A new method to identify multiple sources of oscillatory activity from magnetoencephalographic data

Identifying the sources of oscillatory activity in the human brain is a challenging problem in current magnetoencephalography (MEG) and electroencephalography (EEG) research. The fluctuations in phase and amplitude of cortical oscillations preclude signal averaging over successive sections of the data without a priori assumptions. In addition, several sources at different locations often produce oscillatory activity at similar frequencies. For example, spontaneous oscillatory activity in the 8- to 13-Hz band is produced simultaneously at least in the posterior parts of the brain and bilaterally in the sensorimotor cortices. The previous approaches of identifying sources of oscillatory activity by dipole modeling of bandpass filtered data are quite laborious and require that multiple criteria are defined by an experienced user. In this work we introduce a convenient method for source localization using minimum current estimates in the frequency domain. Individual current estimates are calculated for the Fourier transforms of successive sections of continuous data. These current estimates are then averaged. The algorithm was tested on simulated and measured MEG data and compared with conventional dipole modeling. The main advantage of the proposed method is that it provides an efficient approach for simultaneous estimation of multiple sources of oscillatory activity in the same frequency band.     

A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition

Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. Here we present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic "peaky" spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Our simulations demonstrate that the method allows extraction of oscillatory signals even with a signal-to-noise ratio as low as 1:10. The SSD also outperformed conventional approaches based on independent component analysis. Using real EEG data we also show that SSD allows extraction of neuronal oscillations (e.g., in alpha frequency range) with high signal-to-noise ratio and with the spatial patterns corresponding to central and occipito-parietal sources. Importantly, running time for SSD is only a few milliseconds, which clearly distinguishes it from other extraction techniques usually requiring minutes or even hours of computational time. Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.     

Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain

This paper presents a computationally efficient source estimation algorithm that localizes cortical oscillations and their phase relationships. The proposed method employs wavelet-transformed magnetoencephalography (MEG) data and uses anatomical MRI to constrain the current locations to the cortical mantle. In addition, the locations of the sources can be further confined with the help of functional MRI (fMRI) data. As a result, we obtain spatiotemporal maps of spectral power and phase relationships. As an example, we show how the phase locking value (PLV), that is, the trial-by-trial phase relationship between the stimulus and response, can be imaged on the cortex. We apply the method to spontaneous, evoked, and driven cortical oscillations measured with MEG. We test the method of combining MEG, structural MRI, and fMRI using simulated cortical oscillations along Heschl's gyrus (HG). We also analyze sustained auditory gamma-band neuromagnetic fields from MEG and fMRI measurements. Our results show that combining the MEG recording with fMRI improves source localization for the non-noise-normalized wavelet power. In contrast, noise-normalized spectral power or PLV localization may not benefit from the fMRI constraint. We show that if the thresholds are not properly chosen, noise-normalized spectral power or PLV estimates may contain false (phantom) sources, independent of the inclusion of the fMRI prior information. The proposed algorithm can be used for evoked MEG/EEG and block-designed or event-related fMRI paradigms, or for spontaneous MEG data sets. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain can provide further understanding of large-scale neural activity and communication between different brain regions.     

Paired pulse depression in the somatosensory cortex: associations between MEG and BOLD fMRI

Interpretation of the blood oxygen level dependent (BOLD) response measured using functional magnetic resonance imaging (fMRI) requires an understanding of the underlying neuronal activity. Here we report on a study using both magnetoencephalography (MEG) and BOLD fMRI, to measure the brain's functional response to electrical stimulation of the median nerve in a paired pulse paradigm. Interstimulus Intervals (ISIs) of 0.25, 0.5, 0.75, 1.0, 1.5 and 2.0 s are used to investigate how the MEG detected neural response to a second pulse is affected by that from a preceding pulse and if these MEG modulations are reflected in the BOLD response. We focus on neural oscillatory activity in the β-band (13-30 Hz) and the P35m component of the signal averaged evoked response in the sensorimotor cortex. A spatial separation of β ERD and ERS following each pulse is demonstrated suggesting that these two effects arise from separate neural generators, with ERS exhibiting a closer spatial relationship with the BOLD response. The spatial distribution and extent of BOLD activity were unaffected by ISI, but modulations in peak amplitude and latency were observed. Non-linearities in both induced oscillatory activity ERS and in the signal averaged evoked response are found for ISIs of up to 2 s when the signal averaged evoked response has returned to baseline, with the P35m component displaying paired pulse depression effects. The β-band ERS magnitude was modulated by ISI, however the ERD magnitude was not. These results support the assumption that BOLD non-linearity arises not only from a non-linear vascular response to neural activity but also a non-linear neural response to the stimulus with ISI up to 2 s.     

Task-dependent modulations of cortical oscillatory activity in human subjects during a bimanual precision grip task

Oscillations are a widespread feature of normal brain activity and have been reported at a variety of different frequencies in different neuronal systems. The demonstration that oscillatory activity is present in motor command signals has prompted renewed interest in the possible functions of synchronous oscillatory activity within the primate sensorimotor system. In the current study, we investigated task-dependent modulations in coupling between sensorimotor cortical oscillators during a bimanual precision grip task. The task required a hold-ramp-hold pattern of grip force to be exerted on a compliant object with the dominant right hand, while maintaining a steady grip with the nondominant hand. We found significant task-related modulation of 15- to 30-Hz coherence between magnetoencephalographic (MEG) activity recorded from the left sensorimotor cortex and electromyographic (EMG) activity in hand muscles on the right side. This coherence was maximal during steady hold, but disappeared during the ramp movements. Interestingly coherence between the right sensorimotor MEG and left-hand EMG showed a similar, although less deeply modulated, task-related pattern, even though this hand was maintaining a simple steady grip. No significant ipsilateral MEG-EMG coherence was observed in the 15- to 30-Hz passband for either hand. These results suggest that the cortical oscillators in the two sensorimotor cortices are independent to some degree but that they may share a common mechanism that attenuates the cortical power in both hemispheres in the 15- to 30-Hz range during movements of one hand. The results are consistent with the hypothesis that oscillatory activity in the motor system is important in resetting the descending motor commands needed for changes in motor state, such as those that occur in the transition from movement to steady grip.     

The perception of pain in others suppresses somatosensory oscillations: a magnetoencephalography study

Accumulating evidence demonstrates that similar neural circuits are activated during the first-hand experience of pain and the observation of pain in others. However, most functional MRI studies did not detect signal change in the primary somatosensory cortex during pain empathy. To test if the perception of pain in others involves the primary somatosensory cortex, neuromagnetic oscillatory activity was recorded from the primary somatosensory cortex in 16 participants while they observed static pictures depicting body parts in painful and non-painful situations. The left median nerve was stimulated at the wrist, and the poststimulus rebounds of the approximately 10-Hz somatosensory cortical oscillations were quantified. Compared to the baseline condition, the level of the approximately 10-Hz oscillations was suppressed during both of the observational situations, indicating the activation of the primary somatosensory cortex. Importantly, watching painful compared to non-painful situations suppressed somatosensory oscillations to a significant stronger degree. In addition, the suppression caused by perceiving others in the painful relative to the non-painful situations correlated with the perspective taking subscale of the interpersonal reaction index. These results, consistent with the mirror-neuron system, demonstrate that the perception of pain in others modulates neural activity in primary somatosensory cortex and supports the idea that the perception of pain in others elicits subtle somatosensory activity that may be difficult to detect by fMRI techniques.     

Spectral signal space projection algorithm for frequency domain MEG and EEG denoising, whitening, and source imaging

MEG and EEG data contain additive correlated noise generated by environmental and physiological sources. To suppress this type of spatially coloured noise, source estimation is often performed with spatial whitening based on a measured or estimated noise covariance matrix. However, artifacts that span relatively small noise subspaces, such as cardiac, ocular, and muscle artifacts, are often explicitly removed by a variety of denoising methods (e.g., signal space projection) before source imaging. Here, we introduce a new approach, the spectral signal space projection (S(3)P) algorithm, in which time-frequency (TF)-specific spatial projectors are designed and applied to the noisy TF-transformed data, and whitened source estimation is performed in the TF domain. The approach can be used to derive spectral variants of all linear time domain whitened source estimation algorithms. The denoised sensor and source time series are obtained by the corresponding inverse TF-transform. The method is evaluated and compared with existing subspace projection and signal separation techniques using experimental data. Altogether, S(3)P provides an expanded framework for MEG/EEG data denoising and whitened source imaging in both the time and frequency/scale domains.     

Changes in brain network activity during working memory tasks: a magnetoencephalography study

In this study, we elucidate the changes in neural oscillatory processes that are induced by simple working memory tasks. A group of eight subjects took part in modified versions of the N-back and Sternberg working memory paradigms. Magnetoencephalography (MEG) data were recorded, and subsequently processed using beamformer based source imaging methodology. Our study shows statistically significant increases in θ oscillations during both N-back and Sternberg tasks. These oscillations were shown to originate in the medial frontal cortex, and further to scale with memory load. We have also shown that increases in θ oscillations are accompanied by decreases in β and γ band oscillations at the same spatial coordinate. These decreases were most prominent in the 20-40 Hz frequency range, although spectral analysis showed that γ band power decrease extends up to at least 80 Hz. β/γ Power decrease also scales with memory load. Whilst θ increases were predominately observed in the medial frontal cortex, β/γ decreases were associated with other brain areas, including nodes of the default mode network (for the N-back task) and areas associated with language processing (for the Sternberg task). These observations are in agreement with intracranial EEG and fMRI studies. Finally, we have shown an intimate relationship between changes in β/γ band oscillatory power at spatially separate network nodes, implying that activity in these nodes is not reflective of uni-modal task driven changes in spatially separate brain regions, but rather represents correlated network activity. The utility of MEG as a non-invasive means to measure neural oscillatory modulation has been demonstrated and future studies employing this technology have the potential to gain a better understanding of neural oscillatory processes, their relationship to functional and effective connectivity, and their correspondence to BOLD fMRI.     

A method for spatiotemporal mapping of event-related modulation of cortical rhythmic activity

Cortical rhythmic activity can be systematically modulated by stimuli or tasks and may thus provide relevant information about brain function. Meaningful use of those phenomena requires characterization of both locations and time courses of event-related suppressions and increases of oscillatory activity. However, localization of the neural sources of cortical rhythms during intervals of very low levels of activity, and within short time intervals, is not a trivial matter. Hence, event-related modulation of rhythmic activity has typically been described at the level of magnetoencephalography (MEG) sensors or electroencephalography (EEG) electrodes, without reaching into the brain. Here, we introduce erDICS, an event-related version of Dynamic Imaging of Coherent Sources that allows spatial mapping of the level of oscillatory activity in the brain as a function of time, with respect to stimulus or task timing. By utilizing a time-resolved frequency-domain beamformer, erDICS yields the spatial distribution of both power suppressions and power increases. Permutation tests further reveal areas and time windows in which the modulations of oscillatory power are statistically significant, in individual subjects. We demonstrate the usability of erDICS on simulated and real MEG data. From the erDICS maps we identify areas showing salient event-related changes of rhythmic activity, represent them with equivalent current dipoles and calculate their contribution to the measured signal. Comparison of this multidipole model with the original signal yields a quantitative measure of goodness for the identified source areas and the analysis approach in general.     

A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG

Determining the dynamics of functional connectivity is critical for understanding the brain. Recent functional magnetic resonance imaging (fMRI) studies demonstrate that measuring correlations between brain regions in resting-state activity can be used to reveal intrinsic neural networks. To study the oscillatory dynamics that underlie intrinsic functional connectivity between regions requires high temporal resolution measures of electrophysiological brain activity, such as magnetoencephalography (MEG). However, there is a lack of consensus as to the best method for examining connectivity in resting-state MEG data. Here we adapted a wavelet-based method for measuring phase-locking with respect to the frequency of neural oscillations. This method employs anatomical MRI information combined with MEG data using the minimum norm estimate inverse solution to produce functional connectivity maps from a "seed" region to all other locations on the cortical surface at any and all frequencies of interest. We test this method by simulating phase-locked oscillations at various points on the cortical surface, which illustrates a substantial artifact that results from imperfections in the inverse solution. We demonstrate that normalizing resting-state MEG data using phase-locking values computed on empty room data reduces much of the effects of this artifact. We then use this method with eight subjects to reveal intrinsic interhemispheric connectivity in the auditory network in the alpha frequency band in a silent environment. This spectral resting-state functional connectivity imaging method may allow us to better understand the oscillatory dynamics underlying intrinsic functional connectivity in the human brain.     

Reduced phase locking to slow amplitude modulation in adults with dyslexia: an MEG study

Perception of speech at multiple temporal scales is important for the efficient extraction of meaningful phonological elements. Individuals with developmental dyslexia have difficulty in the accurate neural representation of phonological aspects of speech, across languages. Recently, it was proposed that these difficulties might arise in part because of impaired phase locking to the slower modulations in the speech signal (< 10 Hz), which would affect syllabic parsing and segmentation of the speech stream (the “temporal sampling” hypothesis, Goswami, 2011). Here we measured MEG responses to different rates of amplitude modulated white noise in adults with and without dyslexia. In line with the temporal sampling hypothesis, different patterns of phase locking to amplitude modulation at the delta rate of 2 Hz were found when comparing participants with dyslexia to typically-reading participants. Typical readers exhibited better phase locking to slow modulations in right auditory cortex, whereas adults with dyslexia showed more bilateral phase locking. The results suggest that oscillatory phase locking mechanisms for slower temporal modulations are atypical in developmental dyslexia.     

A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.     

Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation

Source current estimation from electromagnetic (MEG and EEG) signals is an ill-posed problem that often produces blurry or inaccurately positioned estimates. The two modalities have distinct factors limiting the resolution, e.g., MEG cannot detect radially oriented sources, while EEG is sensitive to accuracy of the head model. This makes combined EEG + MEG estimation techniques desirable, but different acquisition noise statistics, complexity of the head models, and lack of pertinent metrics all complicate the assessment of the resulting improvements. We investigated analytically the effect of including EEG recordings in MEG studies versus the addition of new MEG channels when computing noise-normalized minimum ℓ₂-norm estimates. Three-compartment boundary-element forward models were constructed using structural MRI scans for four subjects. Singular value analysis of the resulting forward models predicted better performance of the EEG + MEG case in the form of higher matrix rank. MNE inverse operators for EEG, MEG and EEG + MEG were constructed using the sensor noise covariance estimated from data. Metrics derived from the resolution matrices predicted higher spatial resolution in EEG + MEG as compared to MEG due to decreased spread (lower spatial dispersion, higher resolution index) with no reduction in dipole localization error. The effect was apparent in all source locations, with increased magnitude for deep areas such as the cingulate cortex. We were also able to corroborate the results for the somatosensory cortex using median nerve responses.     

Modulation of motor-cortex oscillatory activity by painful Adelta- and C-fiber stimuli

Spontaneous approximately 20-Hz oscillations, arising predominantly from the primary motor cortex (MI), are readily observed by magnetoencephalography (MEG). Prior studies have indicated that the level of the approximately 20-Hz rhythm reflects the functional state of the MI cortex: increased 20-Hz level is associated with increased inhibition and suppression of the rhythm with excitation of MI. Close interaction is suggested between pain and the motor system by the association of chronic pain with motor dysfunction and by the alleviation of pain by motor-cortex stimulation. We therefore explored the effect of noxious input on motor-cortex functions by recording MEG signals from nine healthy subjects during selective laser stimulation of Adelta- and C-fibers of the hand. The approximately 20-Hz level was suppressed in the contralateral MI cortex in all nine subjects after painful Adelta- and C-fiber stimuli (P < 0.001). The suppression started 180 +/- 10 ms (mean +/- SEM) after Adelta-fiber stimuli and 820 +/- 30 ms after C-fiber stimuli, and peaked 160-170 ms later. Similar, but about 50% weaker, suppression of the approximately 20-Hz oscillations occurred in seven out of nine subjects in the ipsilateral MI. These results suggest automatic, lateralized, excitation of the MI cortex by noxious Adelta- and C-fiber input.     

Gamma band pitch responses in human auditory cortex measured with magnetoencephalography

We have previously used direct electrode recordings in two human subjects to identify neural correlates of the perception of pitch (Griffiths, Kumar, Sedley et al., Direct recordings of pitch responses from human auditory cortex, Curr. Biol. 22 (2010), pp. 1128-1132). The present study was carried out to assess virtual-electrode measures of pitch perception based on non-invasive magnetoencephalography (MEG). We recorded pitch responses in 13 healthy volunteers using a passive listening paradigm and the same pitch-evoking stimuli (regular interval noise; RIN) as in the previous study. Source activity was reconstructed using a beamformer approach, which was used to place virtual electrodes in auditory cortex. Time-frequency decomposition of these data revealed oscillatory responses to pitch in the gamma frequency band to occur, in Heschl's gyrus, from 60 Hz upwards. Direct comparison of these pitch responses to the previous depth electrode recordings shows a striking congruence in terms of spectrotemporal profile and anatomical distribution. These findings provide further support that auditory high gamma oscillations occur in association with RIN pitch stimuli, and validate the use of MEG to assess neural correlates of normal and abnormal pitch perception.     

Application of artificial neural network to fMRI regression analysis

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.     

A neural mass model for MEG/EEG: coupling and neuronal dynamics

Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequency-specific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their functional significance remains a matter of debate. Some of the mechanisms that generate these signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. The kinetics of the ensuing population dynamics determine the frequency of oscillations. In this work we extended the classical nonlinear lumped-parameter model of alpha rhythms, initially developed by Lopes da Silva and colleagues [Kybernetik 15 (1974) 27], to generate more complex dynamics. We show that the whole spectrum of MEG/EEG signals can be reproduced within the oscillatory regime of this model by simply changing the population kinetics. We used the model to examine the influence of coupling strength and propagation delay on the rhythms generated by coupled cortical areas. The main findings were that (1) coupling induces phase-locked activity, with a phase shift of 0 or pi when the coupling is bidirectional, and (2) both coupling and propagation delay are critical determinants of the MEG/EEG spectrum. In forthcoming articles, we will use this model to (1) estimate how neuronal interactions are expressed in MEG/EEG oscillations and establish the construct validity of various indices of nonlinear coupling, and (2) generate event-related transients to derive physiologically informed basis functions for statistical modelling of average evoked responses.     

Inferring task-related networks using independent component analysis in magnetoencephalography

A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data.