Review on solving the inverse problem in EEG source analysis

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm.

Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information

For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments.

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.

Sparse MEG source imaging for reconstructing dynamic sources of interictal spikes in partial epilepsy

PURPOSE

The present study aimed to test the feasibility of a novel neuroimaging technique, that is, variation-based sparse cortical current density (VB-SCCD) imaging algorithm, in noninvasively estimating location and extent of epileptic sources from interictal magnetoencephalography (MEG) data.

METHODS

A total of 108 interictal spikes from 3 partial epilepsy patients were selected to perform VB-SCCD source analysis. Cortical sources were identified at spike peaks, rising phases, and entire spikes, respectively, from all interictal spikes in each patient, to estimate source locations and extents, and validated using presurgical evaluation data. Other source analysis methods, that is, minimum norm estimate and sparse source imaging were also performed for comparison.

RESULTS

Cortical sources reconstructed by VB-SCCD that are consistent with clinical presurgical evaluation outcomes have detection rates of 65.8% at spike peaks, 85.1% during rising phases, and 92.6% in entire spikes. Stable spatiotemporal patterns of reconstructed cortical sources were also obtained using VB-SCCD, which provide more insights about the formation and propagation of interictal epileptic activity.

CONCLUSIONS

Our present results suggest that the VB-SCCD technique has the capability in estimating location and extent of epileptic sources of interictal spikes and is promising to become a valuable noninvasive tool in assisting presurgical planning for partial epilepsy patients.

A review of EEG and MEG for brainnetome research

The majority of brain activities are performed by functionally integrating separate regions of the brain. Therefore, the synchronous operation of the brain's multiple regions or neuronal assemblies can be represented as a network with nodes that are interconnected by links. Because of the complexity of brain interactions and their varying effects at different levels of complexity, one of the corresponding authors of this paper recently proposed the brainnetome as a new -ome to explore and integrate the brain network at different scales. Because electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive and have outstanding temporal resolution and because they are the primary clinical techniques used to capture the dynamics of neuronal connections, they lend themselves to the analysis of the neural networks comprising the brainnetome. Because of EEG/MEG's applicability to brainnetome analyses, the aim of this review is to identify the procedures that can be used to form a network using EEG/MEG data in sensor or source space and to promote EEG/MEG network analysis for either neuroscience or clinical applications. To accomplish this aim, we show the relationship of the brainnetome to brain networks at the macroscale and provide a systematic review of network construction using EEG and MEG. Some potential applications of the EEG/MEG brainnetome are to use newly developed methods to associate the properties of a brainnetome with indices of cognition or disease conditions. Associations based on EEG/MEG brainnetome analysis may improve the comprehension of the functioning of the brain in neuroscience research or the recognition of abnormal patterns in neurological disease.

Dipole source localization of mouse electroencephalogram using the Fieldtrip toolbox

The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.

Multisensory integration of dynamic emotional faces and voices: method for simultaneous EEG-fMRI measurements

Combined EEG-fMRI analysis correlates time courses from single electrodes or independent EEG components with the hemodynamic response. Implementing information from only one electrode, however, may miss relevant information from complex electrophysiological networks. Component based analysis, in turn, depends on a priori knowledge of the signal topography. Complex designs such as studies on multisensory integration of emotions investigate subtle differences in distributed networks based on only a few trials per condition. Thus, they require a sensitive and comprehensive approach which does not rely on a-priori knowledge about the underlying neural processes. In this pilot study, feasibility and sensitivity of source localization-driven analysis for EEG-fMRI was tested using a multisensory integration paradigm. Dynamic audiovisual stimuli consisting of emotional talking faces and pseudowords with emotional prosody were rated in a delayed response task. The trials comprised affectively congruent and incongruent displays. In addition to event-locked EEG and fMRI analyses, induced oscillatory EEG responses at estimated cortical sources and in specific temporo-spectral windows were correlated with the corresponding BOLD responses. EEG analysis showed high data quality with less than 10% trial rejection. In an early time window, alpha oscillations were suppressed in bilateral occipital cortices and fMRI analysis confirmed high data quality with reliable activation in auditory, visual and frontal areas to the presentation of multisensory stimuli. In line with previous studies, we obtained reliable correlation patterns for event locked occipital alpha suppression and BOLD signal time course. Our results suggest a valid methodological approach to investigate complex stimuli using the present source localization driven method for EEG-fMRI. This novel procedure may help to investigate combined EEG-fMRI data from novel complex paradigms with high spatial and temporal resolution.

Compensatory changes in cortical resource allocation in adults with hearing loss

Hearing loss has been linked to many types of cognitive decline in adults, including an association between hearing loss severity and dementia. However, it remains unclear whether cortical re-organization associated with hearing loss occurs in early stages of hearing decline and in early stages of auditory processing. In this study, we examined compensatory plasticity in adults with mild-moderate hearing loss using obligatory, passively-elicited, cortical auditory evoked potentials (CAEP). High-density EEG elicited by speech stimuli was recorded in adults with hearing loss and age-matched normal hearing controls. Latency, amplitude and source localization of the P1, N1, P2 components of the CAEP were analyzed. Adults with mild-moderate hearing loss showed increases in latency and amplitude of the P2 CAEP relative to control subjects. Current density reconstructions revealed decreased activation in temporal cortex and increased activation in frontal cortical areas for hearing-impaired listeners relative to normal hearing listeners. Participants' behavioral performance on a clinical test of speech perception in noise was significantly correlated with the increases in P2 latency. Our results indicate that changes in cortical resource allocation are apparent in early stages of adult hearing loss, and that these passively-elicited cortical changes are related to behavioral speech perception outcome.

Dipole source analysis for readiness potential and field using simultaneously measured EEG and MEG signals

Various source localization techniques have indicated the generators of each identifiable component of movement-related cortical potentials, since the discovery of the surface negative potential prior to self-paced movement by Kornhuber and Decke. Readiness potentials and fields preceding self-paced finger movements were recorded simultaneously using multichannel electroencephalography (EEG) and magnetoencephalography (MEG) from five healthy subjects. The cortical areas involved in this paradigm are the supplementary motor area (SMA) (bilateral), pre-SMA (bilateral), and contralateral motor area of the moving finger. This hypothesis is tested in this paper using the dipole source analysis independently for only EEG, only MEG, and both combined. To localize the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for spherical head models consisting of a set of connected volumes, typically representing the scalp, skull, and brain. In the source reconstruction it is to be expected that EEG predominantly localizes radially oriented sources while MEG localizes tangential sources at the desired region of the cortex. The effect of MEG on EEG is also observed when analyzing both combined data. When comparing the two head models, the spherical and the realistic head models showed similar results. The significant points for this study are comparing the source analysis between the two modalities (EEG and MEG) so as to assure that EEG is sensitive to mostly radially orientated sources while MEG is only sensitive to only tangential sources, and comparing the spherical and individual head models.

Source localization techniques for direction decoding from local field potentials

Local Field Potential (LFP) recordings are one type of intracortical recordings, (besides Single Unit Activity) that can help decode movement direction successfully. In the longterm however, using LFPs for decoding presents some major challenges like inherent instability and non-stationarity. Our approach to overcome this challenge bases around the hypothesis that each task has a signature source-location pattern. The methodology involves introduction of source localization, and tracking of sources over a period of time that enables us to decode movement direction in an eight-direction center-out-reach-task. We establish that such tracking can be used for long term decoding, with preliminary results indicating consistent patterns. In fact, tracking task related source locations render up to 66% accuracy in decoding movement direction one week after the decoding model was learnt.

Cortical graph smoothing: a novel method for exploiting DWI-derived anatomical brain connectivity to improve EEG source estimation

The electroencephalography source estimation problem consists of inferring cortical activation from measurements of electrical potential taken on the scalp surface. This inverse problem is intrinsically ill-posed. In particular the dimensionality of cortical sources greatly exceeds the number of electrode measurements, and source estimation requires regularization to obtain a unique solution. In this work, we introduce a novel regularization function called cortical graph smoothing, which exploits knowledge of anatomical connectivity available from diffusion-weighted imaging. Given a weighted graph description of the anatomical connectivity of the brain, cortical graph smoothing penalizes the weighted sum of squares of differences of cortical activity across the graph edges, thus encouraging solutions with consistent activation across anatomically connected regions. We explore the performance of the cortical graph smoothing source estimates for analysis of the event related potential for simple motor tasks, and compare against the commonly used minimum norm, weighted minimum norm, LORETA and sLORETA source estimation methods. Evaluated over a series of 18 subjects, the proposed cortical graph smoothing method shows superior localization accuracy compared to the minimum norm method, and greater relative peak intensity than the other comparison methods.

Emotional modulation of experimental pain: a source imaging study of laser evoked potentials

Negative emotions have been shown to augment experimental pain. As induced emotions alter brain activity, it is not clear whether pain augmentation during noxious stimulation would be related to neural activation existing prior to onset of a noxious stimulus or alternatively, whether emotional stimuli would only alter neural activity during the period of nociceptive processing. We analyzed the spatio-temporal patterns of laser evoked potentials (LEPs) occurring prior to and during the period of cortical processing of noxious laser stimuli during passive viewing of negative, positive, or neutral emotional pictures. Independent component analysis (ICA) was applied to series of source activation volumes, reconstructed using local autoregressive average model (LAURA). Pain was the strongest when laser stimuli were associated with negative emotional pictures. Prior to laser stimulus and during the first 100 ms after onset of laser stimulus, activations were seen in the left and right medial temporal cortex, cerebellum, posterior cingulate, and rostral cingulate/prefrontal cortex. In all these regions, positive or neutral pictures showed stronger activations than negative pictures. During laser stimulation, activations in the right and left anterior insula, temporal cortex and right anterior and posterior parietal cortex were stronger during negative than neutral or positive emotional pictures. Results suggest that negative emotional stimuli increase activation in the left and right anterior insula and temporal cortex, and right posterior and anterior parietal cortex only during the period of nociceptive processing. The role of background brain activation in emotional modulation of pain appears to be only permissive, and consisting in attenuation of activation in structures maintaining the resting state of the brain.

BOLD consistently matches electrophysiology in human sensorimotor cortex at increasing movement rates: a combined 7T fMRI and ECoG study on neurovascular coupling

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to measure human brain function and relies on the assumption that hemodynamic changes mirror the underlying neuronal activity. However, an often reported saturation of the BOLD response at high movement rates has led to the notion of a mismatch in neurovascular coupling. We combined BOLD fMRI at 7T and intracranial electrocorticography (ECoG) to assess the relationship between BOLD and neuronal population activity in human sensorimotor cortex using a motor task with increasing movement rates. Though linear models failed to predict BOLD responses from the task, the measured BOLD and ECoG responses from the same tissue were in good agreement. Electrocorticography explained almost 80% of the mismatch between measured- and model-predicted BOLD responses, indicating that in human sensorimotor cortex, a large portion of the BOLD nonlinearity with respect to behavior (movement rate) is well predicted by electrophysiology. The results further suggest that other reported examples of BOLD mismatch may be related to neuronal processes, rather than to neurovascular uncoupling.

Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals

The sources of somatosensory evoked potentials (SEPs) and fields (SEFs), which is a standard paradigm, is investigated using multichannel EEG and MEG simultaneous recordings. The hypothesis that SEP & SEF sources are generated in the posterior bank of the central sulcus is tested, and analyses are compared based on EEG only, MEG only, bandpass filtered MEG, and both combined. To locate the sources, the forward problem is first solved by using the boundary-element method for realistic head models and by using a locally-fitted-sphere approach for averaged head models consisting of a set of connected volumes, typically representing the skull, scalp, and brain. The location of each dipole is then estimated using fixed MUSIC and current-density-reconstruction (CDR) algorithms. For both analyses, the results demonstrate that the band-pass filtered MEG can localize the sources accurately at the desired region as compared to only EEG and unfiltered MEG. For CDR analysis, it looks like MEG affects EEG during the combined analyses. The MUSIC algorithm gives better results than CDR, and when comparing the two head models, the averaged and the realistic head models showed the same result.

A modified oddball paradigm for investigation of neural correlates of attention: a simultaneous ERP-fMRI study

Introduction

The objective of the presented study was to develop and evaluate a P300 experimental protocol for simultaneous registration of event-related potentials (ERPs) and functional MRI (fMRI) data with continuous imaging. It may be useful for investigating attention and working memory processes in specific populations, such as children and neuropsychiatric patients.

Materials and methods

Eleven children were investigated with simultaneous ERP–fMRI. To fulfill requirements of both BOLD and electroencephalographic signal registration, a modified oddball task was used. To verify the ERP–fMRI protocol we also performed a study outside the scanner using a typical two-stimuli oddball paradigm.

Results

Localization of the P300 component of ERPs partially corresponded with fMRI results in the frontal and parietal brain regions. FMRI activations were found in: middle frontal gyrus, insula, SMA, parietal lobule, thalamus, and cerebellum. Our modified oddball task provided ERP–fMRI results with high level of significance (EEG SNR = 35, fMRI p < 0.05–Bonf.). ERPs obtained in the scanner were comparable with those registered outside the scanner, although some differences in the amplitude were noticed, mainly in the N100 component.

Conclusion

In our opinion the presented paradigm may be successfully applied for simultaneous ERP–fMRI registration of neural correlates of attention in vulnerable populations.

Assisted closed-loop optimization of SSVEP-BCI efficiency

We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.

Evaluations of sparse source imaging and minimum norm estimate methods in both simulation and clinical MEG data

The aim of the present study is to evaluate the capability of a recently proposed l(1)-norm based regularization method, named as variation-based sparse cortical current density (VB-SCCD) algorithm, in estimating location and spatial coverage of extensive brain sources. Its performance was compared to the conventional minimum norm estimate (MNE) using both simulations and clinical interictal spike MEG data from epilepsy patients. Four metrics were adopted to evaluate two regularization methods for EEG/MEG inverse problems from different aspects in simulation study. Both methods were further compared in reconstructing epileptic sources and validated using results from clinical diagnosis. Both simulation and experimental results suggest VB-SCCD has better performance in localization and estimation of source extents, as well as less spurious sources than MNE, which makes it a promising noninvasive tool to assist presurgical evaluation for surgical treatment in epilepsy patients.

The relationship between brain cortical activity and brain oxygenation in the prefrontal cortex during hypergravity exposure

Artificial gravity has been proposed as a method to counteract the physiological deconditioning of long-duration spaceflight; however, the effects of hypergravity on the central nervous system has had little study. The study aims to investigate whether there is a relationship between prefrontal cortex brain activity and prefrontal cortex oxygenation during exposure to hypergravity. Twelve healthy participants were selected to undergo hypergravity exposure aboard a short-arm human centrifuge. Participants were exposed to hypergravity in the +Gz axis, starting from 0.6 +Gz for women, and 0.8 +Gz for men, and gradually increasing by 0.1 +Gz until the participant showed signs of syncope. Brain cortical activity was measured using electroencephalography (EEG) and localized to the prefrontal cortex using standard low-resolution brain electromagnetic tomography (LORETA). Prefrontal cortex oxygenation was measured using near-infrared spectroscopy (NIRS). A significant increase in prefrontal cortex activity (P < 0.05) was observed during hypergravity exposure compared with baseline. Prefrontal cortex oxygenation was significantly decreased during hypergravity exposure, with a decrease in oxyhemoglobin levels (P < 0.05) compared with baseline and an increase in deoxyhemoglobin levels (P < 0.05) with increasing +Gz level. No significant correlation was found between prefrontal cortex activity and oxy-/deoxyhemoglobin. It is concluded that the increase in prefrontal cortex activity observed during hypergravity was most likely not the result of increased +Gz values resulting in a decreased oxygenation produced through hypergravity exposure. No significant relationship between prefrontal cortex activity and oxygenation measured by NIRS concludes that brain activity during exposure to hypergravity may be difficult to measure using NIRS. Instead, the increase in prefrontal cortex activity might be attributable to psychological stress, which could pose a problem for the use of a short-arm human centrifuge as a countermeasure.

Wavelet based sparse source imaging technique

The present study proposed a novel multi-resolution wavelet to efficiently compress cortical current densities on the highly convoluted cortical surface. The basis function of the proposed wavelet is supported on triangular faces of the cortical mesh and it is thus named as the face-based wavelet to be distinguished from other vertex-based wavelets. The proposed face-based wavelet was used as a transform to gain the sparse representation of cortical sources and then was integrated into the framework of L1-norm regularizations with the purpose to improve the performance of sparse source imaging (SSI) in solving EEG/MEG inverse problems. Monte Carlo simulations were conducted with multiple extended sources (up to ten) at random locations. Experimental MEG data from an auditory induced language task was further adopted to evaluate the performance of the proposed wavelet based SSI technique. The present results indicated that the face-based wavelet can efficiently compress cortical current densities and has better performance than the vertex-based wavelet in helping inverse source reconstructions in terms of estimation accuracies in source localization and source extent. Experimental results further indicated improved detection performance of the face-based wavelet as compared with the vertex-based wavelet in the framework of SSI. It thus suggests the proposed wavelet based SSI can become a promising tool in studying brain functions and networks.

The virtual brain integrates computational modeling and multimodal neuroimaging

Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) ( www.thevirtualbrain.org ), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

Peripheral and central nervous contribution to gastrointestinal symptoms in diabetic patients with autonomic neuropathy

BACKGROUND & AIMS

Long-term diabetes mellitus (DM) has been associated with neuronal changes in the enteric, peripheral and/or central nervous system. Moreover, abnormal visceral sensation and gastrointestinal (GI) symptoms are seen in up to 75% of patients. To explore the role of diabetic autonomic neuropathy (DAN) in patients with long-standing DM, we investigated psychophysical responses and neuronal activity recorded as evoked brain potentials and dipolar source modelling.

METHODS

Fifteen healthy volunteers and 14 type-1 DM patients with DAN were assessed with a symptom score index characterizing upper GI abnormalities. Multichannel (62) electroencephalography was recorded during painful electrical stimulation of the lower oesophagus. Brain activity to painful stimulations was modelled using Brain Electrical Source Analysis (besa).

RESULTS

Diabetic patients had higher stimulus intensities to evoke painful sensation (p ≤ 0.001), longer latencies of N2 and P2 components (both p ≤ 0.001), and lower amplitudes of P1-N2 and N2-P2 complexes (p ≤ 0.001; p = 0.02). Inverse modelling of brain sources showed deeper bilateral insular dipolar source localization (p = 0.002). Symptom score index was negatively correlated with the depth of insular activity (p = 0.004) and positively correlated with insular dipole strength (p = 0.03).

CONCLUSION

DM patients show peripheral and central neuroplastic changes. Moreover, the role of abnormal insular processing may explain the appearance and persistence of GI symptoms related to DAN. This enhanced understanding of DAN may have future clinical and therapeutical implications.

Harmony: EEG/MEG linear inverse source reconstruction in the anatomical basis of spherical harmonics

EEG/MEG source localization based on a "distributed solution" is severely underdetermined, because the number of sources is much larger than the number of measurements. In particular, this makes the solution strongly affected by sensor noise. A new way to constrain the problem is presented. By using the anatomical basis of spherical harmonics (or spherical splines) instead of single dipoles the dimensionality of the inverse solution is greatly reduced without sacrificing the quality of the data fit. The smoothness of the resulting solution reduces the surface bias and scatter of the sources (incoherency) compared to the popular minimum-norm algorithms where single-dipole basis is used (MNE, depth-weighted MNE, dSPM, sLORETA, LORETA, IBF) and allows to efficiently reduce the effect of sensor noise. This approach, termed Harmony, performed well when applied to experimental data (two exemplars of early evoked potentials) and showed better localization precision and solution coherence than the other tested algorithms when applied to realistically simulated data.

Sparse imaging of cortical electrical current densities via wavelet transforms

While the cerebral cortex in the human brain is of functional importance, functions defined on this structure are difficult to analyze spatially due to its highly convoluted irregular geometry. This study developed a novel L1-norm regularization method using a newly proposed multi-resolution face-based wavelet method to estimate cortical electrical activities in electroencephalography (EEG) and magnetoencephalography (MEG) inverse problems. The proposed wavelets were developed based on multi-resolution models built from irregular cortical surface meshes, which were realized in this study too. The multi-resolution wavelet analysis was used to seek sparse representation of cortical current densities in transformed domains, which was expected due to the compressibility of wavelets, and evaluated using Monte Carlo simulations. The EEG/MEG inverse problems were solved with the use of the novel L1-norm regularization method exploring the sparseness in the wavelet domain. The inverse solutions obtained from the new method using MEG data were evaluated by Monte Carlo simulations too. The present results indicated that cortical current densities could be efficiently compressed using the proposed face-based wavelet method, which exhibited better performance than the vertex-based wavelet method. In both simulations and auditory experimental data analysis, the proposed L1-norm regularization method showed better source detection accuracy and less estimation errors than other two classic methods, i.e. weighted minimum norm (wMNE) and cortical low-resolution electromagnetic tomography (cLORETA). This study suggests that the L1-norm regularization method with the use of face-based wavelets is a promising tool for studying functional activations of the human brain.

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.

Impact of lower- vs. upper-hemifield presentation on automatic colour-deviance detection: a visual mismatch negativity study

The automatic processing of deviances from the temporal context of the visual environment has become an important topic in visual cognitive sciences, which is often investigated using the visual mismatch negativity (vMMN). This event-related potential (ERP) component is elicited by an irregular stimulus (e.g., a red disc) presented in a series of stimuli (e.g., green discs) comprising a temporal regularity (e.g., colour repetition). We determined the influence of lower- vs. upper-hemifield presentation of the irregular stimulus on the vMMN while using whole-field stimulus displays controlling for sustained shifts in spatial attention. Deviances presented in the lower hemifield elicited a larger vMMN than the ones presented in the upper hemifield at a latency of 200-280ms. However, this asymmetry was preceded by deviance-related hemifield effects already emerging at an earlier latency (110-150ms), where upper-hemifield deviances elicited a positive potential but lower-hemifield deviances did not. With variable resolution electromagnetic tomography (VARETA) early deviance-related activity was localised to retinotopically organised regions of the visual cortex (BA 17/18) and vMMN-sources were localised to the middle/superior occipital gyrus, to higher areas along the temporal visual stream, but also to BA 17/18. We argue that the upper/lower-hemifield vMMN asymmetry relies at least partially on the hemifield-dependent differential sensitivity of early deviance-related activity generated in retinotopically organised regions of the visual cortex. However, a superior automatic processing of deviances presented in the lower visual hemifield may also contribute to the effect.

Methods for simultaneous EEG-fMRI: an introductory review

The simultaneous recording and analysis of electroencephalography (EEG) and fMRI data in human systems, cognitive and clinical neurosciences is rapidly evolving and has received substantial attention. The significance of multimodal brain imaging is documented by a steadily increasing number of laboratories now using simultaneous EEG-fMRI aiming to achieve both high temporal and spatial resolution of human brain function. Due to recent developments in technical and algorithmic instrumentation, the rate-limiting step in multimodal studies has shifted from data acquisition to analytic aspects. Here, we introduce and compare different methods for data integration and identify the benefits that come with each approach, guiding the reader toward an understanding and informed selection of the integration approach most suitable for addressing a particular research question.

The impact of acute psychosocial stress on magnetoencephalographic correlates of emotional attention and exogenous visual attention

Stress-induced acute activation of the cerebral catecholaminergic systems has often been found in rodents. However, little is known regarding the consequences of this activation on higher cognitive functions in humans. Theoretical inferences would suggest increased distractibility in the sense of increased exogenous attention and emotional attention. The present study investigated the influence of acute stress responses on magnetoencephalographic (MEG) correlates of visual attention. Healthy male subjects were presented emotional and neutral pictures in three subsequent MEG recording sessions after being exposed to a TSST-like social stressor, intended to trigger a HPA-response. The subjects anticipation of another follow-up stressor was designed to sustain the short-lived central catecholaminergic stress reactions throughout the ongoing MEG recordings. The heart rate indicates a stable level of anticipatory stress during this time span, subsequent cortisol concentrations and self-report measures of stress were increased. With regard to the MEG correlates of attentional functions, we found that the N1m amplitude remained constantly elevated during stressor anticipation. The magnetic early posterior negativity (EPNm) was present but, surprisingly, was not at all modulated during stressor anticipation. This suggests that a general increase of the influence of exogenous attention but no specific effect regarding emotional attention in this time interval. Regarding the time course of the effects, an influence of the HPA on these MEG correlates of attention seems less likely. An influence of cerebral catecholaminergic systems is plausible, but not definite.

Zur Anwendung der Elektroenzephalographie in der Sportpsychologie

Zusammenfassung: Das Elektroenzephalogramm (EEG) ist ein geeignetes Instrument, um diejenigen bioelektrischen Vorgänge zu untersuchen, die kognitiven Prozessen oder emotionalen Zuständen zugrunde liegen, welche fundamentale Prozesse im Sport darstellen. Ziel dieses Artikels ist es, die methodischen Möglichkeiten der Elektroenzephalographie in bewegungs- und sportwissenschaftlichen Studien zu beleuchten, einen Überblick über bisherige Befunde zu geben und die Verwendung des EEGs kritisch zu bewerten. Nach einer einführenden Darstellung der Grundlagen des EEGs und der wichtigsten Analysemöglichkeiten, werden drei Gruppen von Studien diskutiert, die sich mit den EEG Korrelaten 1. grundlegende Aspekte von Bewegung und Bewegungslernen, 2. aufmerksamkeitsspezifische Veränderungen während der Bewegung und 3. affektive Veränderungen im Zusammenhang mit sportlicher Bewegung beschäftigen. Es wird die Relevanz der Elektroenzephalographie aufgezeigt und abschließend auch auf die Limitationen dieses Zuganges eingegangen.

Inverse modeling in magnetic source imaging: Comparison of MUSIC, SAM(g2), and sLORETA to interictal intracranial EEG

Magnetoencephalography (MEG) is used in the presurgical work-up of patients with focal epilepsy. In particular, localization of MEG interictal spikes may guide or replace invasive electroencephalography monitoring that is required in difficult cases. From literature, it is not clear which MEG source localization method performs best in this clinical setting. Therefore, we applied three source localization methods to the same data from a large patient group for which a gold standard, interictal spikes as identified in electrocorticography (ECoG), was available. The methods used were multiple signal classification (MUSIC), Synthetic Aperture Magnetometry kurtosis [SAM(g2)], and standardized low-resolution electromagnetic tomography. MEG and ECoG data from 38 patients with refractory focal epilepsy were obtained. Results of the three source localization methods applied to the interictal MEG data were assigned to predefined anatomical regions. Interictal spikes as identified in ECoG were also assigned to these regions. Identified regions by each MEG method were compared to ECoG. Sensitivity and positive predictive value (PPV) of each MEG method were calculated. All three MEG methods showed a similar overall correlate with ECoG spikes, but the methods differ in which regions they detect. The choice of the inverse model thus has an unexpected influence on the results of magnetic source imaging. Combining inverse methods and seeking consensus can be used to improve specificity at the cost of some sensitivity. Combining MUSIC with SAM(g2) gives the best results (sensitivity = 38% and PPV = 82%).

Real-time fMRI applied to pain management

Current views recognize the brain as playing a pivotal role in the arising and maintenance of pain experience. Real-time fMRI (rtfMRI) feedback is a potential tool for pain modulation that directly targets the brain with the goal of restoring regulatory function. Though still relatively new, rtfMRI is a rapidly developing technology that has evolved in the last 15 years from simple proof of concept experiments to demonstrations of learned control of single and multiple brain areas. Numerous studies indicate rtfMRI feedback assisted control over specific brain areas may have applications including mood regulation, language processing, neurorehabilitation in stroke, enhancement of perception and learning, and pain management. We discuss in detail earlier work from our lab in which rtfMRI feedback was used to train both healthy controls and chronic pain patients to modulate anterior cingulate cortex (ACC) activation for the purposes of altering pain experience. Both groups improved in their ability to control ACC activation and modulate their pain with rtfMRI feedback training. Furthermore, the degree to which participants were able to modulate their pain correlated with the degree of control over ACC activation. We additionally review current advances in rtfMRI feedback, such as real-time pattern classification, that bring the technology closer to more comprehensive control over neural function. Finally, remaining methodological questions concerning the further development of rtfMRI feedback and its implications for the future of pain research are also discussed.

Assessing cortical network properties using TMS-EEG

The past decade has seen significant developments in the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to directly assess cortical network properties such as excitability and connectivity in humans. New hardware solutions, improved EEG amplifier technology, and advanced data processing techniques have allowed substantial reduction of the TMS-induced artifact, which had previously rendered concurrent TMS-EEG impossible. Various physiological artifacts resulting from TMS have also been identified, and methods are being developed to either minimize or remove these sources of artifact. With these developments, TMS-EEG has unlocked regions of the cortex to researchers that were previously inaccessible to TMS. By recording the TMS-evoked response directly from the cortex, TMS-EEG provides information on the excitability, effective connectivity, and oscillatory tuning of a given cortical area, removing the need to infer such measurements from indirect measures. In the following review, we investigate the different online and offline methods for reducing artifacts in TMS-EEG recordings and the physiological information contained within the TMS-evoked cortical response. We then address the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity, before briefly reviewing studies that have utilized TMS-EEG to explore cortical network properties at rest and during different functional brain states.

Dynamic changes of ICA-derived EEG functional connectivity in the resting state

An emerging issue in neuroscience is how to identify baseline state(s) and accompanying networks termed "resting state networks" (RSNs). Although independent component analysis (ICA) in fMRI studies has elucidated synchronous spatiotemporal patterns during cognitive tasks, less is known about the changes in EEG functional connectivity between eyes closed (EC) and eyes open (EO) states, two traditionally used baseline indices. Here we investigated healthy subjects (n = 27) in EC and EO employing a four-step analytic approach to the EEG: (1) group ICA to extract independent components (ICs), (2) standardized low-resolution tomography analysis (sLORETA) for cortical source localization of IC network nodes, followed by (3) graph theory for functional connectivity estimation of epochwise IC band-power, and (4) circumscribing IC similarity measures via hierarchical cluster analysis and multidimensional scaling (MDS). Our proof-of-concept results on alpha-band power demonstrate five statistically clustered groups with frontal, central, parietal, occipitotemporal, and occipital sources. Importantly, during EO compared with EC, graph analyses revealed two salient functional networks with frontoparietal connectivity: a more medial network with nodes in the mPFC/precuneus which overlaps with the "default-mode network" (DMN), and a more lateralized network comprising the middle frontal gyrus and inferior parietal lobule, coinciding with the "dorsal attention network" (DAN). Furthermore, a separate MDS analysis of ICs supported the emergence of a pattern of increased proximity (shared information) between frontal and parietal clusters specifically for the EO state. We propose that the disclosed component groups and their source-derived EEG functional connectivity maps may be a valuable method for elucidating direct neuronal (electrophysiological) RSNs in healthy people and those suffering from brain disorders.

A general Bayesian treatment for MEG source reconstruction incorporating lead field uncertainty

There is uncertainty introduced when a cortical surface based model derived from an anatomical MRI is used to reconstruct neural activity with MEG data. This is a specific case of a problem with uncertainty in parameters on which M/EEG lead fields depend non-linearly. Here we present a general mathematical treatment of any such problem with a particular focus on co-registration. We use a Metropolis search followed by Bayesian Model Averaging over multiple sparse prior source inversions with different headlocation/orientation parameters. Based on MEG data alone we can locate the cortex to within 4mm at empirically realistic signal to noise ratios. We also show that this process gives improved posterior distributions on the estimated current distributions, and can be extended to make inference on the locations of local maxima by providing confidence intervals for each source.

EEG source localization based on multivariate autoregressive models using Kalman filtering

The estimation of current distributions from electroencephalographic recordings poses an inverse problem, which can approximately be solved by including dynamical models as spatio-temporal constraints onto the solution. In this paper, we consider the electrocardiography source localization task, where a specific structure for the dynamical model of current distribution is directly obtained from the data by fitting multivariate autoregressive models to electroencephalographic time series. Whereas previous approaches consider an approximation of the internal connectivity of the sources, the proposed methodology takes into account a realistic structure of the model estimated from the data, such that it becomes possible to obtain improved inverse solutions. The performance of the new method is demonstrated by application to simulated electroencephalographic data over several signal to noise ratios, where the source localization task is evaluated by using the localization error and the data fit error. Finally, it is shown that estimating MVAR models makes possible to obtain inverse solutions of considerably improved quality, as compared to the usual instantaneous inverse solutions, even if the regularized inverse of Tikhonov is used.

The quantitative electroencephalogram and the low-resolution electrical tomographic analysis in posttraumatic stress disorder

The electroencephalogram (EEG) is the recording of the brain electrical activity as measured on the scalp. Using mathematical algorithms, the 3-dimensional (3D) distribution of the electrical potential inside the brain can be calculated. One of the methods to calculate it is the low-resolution electrical tomographic analysis (LORETA). In this research, we seek to find the brain structures that differentiate patients with posttraumatic stress disorder (PTSD) from controls. Ten right-handed consenting adult male patients were recruited from a PTSD clinic. All patients fulfilled Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision [DSM-IV-TR]) criteria for chronic PTSD (duration >2 years.) and were on drug treatment regimens that had been stable for at least 2 months (involving only serotonin reuptake inhibitors [SSRIs] and benzodiazepines).The control group consisted of 10 healthy hospital staff members. All study participants underwent 19 channel EEG measurements according to current standards of practice. All artifact-free EEG strips were examined for spectral as well as LORETA analysis focusing on the theta (4-7 Hz) band which is suggested to reflect the activity of the limbic system. The theta band showed a statistically significant difference (P < .05) between the 2 groups in the right temporal lobe and in both the right and left frontal lobes. Our findings support existing research data obtained via other imaging technologies, which demonstrated structural alterations in the right temporal and frontal areas in PTSD. These results indicate that combining quantitative EEG (QEEG) and the LORETA method, among other methods, may improve the neuroanatomical resolution of EEG data analysis.

Stability of ICA decomposition across within-subject EEG datasets

Independent Component Analysis (ICA) has been successfully used to identify brain related signals and artifacts from multi-channel electroencephalographic (EEG) data. However the stability of ICA decompositions across sessions from a single subject has not been investigated. The goal of this study was to isolate EEG independent components (ICs) across sessions for each subject so as to assess whether ICs are reproducible across sessions. We used 64-channel EEG data recorded from two subjects during a simple mind-wandering experiment. Each subject participated in 11 twenty-minute sessions over a period of five weeks. Extended Infomax ICA decomposition was performed on the continuous data of each session. We used a simple IC clustering technique based on correlation of scalp topographies. Several clusters of homogenous components were identified for each subject. Typical component clusters accounting for eye movement and eye blink artifacts were identified. Both clusters included one component from each recording session. In addition, several clusters corresponding to brain electrical sources, among them clusters exhibiting prominent alpha, beta and Mu band activities, included components from most sessions. These results present evidence that ICA can provide relatively stable solutions across sessions, with important implications for Brain Computer Interface research.