Volume conduction effects in EEG and MEG

Differential Phonological and Semantic Modulation of Neurophysiological Responses to Visual Word Recognition

BACKGROUND

Reading words for meaning relies on orthographic, phonological and semantic processing. The triangle model implicates a direct orthography-to-semantics pathway and a phonologically mediated orthography-to-semantics pathway, which interact with each other. The temporal evolution of processing in these routes is not well understood, although theoretical evidence predicts early phonological processing followed by interactive phonological and semantic processing.

METHOD

This study used electroencephalography-event-related potential (ERP) analysis and magnetoencephalography (MEG) source localisation to identify temporal markers and the corresponding neural generators of these processes in early (∼200 ms) and late (∼400 ms) neurophysiological responses to visual words, pseudowords and consonant strings.

RESULTS

ERP showed an effect of phonology but not semantics in both time windows, although at ∼400 ms there was an effect of stimulus familiarity. Phonological processing at ~200 ms was localised to the left occipitotemporal cortex and the inferior frontal gyrus. At 400 ms, there was continued phonological processing in the inferior frontal gyrus and additional semantic processing in the anterior temporal cortex. There was also an area in the left temporoparietal junction which was implicated in both phonological and semantic processing. In ERP, the semantic response at ∼400 ms appeared to be masked by concurrent processes relating to familiarity, while MEG successfully differentiated these processes.

DISCUSSION

The results support the prediction of early phonological processing followed by an interaction of phonological and semantic processing during word recognition. Neuroanatomical loci of these processes are consistent with previous neuropsychological and functional magnetic resonance imaging studies. The results also have implications for the classical interpretation of N400-like responses as markers for semantic processing.

Skull Defects in Finite Element Head Models for Source Reconstruction from Magnetoencephalography Signals

Magnetoencephalography (MEG) signals are influenced by skull defects. However, there is a lack of evidence of this influence during source reconstruction. Our objectives are to characterize errors in source reconstruction from MEG signals due to ignoring skull defects and to assess the ability of an exact finite element head model to eliminate such errors. A detailed finite element model of the head of a rabbit used in a physical experiment was constructed from magnetic resonance and co-registered computer tomography imaging that differentiated nine tissue types. Sources of the MEG measurements above intact skull and above skull defects respectively were reconstructed using a finite element model with the intact skull and one incorporating the skull defects. The forward simulation of the MEG signals reproduced the experimentally observed characteristic magnitude and topography changes due to skull defects. Sources reconstructed from measured MEG signals above intact skull matched the known physical locations and orientations. Ignoring skull defects in the head model during reconstruction displaced sources under a skull defect away from that defect. Sources next to a defect were reoriented. When skull defects, with their physical conductivity, were incorporated in the head model, the location and orientation errors were mostly eliminated. The conductivity of the skull defect material non-uniformly modulated the influence on MEG signals. We propose concrete guidelines for taking into account conducting skull defects during MEG coil placement and modeling. Exact finite element head models can improve localization of brain function, specifically after surgery.

Investigation of the electrophysiological correlates of negative BOLD response during intermittent photic stimulation: An EEG-fMRI study

Although the occurrence of concomitant positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to visual stimuli is increasingly investigated in neuroscience, it still lacks a definite explanation. Multimodal imaging represents a powerful tool to study the determinants of negative BOLD responses: the integration of functional Magnetic Resonance Imaging (fMRI) and electroencephalographic (EEG) recordings is especially useful, since it can give information on the neurovascular coupling underlying this complex phenomenon. In the present study, the brain response to intermittent photic stimulation (IPS) was investigated in a group of healthy subjects using simultaneous EEG-fMRI, with the main objective to study the electrophysiological mechanisms associated with the intense NBRs elicited by IPS in extra-striate visual cortex. The EEG analysis showed that IPS induced a desynchronization of the basal rhythm, followed by the instauration of a novel rhythm driven by the visual stimulation. The most interesting results emerged from the EEG-informed fMRI analysis, which suggested a relationship between the neuronal rhythms at 10 and 12 Hz and the BOLD dynamics in extra-striate visual cortex. These findings support the hypothesis that NBRs to visual stimuli may be neuronal in origin rather than reflecting pure vascular phenomena. Hum Brain Mapp 37:2247-2262, 2016. © 2016 Wiley Periodicals, Inc.

The role of blood vessels in high-resolution volume conductor head modeling of EEG

Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.

Optogenetic Mapping of Functional Connectivity in Freely Moving Mice via Insertable Wrapping Electrode Array Beneath the Skull

Spatiotemporal mapping of neural interactions through electrocorticography (ECoG) is the key to understanding brain functions and disorders. For the entire brain cortical areas, this approach has been challenging, especially in freely moving states, owing to the need for extensive craniotomy. Here, we introduce a flexible microelectrode array system, termed iWEBS, which can be inserted through a small cranial slit and stably wrap onto the curved cortical surface. Using iWEBS, we measured dynamic changes of signals across major cortical domains, namely, somatosensory, motor, visual and retrosplenial areas, in freely moving mice. iWEBS robustly displayed somatosensory evoked potentials (SEPs) in corresponding cortical areas to specific somatosensory stimuli. We also used iWEBS for mapping functional interactions between cortical areas in the propagation of spike-and-wave discharges (SWDs), the neurological marker of absence seizures, triggered by optogenetic inhibition of a specific thalamic nucleus. This demonstrates that iWEBS represents a significant improvement over conventional ECoG recording methodologies and, therefore, is a competitive recording system for mapping wide-range brain connectivity under various behavioral conditions.

Facilitative effect of repetitive presentation of one stimulus on cortical responses to other stimuli in macaque monkeys--a possible neural mechanism for mismatch negativity

The event-related potential 'mismatch negativity' (MMN) is an indicator of a perceiver's ability to detect deviations in sensory signal streams. MMN and its homologue in animals, mismatch activity (MMA), are differential neural responses to a repeatedly presented stimulus and a subsequent deviant stimulus (oddball). Because neural mechanisms underlying MMN and MMA remain unclear, there is a controversy as to whether MMN and MMA arise solely from stimulus-specific adaptation (SSA), in which the response to a stimulus cumulatively attenuates with its repetitive presentation. To address this issue, we used electrocorticography and the auditory roving-oddball paradigm in two awake macaque monkeys. We examined the effect of stimulus repetition number on MMA and on responses to repeated stimuli and oddballs across the cerebral cortex in the time-frequency domain. As the repetition number increased, MMA spread across the temporal, frontal and parietal cortices, and each electrode yielded a larger MMA. Surprisingly, this increment in MMA largely depended on response augmentation to the oddball rather than on SSA to the repeated stimulus. Following sufficient repetition, the oddball evoked a spectral power increment in some electrodes on the frontal cortex that had shown no power increase to the stimuli with less or no preceding repetition. We thereby revealed that repetitive presentation of one stimulus not only leads to SSA but also facilitates the cortical response to oddballs involving a wide range of cortical regions. This facilitative effect might underlie the generation of MMN-like scalp potentials in macaques that potentially shares similar neural mechanisms with MMN in humans.

Non-invasive brain mapping in epilepsy: Applications from magnetoencephalography

BACKGROUND

Non-invasive in vivo neurophysiological recordings with EEG/MEG are key to the diagnosis, classification, and further understanding of epilepsy. Historically the emphasis of these recordings has been the localisation of the putative sources of epileptic discharges. More recent developments see new techniques studying oscillatory dynamics, connectivity and network properties.

NEW METHOD

New analysis strategies for whole head MEG include the development of spatial filters or beamformers for source localisation, time-frequency analysis for cortical dynamics and graph theory applications for connectivity.

RESULTS

The idea of epilepsy as a network disorder is not new, and new applications of structural and functional brain imaging show differences in cortical and subcortical networks in patients with epilepsy compared to controls. Concepts of 'focal' and 'generalised' are challenged by evidence of focal onsets in generalised epileptic discharges, and widespread network changes in focal epilepsy. Spectral analyses can show differences in induced cortical response profiles, particularly in photosensitive epilepsy.

COMPARISON WITH EXISTING METHOD

This review focuses on the application of MEG in the study of epilepsy, starting with a brief historical perspective, followed by novel applications of source localisation, time-frequency and connectivity analyses.

CONCLUSION

Novel MEG analyses approaches show altered cortical dynamics and widespread network alterations in focal and generalised epilepsies, and identification of regional network abnormalities may have a role in epilepsy surgery evaluation.

Boosting specificity of MEG artifact removal by weighted support vector machine

An automatic artifact removal method of magnetoencephalogram (MEG) was presented in this paper. The method proposed is based on independent components analysis (ICA) and support vector machine (SVM). However, different from the previous studies, in this paper we consider two factors which would influence the performance. First, the imbalance factor of independent components (ICs) of MEG is handled by weighted SVM. Second, instead of simply setting a fixed weight to each class, a re-weighting scheme is used for the preservation of useful MEG ICs. Experimental results on manually marked MEG dataset showed that the method proposed could correctly distinguish the artifacts from the MEG ICs. Meanwhile, 99.72% ± 0.67 of MEG ICs were preserved. The classification accuracy was 97.91% ± 1.39. In addition, it was found that this method was not sensitive to individual differences. The cross validation (leave-one-subject-out) results showed an averaged accuracy of 97.41% ± 2.14.

Incorporating priors for EEG source imaging and connectivity analysis

Electroencephalography source imaging (ESI) is a useful technique to localize the generators from a given scalp electric measurement and to investigate the temporal dynamics of the large-scale neural circuits. By introducing reasonable priors from other modalities, ESI reveals the most probable sources and communication structures at every moment in time. Here, we review the available priors from such techniques as magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET). The modality's specific contribution is analyzed from the perspective of source reconstruction. For spatial priors, EEG-correlated fMRI, temporally coherent networks (TCNs) and resting-state fMRI are systematically introduced in the ESI. Moreover, the fiber tracking (diffusion tensor imaging, DTI) and neuro-stimulation techniques (transcranial magnetic stimulation, TMS) are also introduced as the potential priors, which can help to draw inferences about the neuroelectric connectivity in the source space. We conclude that combining EEG source imaging with other complementary modalities is a promising approach toward the study of brain networks in cognitive and clinical neurosciences.

The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults

In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood‐oxygenation level‐dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI. The resting‐state fluctuation amplitude (RSFA) in the fMRI signal (rsfMRI) has been proposed as an index of vascular reactivity. The RSFA compares favourably with other techniques such as breath‐hold and hypercapnia, but the latter are more difficult to perform in some populations, such as older adults. The RSFA is therefore a candidate for use in adjusting for age‐related changes in vascular reactivity in fMRI studies. The use of RSFA is predicated on its sensitivity to vascular rather than neural factors; however, the extent to which each of these factors contributes to RSFA remains to be characterized. The present work addressed these issues by comparing RSFA (i.e., rsfMRI variability) to proxy measures of (i) cardiovascular function in terms of heart rate (HR) and heart rate variability (HRV) and (ii) neural activity in terms of resting state magnetoencephalography (rsMEG). We derived summary scores of RSFA, a sensorimotor task BOLD activation, cardiovascular function and rsMEG variability for 335 healthy older adults in the population‐based Cambridge Centre for Ageing and Neuroscience cohort (Cam‐CAN; http://www.cam-can.com). Mediation analysis revealed that the effects of ageing on RSFA were significantly mediated by vascular factors, but importantly not by the variability in neuronal activity. Furthermore, the converse effects of ageing on the rsMEG variability were not mediated by vascular factors. We then examined the effect of RSFA scaling of task‐based BOLD in the sensorimotor task. The scaling analysis revealed that much of the effects of age on task‐based activation studies with fMRI do not survive correction for changes in vascular reactivity, and are likely to have been overestimated in previous fMRI studies of ageing. The results from the mediation analysis demonstrate that RSFA is modulated by measures of vascular function and is not driven solely by changes in the variance of neural activity. Based on these findings we propose that the RSFA scaling method is articularly useful in large scale and longitudinal neuroimaging studies of ageing, or with frail participants, where alternative measures of vascular reactivity are impractical. Hum Brain Mapp 36:2248–2269, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Influence of the head model on EEG and MEG source connectivity analyses

The results of brain connectivity analysis using reconstructed source time courses derived from EEG and MEG data depend on a number of algorithmic choices. While previous studies have investigated the influence of the choice of source estimation method or connectivity measure, the effects of the head modeling errors or simplifications have not been studied sufficiently. In the present simulation study, we investigated the influence of particular properties of the head model on the reconstructed source time courses as well as on source connectivity analysis in EEG and MEG. Therefore, we constructed a realistic head model and applied the finite element method to solve the EEG and MEG forward problems. We considered the distinction between white and gray matter, the distinction between compact and spongy bone, the inclusion of a cerebrospinal fluid (CSF) compartment, and the reduction to a simple 3-layer model comprising only the skin, skull, and brain. Source time courses were reconstructed using a beamforming approach and the source connectivity was estimated by the imaginary coherence (ICoh) and the generalized partial directed coherence (GPDC). Our results show that in both EEG and MEG, neglecting the white and gray matter distinction or the CSF causes considerable errors in reconstructed source time courses and connectivity analysis, while the distinction between spongy and compact bone is just of minor relevance, provided that an adequate skull conductivity value is used. Large inverse and connectivity errors are found in the same regions that show large topography errors in the forward solution. Moreover, we demonstrate that the very conservative ICoh is relatively safe from the crosstalk effects caused by imperfect head models, as opposed to the GPDC.

Visualizing simulated electrical fields from electroencephalography and transcranial electric brain stimulation: a comparative evaluation

Electrical activity of neuronal populations is a crucial aspect of brain activity. This activity is not measured directly but recorded as electrical potential changes using head surface electrodes (electroencephalogram - EEG). Head surface electrodes can also be deployed to inject electrical currents in order to modulate brain activity (transcranial electric stimulation techniques) for therapeutic and neuroscientific purposes. In electroencephalography and noninvasive electric brain stimulation, electrical fields mediate between electrical signal sources and regions of interest (ROI). These fields can be very complicated in structure, and are influenced in a complex way by the conductivity profile of the human head. Visualization techniques play a central role to grasp the nature of those fields because such techniques allow for an effective conveyance of complex data and enable quick qualitative and quantitative assessments. The examination of volume conduction effects of particular head model parameterizations (e.g., skull thickness and layering), of brain anomalies (e.g., holes in the skull, tumors), location and extent of active brain areas (e.g., high concentrations of current densities) and around current injecting electrodes can be investigated using visualization. Here, we evaluate a number of widely used visualization techniques, based on either the potential distribution or on the current-flow. In particular, we focus on the extractability of quantitative and qualitative information from the obtained images, their effective integration of anatomical context information, and their interaction. We present illustrative examples from clinically and neuroscientifically relevant cases and discuss the pros and cons of the various visualization techniques.

The neurophysiological bases of EEG and EEG measurement: a review for the rest of us

A thorough understanding of the EEG signal and its measurement is necessary to produce high quality data and to draw accurate conclusions from those data. However, publications that discuss relevant topics are written for divergent audiences with specific levels of expertise: explanations are either at an abstract level that leaves readers with a fuzzy understanding of the electrophysiology involved, or are at a technical level that requires mastery of the relevant physics to understand. A clear, comprehensive review of the origin and measurement of EEG that bridges these high and low levels of explanation fills a critical gap in the literature and is necessary for promoting better research practices and peer review. The present paper addresses the neurophysiological source of EEG, propagation of the EEG signal, technical aspects of EEG measurement, and implications for interpretation of EEG data.

A guideline for head volume conductor modeling in EEG and MEG

For accurate EEG/MEG source analysis it is necessary to model the head volume conductor as realistic as possible. This includes the distinction of the different conductive compartments in the human head. In this study, we investigated the influence of modeling/not modeling the conductive compartments skull spongiosa, skull compacta, cerebrospinal fluid (CSF), gray matter, and white matter and of the inclusion of white matter anisotropy on the EEG/MEG forward solution. Therefore, we created a highly realistic 6-compartment head model with white matter anisotropy and used a state-of-the-art finite element approach. Starting from a 3-compartment scenario (skin, skull, and brain), we subsequently refined our head model by distinguishing one further of the above-mentioned compartments. For each of the generated five head models, we measured the effect on the signal topography and signal magnitude both in relation to a highly resolved reference model and to the model generated in the previous refinement step. We evaluated the results of these simulations using a variety of visualization methods, allowing us to gain a general overview of effect strength, of the most important source parameters triggering these effects, and of the most affected brain regions. Thereby, starting from the 3-compartment approach, we identified the most important additional refinement steps in head volume conductor modeling. We were able to show that the inclusion of the highly conductive CSF compartment, whose conductivity value is well known, has the strongest influence on both signal topography and magnitude in both modalities. We found the effect of gray/white matter distinction to be nearly as big as that of the CSF inclusion, and for both of these steps we identified a clear pattern in the spatial distribution of effects. In comparison to these two steps, the introduction of white matter anisotropy led to a clearly weaker, but still strong, effect. Finally, the distinction between skull spongiosa and compacta caused the weakest effects in both modalities when using an optimized conductivity value for the homogenized compartment. We conclude that it is highly recommendable to include the CSF and distinguish between gray and white matter in head volume conductor modeling. Especially for the MEG, the modeling of skull spongiosa and compacta might be neglected due to the weak effects; the simplification of not modeling white matter anisotropy is admissible considering the complexity and current limitations of the underlying modeling approach.

Classification of four-class motor imagery employing single-channel electroencephalography

With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.

Lasting modulation effects of rTMS on neural activity and connectivity as revealed by resting-state EEG

The long-lasting neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) are of great interest for therapeutic applications in various neurological and psychiatric disorders, due to which functional connectivity among brain regions is profoundly disturbed. Classic TMS studies selectively alter neural activity in specific brain regions and observe neural activity changes on nonperturbed areas to infer underlying connectivity and its changes. Less has been indicated in direct measures of functional connectivity and/or neural network and on how connectivity/network alterations occur. Here, we developed a novel analysis framework to directly investigate both neural activity and connectivity changes induced by rTMS from resting-state EEG (rsEEG) acquired in a group of subjects with a chronic disorder of imbalance, known as the mal de debarquement syndrome (MdDS). Resting-state activity in multiple functional brain areas was identified through a data-driven blind source separation analysis on rsEEG data, and the connectivity among them was characterized using a phase synchronization measure. Our study revealed that there were significant long-lasting changes in resting-state neural activity, in theta, low alpha, and high alpha bands and neural networks in theta, low alpha, high alpha and beta bands, over broad cortical areas 4 to 5 h after the last application of rTMS in a consecutive five-day protocol. Our results of rsEEG connectivity further indicated that the changes, mainly in the alpha band, over the parietal and occipital cortices from pre- to post-TMS sessions were significantly correlated, in both magnitude and direction, to symptom changes in this group of subjects with MdDS. This connectivity measure not only suggested that rTMS can generate positive treatment effects in MdDS patients, but also revealed new potential targets for future therapeutic trials to improve treatment effects. It is promising that the new connectivity measure from rsEEG can be used to understand the variability in treatment response to rTMS in brain disorders with impaired functional connectivity and, eventually, to determine individually tailored stimulation parameters and treatment procedures in rTMS.

Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

Neuromagnetic coherence of epileptic activity: an MEG study

PURPOSE

This study was undertaken to test the hypothesis that patients with epilepsy have abnormal imaginary coherence compared with control subjects.

METHODS

Thirty patients with seizures underwent magnetoencephalography (MEG) recording using a whole cortex MEG system. Conventional equivalent current dipoles (ECDs) and synthetic aperture magnetometry (SAM) were used to analyze MEG data. Neural synchronization was studied using imaginary coherence to analyze resting-state MEG data. The ECDs, SAM, and MEG results were then compared with intra/extra-operative EEG.

RESULTS

Abnormal imaginary coherence was identified in all patients (30/30, 100%). The locations of abnormal imaginary coherence were in agreement with the ECDs locations of spikes in 23 patients (23/30, 76.7%). The ECD locations in 5 patients were scattered or located bilaterally. The locations of abnormal imaginary coherence were in agreement with SAM locations in 26 patients (26/30, 86.7%). One case of imaginary coherence was located in two lobes. The ECDs fit locations were in agreement with SAM locations in 21 patients (21/30, 70.0%). The locations of abnormal imaginary coherence, ECDs, and SAM were in agreement with intra/extra-operative EEG in 23 patients (23/30, 76.7%), 17 patients (17/30, 56.7%), and 20 patients (20/30, 66.7%), respectively. The results of ECDs location, SAM location, imaginary coherence, and intracranial EEG (iEEG) were consistent in 15 patients (15/30, 50%).

CONCLUSIONS

The results show that patients with epilepsy have abnormal imaginary coherence, and suggest that the location and coherence of epileptic activity could be quantitatively identified and analyzed using neuromagnetic signals.

Decoding individual finger movements from one hand using human EEG signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.

Reconstructing coherent networks from electroencephalography and magnetoencephalography with reduced contamination from volume conduction or magnetic field spread

Volume conduction (VC) and magnetic field spread (MFS) induce spurious correlations between EEG/MEG sensors, such that the estimation of functional networks from scalp recordings is inaccurate. Imaginary coherency [1] reduces VC/MFS artefacts between sensors by assuming that instantaneous interactions are caused predominantly by VC/MFS and do not contribute to the imaginary part of the cross-spectral densities (CSDs). We propose an adaptation of the dynamic imaging of coherent sources (DICS) [2] - a method for reconstructing the CSDs between sources, and subsequently inferring functional connectivity based on coherences between those sources. Firstly, we reformulate the principle of imaginary coherency by performing an eigenvector decomposition of the imaginary part of the CSD to estimate the power that only contributes to the non-zero phase-lagged (NZPL) interactions. Secondly, we construct an NZPL-optimised spatial filter with two a priori assumptions: (1) that only NZPL interactions exist at the source level and (2) the NZPL CSD at the sensor level is a good approximation of the projected source NZPL CSDs. We compare the performance of the NZPL method to the standard method by reconstructing a coherent network from simulated EEG/MEG recordings. We demonstrate that, as long as there are phase differences between the sources, the NZPL method reliably detects the underlying networks from EEG and MEG. We show that the method is also robust to very small phase lags, noise from phase jitter, and is less sensitive to regularisation parameters. The method is applied to a human dataset to infer parts of a coherent network underpinning face recognition.

Effects of sutures and fontanels on MEG and EEG source analysis in a realistic infant head model

In infants, the fontanels and sutures as well as conductivity of the skull influence the volume currents accompanying primary currents generated by active neurons and thus the associated electroencephalography (EEG) and magnetoencephalography (MEG) signals. We used a finite element method (FEM) to construct a realistic model of the head of an infant based on MRI images. Using this model, we investigated the effects of the fontanels, sutures and skull conductivity on forward and inverse EEG and MEG source analysis. Simulation results show that MEG is better suited than EEG to study early brain development because it is much less sensitive than EEG to distortions of the volume current caused by the fontanels and sutures and to inaccurate estimates of skull conductivity. Best results will be achieved when MEG and EEG are used in combination.

Computational representation of a realistic head and brain volume conductor model: electroencephalography simulation and visualization study

Computational head and brain volume conductor modeling is a practical and non-invasive method to investigate neuroelectrical activity in the brain. Anatomical structures included in a model affect the flow of volume currents and the resulting scalp surface potentials. The influence of different tissues within the head on scalp surface potentials was investigated by constructing five highly detailed, realistic head models from segmented and processed Visible Human Man digital images. The models were: (1) model with 20 different tissues, that is, skin, dense connective tissue (fat), aponeurosis (muscle), outer, middle and inner tables of the scalp, dura matter, arachnoid layer (including cerebrospinal fluid), pia matter, six cortical layers, eye tissue, muscle around the eye, optic nerve, temporal muscle, white matter and internal air, (2) model with three main inhomogeneities, that is, scalp, skull, brain, (3) model with homogeneous scalp and remaining inhomogeneities, (4) model with homogeneous skull and remaining inhomogeneities, and (5) model with homogeneous brain matter and remaining inhomogeneities. Scalp potentials because of three different dipolar sources in the parietal-occipital lobe were computed for all five models. Results of a forward solution revealed that tissues included in the model and the dipole source location directly affect the simulated scalp surface potentials. The major finding indicates that significant change in the scalp surface potentials is observed when the brain's distinctions are removed. The other modifications, for example, layers of the scalp and skull are important too, but they have less effect on the overall results.

EEG source imaging in epilepsy--practicalities and pitfalls

EEG source imaging (ESI) is a model-based imaging technique that integrates temporal and spatial components of EEG to identify the generating source of electrical potentials recorded on the scalp. Recent advances in computer technologies have made the analysis of ESI data less time-consuming, and have rekindled interest in this technique as a clinical diagnostic tool. On the basis of the available body of evidence, ESI seems to be a promising tool for epilepsy evaluation; however, the precise clinical value of ESI in presurgical evaluation of epilepsy and in localization of eloquent cortex remains to be investigated. In this Review, we describe two fundamental issues in ESI; namely, the forward and inverse problems, and their solutions. The clinical application of ESI in surgical planning for patients with medically refractory focal epilepsy, and its use in source reconstruction together with invasive recordings, is also discussed. As ESI can be used to map evoked responses, we discuss the clinical utility of this technique in cortical mapping-an essential process when planning resective surgery for brain regions that are in close proximity to eloquent cortex.

How the statistical validation of functional connectivity patterns can prevent erroneous definition of small-world properties of a brain connectivity network

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

Anisotropic spherical head model and its application to imaging electric activity of the brain

This study reports on a solution to Poisson's equation describing an electric potential and field generated by a dipolar current source positioned inside a set of concentric spherical shells characterized by a homogeneous anisotropic conductivity inside each shell. Formulas giving a unique continuation of potentials and fields between the shells are derived. The formulas are applied to a spherical model of the human head to show that (i) real human skulls comprising isotropic compact and cancellous bone layers can be closely approximated by a single shell with radial-tangential conductivity anisotropy ~1:2 and radial conductivity equal to 1/30 of the brain/scalp conductivity; (ii) errors due to the spherical approximation of the head shape are of the same magnitude as errors due to poorly known electrical properties of the modeled head tissues; (iii) commonly used electroencephalography (EEG) average reference contributes 15% of the signal (on the average) and, therefore, makes EEG measurements significantly nonlocal; and (iv) the surface Laplacian of EEG measurements closely approximates electric currents at the skull-scalp interface, providing a parameter-free (up to a constant factor) deblurring and dereferencing of EEG data. These results can be useful for localization of sources underlying electric activity of the brain.

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.

Magnetoencephalography adds to the surgical evaluation process

Summarizing the podium discussion at the AES 2009, strengths and limitations of magnetoencephalography (MEG) are discussed with regard to basic methodological and clinical aspects in routine screening and presurgical evaluation of patients with epilepsies. Current literature and example cases are used to illustrate MEG contribution to clinical decision making, specifically whether a patient with pharmacoresistant epilepsy can move forward to epilepsy surgery. The main conclusion is that the largest role of MEG, as presently performed in the clinical environment, is to increase the number of patients who can go on to surgery, while it should not be used to deny surgery to any patient.

Influence of skull conductivity perturbations on EEG dipole source analysis

PURPOSE

Electroencephalogram (EEG) source analysis is a noninvasive technique used in the presurgical of epilepsy. In this study, the dipole location and orientation errors due to skull conductivity perturbations were investigated in two groups of three-dimensional head models: A spherical head model and a realistic head model.

METHODS

In each group, the head model had a brain-to-skull conductivity ratio (Rsigma) within the range of 10-40. Solving the forward problem in the head model with skull conductivity perturbations along with the inverse problem in the baseline head model with Rsigma=20 permitted the derivation of the dipole estimation errors.

RESULTS

Perturbations in the skull conductivity generated dipole location and orientation errors: The larger the perturbations, the larger the errors and the error ranges. The dipole orientation error due to skull conductivity perturbations was not great (maximal mean of < 5 degrees) in this study, but the dipole location error was notable (maximal mean of > 5 mm).

CONCLUSIONS

Therefore, the influence of skull conductivity perturbations on EEG dipole source analysis cannot be neglected. This study suggests that it is necessary to measure the skull conductivity of the individual patients in order to achieve accurate EEG source analysis.

The generation of tetrahedral mesh models for neuroanatomical MRI

In this article, we describe a detailed method for automatically generating tetrahedral meshes from 3D images having multiple region labels. An adaptively sized tetrahedral mesh modeling approach is described that is capable of producing meshes conforming precisely to the voxelized regions in the image. Efficient tetrahedral mesh improvement is then performed minimizing an energy function containing three terms: a smoothing term to remove the voxelization, a fidelity term to maintain continuity with the image data, and a novel elasticity term to prevent the tetrahedra from becoming flattened or inverted as the mesh deforms while allowing the voxelization to be removed entirely. The meshing algorithm is applied to structural MR image data that has been automatically segmented into 56 neuroanatomical sub-divisions as well as on two other examples. The resulting tetrahedral representation has several desirable properties such as tetrahedra with dihedral angles away from 0 and 180 degrees, smoothness, and a high resolution. Tetrahedral modeling via the approach described here has applications in modeling brain structure in normal as well as diseased brain in human and non-human data and facilitates examination of 3D object deformations resulting from neurological illness (e.g. Alzheimer's disease), development, and/or aging.

Increasing the efficiency of neonatal MEG measurements by alternating auditory and tactile stimulation

OBJECTIVE

To evaluate the possible effect of intervening auditory stimulation on somatosensory evoked magnetic fields in newborns.

METHODS

We recorded auditory and tactile evoked responses with magnetoencephalography (MEG) from two groups of healthy newborns. One group (n=11) received only tactile stimuli to the index finger, the other (n=11) received alternating tactile and auditory (vowel [a:] with 300-ms duration) stimuli. The interval between subsequent tactile stimuli was always 2 s. We analyzed the equivalent current dipoles (ECDs) of the main auditory and somatosensory responses.

RESULTS

The ECDs of the tactile responses agreed with activation of the primary somatosensory cortex at ∼60 ms and the secondary somatosensory region at ∼200 ms. The source of the auditory response (∼250 ms) was clearly distinct from those to tactile stimulation and in line with auditory cortex activation. The intervening auditory stimulation did not affect the strength, latency, or location of the ECDs of the tactile responses.

CONCLUSIONS

Auditory and tactile MEG responses from newborns can be obtained in one measurement session.

SIGNIFICANCE

The alternating stimulation can be used to shorten the total measurement time and/or to improve the signal to noise ratio by collecting more data.

Modeling of the human skull in EEG source analysis

We used computer simulations to investigate finite element models of the layered structure of the human skull in EEG source analysis. Local models, where each skull location was modeled differently, and global models, where the skull was assumed to be homogeneous, were compared to a reference model, in which spongy and compact bone were explicitly accounted for. In both cases, isotropic and anisotropic conductivity assumptions were taken into account. We considered sources in the entire brain and determined errors both in the forward calculation and the reconstructed dipole position. Our results show that accounting for the local variations over the skull surface is important, whereas assuming isotropic or anisotropic skull conductivity has little influence. Moreover, we showed that, if using an isotropic and homogeneous skull model, the ratio between skin/brain and skull conductivities should be considerably lower than the commonly used 80:1. For skull modeling, we recommend (1) Local models: if compact and spongy bone can be identified with sufficient accuracy (e.g., from MRI) and their conductivities can be assumed to be known (e.g., from measurements), one should model these explicitly by assigning each voxel to one of the two conductivities, (2) Global models: if the conditions of (1) are not met, one should model the skull as either homogeneous and isotropic, but with considerably higher skull conductivity than the usual 0.0042 S/m, or as homogeneous and anisotropic, but with higher radial skull conductivity than the usual 0.0042 S/m and a considerably lower radial:tangential conductivity anisotropy than the usual 1:10.

Sensitivity of beamformer source analysis to deficiencies in forward modeling

Beamforming approaches have recently been developed for the field of electroencephalography (EEG) and magnetoencephalography (MEG) source analysis and opened up new applications within various fields of neuroscience. While the number of beamformer applications thus increases fast-paced, fundamental methodological considerations, especially the dependence of beamformer performance on leadfield accuracy, is still quite unclear. In this article, we present a systematic study on the influence of improper volume conductor modeling on the source reconstruction performance of an EEG-data based synthetic aperture magnetometry (SAM) beamforming approach. A finite element model of a human head is derived from multimodal MR images and serves as a realistic volume conductor model. By means of a theoretical analysis followed by a series of computer simulations insight is gained into beamformer performance with respect to reconstruction errors in peak location, peak amplitude, and peak width resulting from geometry and anisotropy volume conductor misspecifications, sensor noise, and insufficient sensor coverage. We conclude that depending on source position, sensor coverage, and accuracy of the volume conductor model, localization errors up to several centimeters must be expected. As we could show that the beamformer tries to find the best fitting leadfield (least squares) with respect to its scanning space, this result can be generalized to other localization methods. More specific, amplitude, and width of the beamformer peaks significantly depend on the interaction between noise and accuracy of the volume conductor model. The beamformer can strongly profit from a high signal-to-noise ratio, but this requires a sufficiently realistic volume conductor model.

Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients

Insight into how brain structures interact is critical for understanding the principles of functional brain architectures and may lead to better diagnosis and therapy for neuropsychiatric disorders. We recorded, simultaneously, magnetoencephalographic (MEG) signals and subcortical local field potentials (LFP) in a Parkinson's disease (PD) patient with bilateral deep brain stimulation (DBS) electrodes in the subthalamic nucleus (STN). These recordings offer a unique opportunity to characterize interactions between the subcortical structures and the neocortex. However, high-amplitude artefacts appeared in the MEG. These artefacts originated from the percutaneous extension wire, rather than from the actual DBS electrode and were locked to the heart beat. In this work, we show that MEG beamforming is capable of suppressing these artefacts and quantify the optimal regularization required. We demonstrate how beamforming makes it possible to localize cortical regions whose activity is coherent with the STN-LFP, extract artefact-free virtual electrode time-series from regions of interest and localize cortical areas exhibiting specific task-related power changes. This furnishes results that are consistent with previously reported results using artefact-free MEG data. Our findings demonstrate that physiologically meaningful information can be extracted from heavily contaminated MEG signals and pave the way for further analysis of combined MEG-LFP recordings in DBS patients.

EEG/MEG source imaging: methods, challenges, and open issues

We present the four key areas of research-preprocessing, the volume conductor, the forward problem, and the inverse problem-that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.

Modeling transcranial DC stimulation

A method to estimate the potential and current density distribution during transcranial DC stimulation (tDCS) is introduced. The volume conductor model consists of a realistic head model (concerning shape as well as conductivity), obtained from TI-, PD- and DT-MR images. The model includes five compartments with different conductivities. For the skull and the white matter compartments, the conductivities are anisotropic. Using this model, the potentials inside the head that are generated by tDCS electrodes positioned on the scalp were computed by using the Finite Element Method. The results show that this is a promising method for the study of the effects of tDCS.

Signal quality of simultaneously recorded invasive and non-invasive EEG

Both invasive and non-invasive electroencephalographic (EEG) recordings from the human brain have an increasingly important role in neuroscience research and are candidate modalities for medical brain-machine interfacing. It is often assumed that the major artifacts that compromise non-invasive EEG, such as caused by blinks and eye movement, are absent in invasive EEG recordings. Quantitative investigations on the signal quality of simultaneously recorded invasive and non-invasive EEG in terms of artifact contamination are, however, lacking. Here we compared blink related artifacts in non-invasive and invasive EEG, simultaneously recorded from prefrontal and motor cortical regions using an approach suitable for detection of small artifact contamination. As expected, we find blinks to cause pronounced artifacts in non-invasive EEG both above prefrontal and motor cortical regions. Unexpectedly, significant blink related artifacts were also found in the invasive recordings, in particular in the prefrontal region. Computing a ratio of artifact amplitude to the amplitude of ongoing brain activity, we find that the signal quality of invasive EEG is 20 to above 100 times better than that of simultaneously obtained non-invasive EEG. Thus, while our findings indicate that ocular artifacts do exist in invasive recordings, they also highlight the much better signal quality of invasive compared to non-invasive EEG data. Our findings suggest that blinks should be taken into account in the experimental design of ECoG studies, particularly when event related potentials in fronto-anterior brain regions are analyzed. Moreover, our results encourage the application of techniques for reducing ocular artifacts to further optimize the signal quality of invasive EEG.

Chronic subdural electrodes in the management of epilepsy

Subdural electrodes play a very important role in the evaluation of a percentage of patients being considered for epilepsy surgery. Electrical activity at very low and very high frequencies, beyond the practical range of scalp EEG, can be recorded subdurally and may contain considerable information not available non-invasively. The recording and stimulating procedures for using chronically implanted subdural electrodes to localize the epileptogenic zone and map eloquent functions of the human cortex are well established, and complication rates are low. Complications include infections, CSF leak, and focal neurologic deficits, all of which tend to be increased with a higher number of electrodes and longer duration of recordings. Careful consideration of the risks and benefits should be coupled with a firm hypothesis about the epileptogenic zone derived from the non-invasive components of the epilepsy workup to guide the decision about whether and where to implant subdural electrodes. When they are employed to answer a specific question in an individual patient, subdural electrodes can optimize the clinical outcome of a candidate for epilepsy surgery.

Modeling skull electrical properties

Accurate representations and measurements of skull electrical conductivity are essential in developing appropriate forward models for applications such as inverse EEG or Electrical Impedance Tomography of the head. Because of its layered structure, it is often assumed that skull is anisotropic, with an anisotropy ratio around 10. However, no detailed investigation of skull anisotropy has been performed. In this paper we investigate four-electrode measurements of conductivities and their relation to tissue anisotropy ratio (ratio of tangential to radial conductivity) in layered or anisotropic biological samples similar to bone. It is shown here that typical values for the thicknesses and radial conductivities of individual skull layers produce tissue with much smaller anisotropy ratios than 10. Moreover, we show that there are very significant differences between the field patterns formed in a three-layered isotropic structure plausible for bone, and those formed assuming that bone is homogeneous and anisotropic. We performed a measurement of conductivity using an electrode configuration sensitive to the distinction between three-layered and homogeneous anisotropic composition and found results consistent with the sample being three-layered. We recommend that the skull be more appropriately represented as three isotropic layers than as homogeneous and anisotropic.