analysis (CVA) or linear support vector machine (SVM) analysis over space and time dimensions. Classifcation was possible in 14/14 subjects with non-linear SVM. Similar efects were noted in an unconstrained source space CVA analysis (classifying 10/14 participants), linear SVM (classifying 9/14 subjects) and non-linear SVM (classifying 13/14 participants). Source space analysis restricted to a priori ROIs showed discrimination was possible in the right and left auditory cortex with each classifcation approach but in the right intraparietal sulcus this was only apparent with non-linear SVM and only in a minority of particpants. Magnetoencephalography can be used to objectively classify auditory experiences from individual subjects

Setting Up the Speech Production Network: How Oscillations Contribute to Lateralized Information Routing

Speech production involves widely distributed brain regions. This MEG study focuses on the spectro-temporal dynamics that contribute to the setup of this network. In 21 participants performing a cue-target reading paradigm, we analyzed local oscillations during preparation for overt and covert reading in the time-frequency domain and localized sources using beamforming. Network dynamics were studied by comparing different dynamic causal models of beta phase coupling in and between hemispheres. While a broadband low frequency effect was found for any task preparation in bilateral prefrontal cortices, preparation for overt speech production was specifically associated with left-lateralized alpha and beta suppression in temporal cortices and beta suppression in motor-related brain regions. Beta phase coupling in the entire speech production network was modulated by anticipation of overt reading. We propose that the processes underlying the setup of the speech production network connect relevant brain regions by means of beta synchronization and prepare the network for left-lateralized information routing by suppression of inhibitory alpha and beta oscillations

Analytical methods and experimental approaches for electrophysiological studies of brain oscillations

Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems