Localization of individual area neuronal activity

A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.     

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.     

Identifying true cortical interactions in MEG using the nulling beamformer

Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recording data. This is because most interaction measures are not robust to the cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. In this article, we describe a modified beamforming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to indicate the performance of our nulling beamforming method.     

Validation of SOBI components from high-density EEG

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies; (3) improve the signal-to-noise ratio (SNR) of somatosensory-evoked potentials (SEPs); and (4) reduce the level of subjectivity involved in the source localization process.     

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.     

The use of stationarity and nonstationarity in the detection and analysis of neural oscillations

Using available signal (i.e., spectral and time-frequency) analysis methods, it can be difficult to detect neural oscillations because of their continuously changing properties (i.e., nonstationarities) and the noise in which they are embedded. Here, we introduce fractally scaled envelope modulation (FSEM) estimation which is sensitive specifically to the changing properties of oscillatory activity. FSEM utilizes the fractal characteristic of wavelet transforms to produce a compact, two-dimensional representation of time series data where signal components at each frequency are made directly comparable according to the spectral distribution of their envelope modulations. This allows the straightforward identification of neural oscillations and other signal components with an envelope structure different from noise. For stable oscillations, we demonstrate how partition-referenced spectral estimation (PRSE) removes the noise slope from spectral estimates, yielding a level estimate where only peaks signifying the presence of oscillatory activity remain. The functionality of these methods is demonstrated with simulations and by analyzing MEG data from human auditory brain areas. FSEM uncovered oscillations in the 9- to 12-Hz and 15- to 18-Hz ranges whereas traditional spectral estimates were able to detect oscillations only in the former range. FSEM further showed that the oscillations exhibited envelope modulations spanning 3-7 s. Thus, FSEM effectively reveals oscillations undetectable with spectral estimates and allows the use of EEG and MEG for studying cognitive processes when the common approach of stimulus time-locked averaging of the measured signal is unfeasible.     

Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study

It is crucial to understand what brain signals can be decoded from single trials with different recording techniques for the development of Brain-Machine Interfaces. A specific challenge for non-invasive recording methods are activations confined to small spatial areas on the cortex such as the finger representation of one hand. Here we study the information content of single trial brain activity in non-invasive MEG and EEG recordings elicited by finger movements of one hand. We investigate the feasibility of decoding which of four fingers of one hand performed a slight button press. With MEG we demonstrate reliable discrimination of single button presses performed with the thumb, the index, the middle or the little finger (average over all subjects and fingers 57%, best subject 70%, empirical guessing level: 25.1%). EEG decoding performance was less robust (average over all subjects and fingers 43%, best subject 54%, empirical guessing level 25.1%). Spatiotemporal patterns of amplitude variations in the time series provided best information for discriminating finger movements. Non-phase-locked changes of mu and beta oscillations were less predictive. Movement related high gamma oscillations were observed in average induced oscillation amplitudes in the MEG but did not provide sufficient information about the finger's identity in single trials. Importantly, pre-movement neuronal activity provided information about the preparation of the movement of a specific finger. Our study demonstrates the potential of non-invasive MEG to provide informative features for individual finger control in a Brain-Machine Interface neuroprosthesis.     

Localization of abnormal discharges causing insular epilepsy by magnetoencephalography

The insula, one of the five cerebral lobes of the brain, is located deep within the brain and lies mainly beneath the temporal lobe. Insular epilepsy can be easily confused and misdiagnosed as temporal lobe epilepsy (TLE) because of the similar clinical symptoms and scalp electroencephalography (EEG) findings due to the insula location and neuronal connections with the temporal lobe. Magnetoencephalography (MEG) has higher sensitivity and spatial resolution than scalp EEG, and thus can often identify epileptic discharges not revealed by scalp EEG. Simultaneous scalp EEG and MEG were performed to detect and localize epileptic discharges in two patients known to have insular epilepsy associated with cavernous angioma in the insula. Epileptic discharges were detected as abnormal spikes in the EEG and MEG findings. In Patient 1, the sources of all MEG spikes detected simultaneously by EEG and MEG (E/M-spikes) were localized in the anterior temporal lobe, similar to TLE. In contrast, the sources of all MEG spikes detected only by MEG (M-spikes) were adjacent to the insular lesion. In Patient 2, the sources of all MEG spikes detected simultaneously by EEG and MEG (E/M-spikes) were localized in the anterior temporal lobe. These findings indicate that MEG allows us to detect insular activity that is undetectable by scalp EEG. In conclusion, simultaneous EEG and MEG are helpful for detecting spikes and obtaining additional information about the epileptic origin and propagation in patients with insular epilepsy.     

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.     

A multitrial analysis for revealing significant corticocortical networks in magnetoencephalography and electroencephalography

We present an MEG/EEG framework to reveal statistically significant brain areas engaged in the same cognitive process across trials without resort to averaging procedures. The variability of neuronal responses is assumed to take place only in the reconstructed time series of cortical sources and not in their positions. This hypothesis allows the use of the surrogate data method to detect recurrently active brain areas across trials adjusted with any cortically constrained focal MEEG inverse solution. Results obtained from synthetic data show that considering several trials enhances the accuracy of the source localisation. We apply this approach on MEG data recorded during a simple visual stimulation. The considered stimulus is frequency tagged in order to reveal the neural network correlated to its perception using phase synchronisation analysis. The results show consistent patterns of distributed synchronous networks centred on occipital areas.     

Cortical source estimates of gamma band amplitude and phase are different in schizophrenia

Reductions in gamma band phase synchrony and evoked power have been reported in schizophrenic subjects in response to auditory stimuli. These results have been observed in the EEG at one or two electrode sites. We wished to extend these results using magnetic field data to estimate the responses at the neural generators themselves in each hemisphere. Whole head magnetoencephalographic (MEG) recordings were used to estimate the phase and amplitude behavior of sources in primary auditory cortex in both hemispheres of schizophrenic and comparison subjects. Both ipsi- and contralateral cases were evaluated using a driving (40 Hz modulated 1 kHz carrier) and a non-driving (1 kHz tone) stimulus. We used source space projection (SSP) to collapse the magnetic field data into estimates of the time course of source strengths in individual trials. Complex wavelet based time–frequency decomposition was used to compute inter-trial phase locking factor (PLF), and mean evoked and induced amplitude for each cortical generator. Schizophrenic subjects showed reduced SSP PLF and evoked source strength for contralateral generators responding to the driving stimulus in both hemispheres. For the pure tone stimulus, only the left hemisphere PLF's in the transient window were reduced. In contrast, subjects with schizophrenia exhibited higher induced 40 Hz power to both stimulus types, consistent with the reduced PLF findings. The method of SSP combined with wavelet based complex demodulation produces a significant improvement in signal-to-noise ratio, and directly estimates the activity of the cortical generators responsible for gamma band auditory MEG evoked fields. Schizophrenic subjects exhibit significant impairment of generation and phase locking of this activity in auditory cortex, suggesting an impairment of GABA-ergic inhibitory interneuronal modulation of pyramidal cell activity.     

Source cancellation profiles of electroencephalography and magnetoencephalography

Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~ 300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.     

Robust source analysis of oscillatory motor cortex activity with inherently variable phase delay

This study evaluated quantitatively the synchronization between the magnetoencephalography (MEG) and electromyography (EMG) signals and developed a novel method for the determination of the synchronization in order to increase the reliability of the source analysis of the oscillatory motor cortex activity. The new method is based on our observation that there are large variances in the time lag due to relatively low muscle-cortex synchronization which reduces the signal-to-noise ratio of the MEG signal when averaged in direct synchrony with the rectified EMG peaks. To improve the localization of the motor cortex activity, time-frequency analysis was performed for each epoch coinciding with an EMG peak to reject the weak oscillatory activity and artifacts. In addition, the MEG signals were shifted to maximize the coherence between MEG and rectified EMG by determining for each accepted epoch the time lag resulting in a maximum cross-correlation. An experiment was carried out using 30 subjects in order to determine the applicability of this method to a real situation. The synchronization and the results of the corresponding source analysis based on the novel method were compared with the data obtained using the non-phase-shift method and Hilbert approach detecting EMG phase. The results showed that the synchronization was significantly enhanced and the signal-to-noise ratio of the MEG signals improved, and that the localized dipoles of all subjects were well clustered at the motor cortex. This method, based on shifting the MEG epochs according to the simultaneously measured time lag, considerably improves the performance of the averaging and localization of the rhythmic activity of the motor cortex.     

Decomposing EEG data into space-time-frequency components using Parallel Factor Analysis

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings--in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.     

The saccadic spike artifact in MEG

Electro- and magnetoencephalography (EEG/MEG) are the means to investigate the dynamics of neuronal activity non-invasively in the human brain. However, both EEG and MEG are also sensitive to non-neural sources, which can severely complicate the interpretation. The saccadic spike potential (SP) at saccade onset has been identified as a particularly problematic artifact in EEG because it closely resembles synchronous neuronal gamma band activity. While the SP and its confounding effects on EEG have been thoroughly characterized, the corresponding artifact in MEG, the saccadic spike field (SF), has not been investigated. Here we provide a detailed characterization of the SF. We simultaneously recorded MEG, EEG, gaze position and electrooculogram (EOG). We compared the SF in MEG for different saccade sizes and directions and contrasted it with the well-known SP in EEG. Our results reveal a saccade amplitude and direction dependent, lateralized saccadic spike artifact, which was most prominent in the gamma frequency range. The SF was strongest at frontal and temporal sensors but unlike the SP in EEG did not contaminate parietal sensors. Furthermore, we observed that the source configurations of the SF were comparable for regular and miniature saccades. Using distributed source analysis we identified the sources of the SF in the extraocular muscles. In summary, our results show that the SF in MEG closely resembles neuronal activity in frontal and temporal sensors. Our detailed characterization of the SF constitutes a solid basis for assessing possible saccadic spike related contamination in MEG experiments.     

Five-dimensional neuroimaging: localization of the time-frequency dynamics of cortical activity

The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time-frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12-30 Hz) and high gamma band (65-90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30-300 Hz) in the cerebellum, although with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high-performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition.     

Altered generation of spontaneous oscillations in Alzheimer's disease

Slowing of spontaneous alpha oscillations and an anterior shift of a source of alpha activity (8-13 Hz) have been consistently reported in the EEG studies of Alzheimer's disease (AD). It is unknown whether these changes are associated with a gradual shift in location and frequency of existing sources or rather with the involvement of a new set of oscillators. We addressed this question by applying source modeling (minimum current estimates, MCE) to spontaneous alpha activity recorded with a 306-channel MEG system from eleven non-medicated AD patients with mild to moderate cognitive impairment and twelve age-matched controls during the eyes-closed session. AD patients had predominant lower alpha band sources in the temporal regions, whereas in the controls, robust alpha sources were found near the parieto-occipital sulcus. Activation within the parieto-occipital region was significantly weaker, and activation in the right temporal area was significantly enhanced in the AD patients. These results suggest an increased temporal-lobe contribution coinciding with parieto-occipital deficits. We propose that MCE, which provides simultaneous mapping of several oscillatory sources, might be useful for detecting neurophysiological abnormalities associated with AD in combination with other neuropsychological and neurological measures.     

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.     

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.