Review on solving the inverse problem in EEG source analysis

Determination of head conductivity frequency response in vivo with optimized EIT-EEG

Electroencephalography (EEG) benefits from accurate head models. Dipole source modelling errors can be reduced from over 1cm to a few millimetres by replacing generic head geometry and conductivity with tailored ones. When adequate head geometry is available, electrical impedance tomography (EIT) can be used to infer the conductivities of head tissues. In this study, the boundary element method (BEM) is applied with three-compartment (scalp, skull and brain) subject-specific head models. The optimal injection of small currents to the head with a modular EIT current injector, and voltage measurement by an EEG amplifier is first sought by simulations. The measurement with a 64-electrode EEG layout is studied with respect to three noise sources affecting EIT: background EEG, deviations from the fitting assumption of equal scalp and brain conductivities, and smooth model geometry deviations from the true head geometry. The noise source effects were investigated depending on the positioning of the injection and extraction electrode and the number of their combinations used sequentially. The deviation from equal scalp and brain conductivities produces rather deterministic errors in the three conductivities irrespective of the current injection locations. With a realistic measurement of around 2 min and around 8 distant distinct current injection pairs, the error from the other noise sources is reduced to around 10% or less in the skull conductivity. The analysis of subsequent real measurements, however, suggests that there could be subject-specific local thinnings in the skull, which could amplify the conductivity fitting errors. With proper analysis of multiplexed sinusoidal EIT current injections, the measurements on average yielded conductivities of 340 mS/m (scalp and brain) and 6.6 mS/m (skull) at 2 Hz. From 11 to 127 Hz, the conductivities increased by 1.6% (scalp and brain) and 6.7% (skull) on the average. The proper analysis was ensured by using recombination of the current injections into virtual ones, avoiding problems in location-specific skull morphology variations. The observed large intersubject variations support the need for in vivo measurement of skull conductivity, resulting in calibrated subject-specific head models.

Sleep as a biological problem: an overview of frontiers in sleep research

Sleep is a physiological process not only for the rest of the body but also for several brain functions such as mood, memory, and consciousness. Nevertheless, the nature and functions of sleep remain largely unknown due to its extremely complicated nature and lack of optimized technology for the experiments. Here we review the recent progress in the biology of the mammalian sleep, which covers a wide range of research areas: the basic knowledge about sleep, the physiology of cerebral cortex in sleeping animals, the detailed morphological features of thalamocortical networks, the mechanisms underlying fluctuating activity of autonomic nervous systems during rapid eye movement sleep, the cutting-edge technology of tissue clearing for visualization of the whole brain, the ketogenesis-mediated homeostatic regulation of sleep, and the forward genetic approach for identification of novel genes involved in sleep. We hope this multifaceted review will be helpful for researchers who are interested in the biology of sleep.

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing “in action and in context”. This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

Cortical surface reconstruction based on MEG data and spherical harmonics

Estimates of coefficients of a spherical harmonic Fourier decomposition of the cortical surface can be obtained solely using MEG/EEG data and free energy as objective function. A stochastic methodology based on a Metropolis Search followed by a Bayesian Model Averaging is proposed to reconstruct cortical anatomy based functional information.

The impact of experienced stress on aged spatial discrimination: Cortical overreliance as a result of hippocampal impairment

A large body of neuroscientific work indicates that exposure to experienced stress causes damage to both cortical and hippocampal cells and results in impairments to cognitive abilities associated with these structures. Similarly, work within the domain of cognitive aging demonstrates that elderly participants who report experiencing greater amounts of stress show reduced levels of cognitive functioning. The present article attempted to combine both findings by collecting data from elderly and young participants who completed a spatial discrimination paradigm developed by Reagh and colleagues [Reagh et al. (2013) Hippocampus 24:303-314] to measure hippocampal-mediated cognitive processes. In order to investigate the effect of stress on the cortex and, indirectly, the hippocampus, it paired the paradigm with electroencephalographic recordings of the theta frequency band, which is thought to reflect cortical/hippocampal interactions. Findings revealed that elderly participants with high levels of experienced stress performed significantly worse on target recognition and lure discrimination and demonstrated heightened levels of cortical theta synchronization compared with young and elderly low stress counterparts. Results therefore provided further evidence for the adverse effect of stress on cognitive aging and indicate that impaired behavioral performance among high stress elderly may coincide with an overreliance on cortical cognitive processing strategies as a result of early damage to the hippocampus.

EEG resting state analysis of cortical sources in patients with benign epilepsy with centrotemporal spikes

Benign epilepsy with centrotemporal spikes (BECTS) is the most common idiopathic childhood epilepsy, which is often associated with developmental disorders in children. In the present study, we analyzed resting state EEG spectral changes in the sensor and source spaces in eight BECTS patients compared with nine age-matched controls. Using high-resolution scalp EEG data, we assessed statistical differences in spatial distributions of EEG power spectra and cortical sources of resting state EEG rhythms in five frequency bands: δ (0.5–3.5 Hz), θ (4–8 Hz), α (8.5–13 Hz), β₁ (13.5–20 Hz) and β₂ (20.5–30 Hz) under the eyes-closed resting state condition. To further investigate the impact of centrotemporal spikes on EEG spectra, we split the EEG data of the patient group into EEG portions with and without spikes. Source localization demonstrated the homogeneity of our population of BECTS patients with a common epileptic zone over the right centrotemporal region. Significant differences in terms of both spectral power and cortical source densities were observed between controls and patients. Patients were characterized by significantly increased relative power in θ, α, β₁ and β₂ bands in the right centrotemporal areas over the spike zone and in the right temporo-parieto-occipital junction. Furthermore, the relative power in all bands significantly decreased in the bilateral frontal and parieto-occipital areas of patients regardless of the presence or absence of spikes in EEG segments. However, the spectral differences between patients and controls were more pronounced in the presence of spikes. This observation emphasized the impact of benign epilepsy on cortical source power, especially in the right centrotemporal regions. Spectral changes in bilateral frontal and parieto-occipital areas may also suggest alterations in the default mode network in BECTS patients.

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BECTS patients exhibited enhanced θ activity over all cortical regions.

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BECTS patients displayed reduced α activity at the occipital regions.

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Patients showed increased power in θ, α, β in right temporo-parieto-occipital pole.

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EEG spectral changes in BECTS patients indicate dysfunction at the epileptic zone.

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EEG spectral changes in BECTS patients may show alteration in default mode network.

Source analysis of electrophysiological correlates of beat induction as sensory-guided action

In this paper we present a reanalysis of electrophysiological data originally collected to test a sensory-motor theory of beat induction (Todd et al., 2002; Todd and Seiss, 2004; Todd and Lee, 2015). The reanalysis is conducted in the light of more recent findings and in particular the demonstration that auditory evoked potentials contain a vestibular dependency. At the core of the analysis is a model which predicts brain dipole source current activity over time in temporal and frontal lobe areas during passive listening to a rhythm, or active synchronization, where it dissociates the frontal activity into distinct sources which can be identified as respectively pre-motor and motor in origin. The model successfully captures the main features of the rhythm in showing that the metrical structure is manifest in an increase in source current activity during strong compared to weak beats. In addition the outcomes of modeling suggest that: (1) activity in both temporal and frontal areas contribute to the metrical percept and that this activity is distributed over time; (2) transient, time-locked activity associated with anticipated beats is increased when a temporal expectation is confirmed following a previous violation, such as a syncopation; (3) two distinct processes are involved in auditory cortex, corresponding to tangential and radial (possibly vestibular dependent) current sources. We discuss the implications of these outcomes for the insights they give into the origin of metrical structure and the power of syncopation to induce movement and create a sense of groove.

EEG source localization constrained by time varying fMRI

A novel approach for Electroencephalogram (EEG) and functional Magnetic Resonance Imaging (fMRI) integration analysis was developed, specifically designed to explore the spatial and temporal details of the "sequential multi-event-related potential" type of neural activities. The approach utilizes the high temporal resolution nature of EEG to compute a current density mapping of the cortical activity, informed by the high spatial resolution fMRI in a time-variant, spatially selective manner. This method was implemented in the analysis of an EEG/fMRI study on motor activation in responses to a visual stimulus that evoked an emotional response. The processed windowed EEG signals were analyzed to select the temporally relevant partial fMRI mapping, which in turn was used to inform EEG source localization calculation. The results were compared against traditional fMRI-informed EEG approaches to demonstrate the spatiotemporal variant fMRI constraints feature as well as the performance of the developed method.

Feature Extraction for BCIs Based on Electromagnetic Source Localization and Multiclass Filter Bank Common Spatial Patterns

Brain-Computer Interfaces (BCIs) provide means for communication and control without muscular movement and, therefore, can offer significant clinical benefits. Electrical brain activity recorded by electroencephalography (EEG) can be interpreted into software commands by various classification algorithms according to the descriptive features of the signal. In this paper we propose a novel EEG BCI feature extraction method employing EEG source reconstruction and Filter Bank Common Spatial Patterns (FBCSP) based on Joint Approximate Diagonalization (JAD). The proposed method is evaluated by the commonly used reference EEG dataset yielding an average classification accuracy of 77.1 ± 10.1 %. It is shown that FBCSP feature extraction applied to reconstructed source components outperforms conventional CSP and FBCSP feature extraction methods applied to signals in the sensor domain.

Group-level component analyses of EEG: validation and evaluation

Multi-subject or group-level component analysis provides a data-driven approach to study properties of brain networks. Algorithms for group-level data decomposition of functional magnetic resonance imaging data have been brought forward more than a decade ago and have significantly matured since. Similar applications for electroencephalographic data are at a comparatively early stage of development though, and their sensitivity to topographic variability of the electroencephalogram or loose time-locking of neuronal responses has not yet been assessed. This study investigates the performance of independent component analysis (ICA) and second order blind source identification (SOBI) for data decomposition, and their combination with either temporal or spatial concatenation of data sets, for multi-subject analyses of electroencephalographic data. Analyses of simulated sources with different spatial, frequency, and time-locking profiles, revealed that temporal concatenation of data sets with either ICA or SOBI served well to reconstruct sources with both strict and loose time-locking, whereas performance decreased in the presence of topographical variability. The opposite pattern was found with a spatial concatenation of subject-specific data sets. This study proofs that procedures for group-level decomposition of electroencephalographic data can be considered valid and promising approaches to infer the latent structure of multi-subject data sets. Yet, specific implementations need further adaptations to optimally address sources of inter-subject and inter-trial variance commonly found in EEG recordings.

Brain plasticity in aphasic patients: intra- and inter-hemispheric reorganisation of the whole linguistic network probed by N150 and N350 components

The present study examined linguistic plastic reorganization of language through Evoked Potentials in a group of 17 non-fluent aphasic patients who had suffered left perisylvian focal lesions, and showed a good linguistic recovery. Language reorganisation was probed with three linguistic tasks (Phonological, Semantic, Orthographic), the early word recognition potential (N150) and the later phonological-related component (N350). Results showed the typical left-lateralised posterior N150 in healthy controls (source: left Fusiform Gyrus), that was bilateral (Semantic) or right sided (Phonological task) in patients (sources: right Inferior/Middle Temporal and Fusiform Gyri). As regards N350, controls revealed different intra- and inter-hemispheric linguistic activation across linguistic tasks, whereas patients exhibited greater activity in left intact sites, anterior and posterior to the damaged area, in all tasks (sources: Superior Frontal Gyri). A comprehensive neurofunctional model is presented, describing how complete intra- and inter-hemispheric reorganisation of the linguistic networks occurs after aphasic damage in the strategically dominant left perisylvian linguistic centres.

Intrinsic coupling modes in source-reconstructed electroencephalography

Intrinsic coupling of neuronal assemblies constitutes a key feature of ongoing brain activity, yielding the rich spatiotemporal patterns observed in neuroimaging data and putatively supporting cognitive processes. Intrinsic coupling has been investigated in electrophysiological recordings using two types of functional connectivity measures: amplitude and phase coupling. These two coupling modes differ in their likely causes and functions, and have been proposed to provide complementary insights into intrinsic neuronal interactions. Here, we investigate the relationship between amplitude and phase coupling in source-reconstructed electroencephalography (EEG). Volume conduction is a key obstacle for connectivity analysis in EEG-we therefore also test the envelope correlation of orthogonalized signals and the phase lag index. Functional connectivity between six seed source regions (bilateral visual, sensorimotor, and auditory cortices) and all other cortical voxels was computed. For all four measures, coupling between homologous sensory areas in both hemispheres was significantly higher than with other voxels at the same physical distance. The frequency of significant coupling differed between sensory areas: 10 Hz for visual, 30 Hz for auditory, and 40 Hz for sensorimotor cortices. By contrasting envelope correlations and phase locking values, we observed two distinct clusters of voxels showing a different relationship between amplitude and phase coupling. Large clusters contiguous to the seed regions showed an identity (1:1) relationship between amplitude and phase coupling, whereas a cluster located around the contralateral homologous regions showed higher phase than amplitude coupling. These results show a relationship between intrinsic coupling modes that is distinct from the effect of volume conduction.

A study on EEG-based brain electrical source of mild depressed subjects

BACKGROUND AND OBJECTIVE

Several abnormal brain regions are known to be linked to depression, including amygdala, orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC) etc. The aim of this study is to apply EEG (electroencephalogram) data analysis to investigate, with respect to mild depression, whether there exists dysregulation in these brain regions.

METHODS

EEG sources were assessed from 9 healthy and 9 mildly depressed subjects who were classified according to the Beck Depression Inventory (BDI) criteria. t-Test was used to calculate the eye movement data and standardized low resolution tomography (sLORETA) was used to correlate EEG activity.

RESULTS

A comparison of eye movement data between the healthy and mild depressed subjects exhibited that mildly depressed subjects spent more time viewing negative emotional faces. Comparison of the EEG from the two groups indicated higher theta activity in BA6 (Brodmann area) and higher alpha activity in BA38.

CONCLUSIONS

EEG source location results suggested that temporal pole activity to be dysregulated, and eye-movement data analysis exhibited mild depressed subjects paid much more attention to negative face expressions, which is also in accordance with the results of EEG source location.

Cortical source localization of mouse extracranial electroencephalogram using the FieldTrip toolbox

Neuronal source estimation is a general tool for analyzing spatiotemporal dynamics in human EEG. Despite rapidly-evolving interest in human brain, there are few EEG based source estimation tools in rodent brain. Therefore, we implemented source estimation tool in a mouse model, using the FieldTrip open-source software. High resolution EEGs with a known cortical source were recorded with a recently developed 40-channel polyimide-based microelectrode under optical stimulation on optogenetially engineered mice. To obtain realistic mouse head models, the volume conduction model was extracted from in vitro mouse brain MRIs. Segmented compartments (skin and outer/inner skull) were used to form triangular meshes and then applied to the boundary element method. The high-resolution EEGs recorded during various optogenetic stimulation of the mouse brain were inversely source reconstructed using minimum-norm estimate. Estimated source locations and strengths were reconstructed, and their error was calculated to evaluate FieldTrip-based source localization algorithm. In summary, source localization imaging of the mouse brain was successfully achieved, using freely-available open source software. This will be useful to investigate the functional dynamics of mouse brain in noninvasive measure.

Sparse EEG Source Localization Using Bernoulli Laplacian Priors

Source localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual l2 norm has been considered and provides solutions with low computational complexity. However, in several situations, realistic brain activity is believed to be focused in a few focal areas. In these cases, the l2 norm is known to overestimate the activated spatial areas. One solution to this problem is to promote sparse solutions for instance based on the l1 norm that are easy to handle with optimization techniques. In this paper, we consider the use of an l0 + l1 norm to enforce sparse source activity (by ensuring the solution has few nonzero elements) while regularizing the nonzero amplitudes of the solution. More precisely, the l0 pseudonorm handles the position of the nonzero elements while the l1 norm constrains the values of their amplitudes. We use a Bernoulli-Laplace prior to introduce this combined l0 + l1 norm in a Bayesian framework. The proposed Bayesian model is shown to favor sparsity while jointly estimating the model hyperparameters using a Markov chain Monte Carlo sampling technique. We apply the model to both simulated and real EEG data, showing that the proposed method provides better results than the l2 and l1  norms regularizations in the presence of pointwise sources. A comparison with a recent method based on multiple sparse priors is also conducted.

The effects of noise vocoding on gap detection thresholds

Gap detection threshold (GDT), the shortest silent interval a person can perceive, is a commonly used measure of temporal processing resolution. The purposes of this study were: (1) to examine the effects of noise vocoding, which has been used to simulate what signals sound like through a cochlear implant, on GDTs in normal-hearing subjects, and (2) to further the understanding of neural mechanisms underlying gap detection using the Auditory Late Response (ALR). Thirteen normal listeners participated. In behavioral tests, the GDTs were determined for the original and vocoded stimuli. In ALR recordings, the subjects were presented with auditory stimuli with and without containing gaps and stimuli with and without being vocoded. Results showed that GDTs were significantly elevated for vocoded stimuli with spectral resolutions of 4 and 20 channels compared to those for the original stimuli. A gap effect was observed in the post-gap ALR. Current densities for N1 peaks evoked by stimuli with zero- vs. non-zero ms gaps, pre- vs. post-gap markers, and original vs. vocoded stimuli were obtained using the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Paired comparisons of pre- and post-gap current density values were made. Results showed a statistical difference between the N1s evoked by pre- vs. post-gap markers, with the activation in the middle frontal gyrus and precentral gyrus. The results suggest that: (1) noise vocoding does affect temporal processing resolution assessed with GDTs, (2) gap detection may involve the recruitment of cognitive neural resources, and (3) the ALR has a potential value of objectively estimating temporal processing resolution.

Self-regulation of brain activity in patients with postherpetic neuralgia: a double-blind randomized study using real-time FMRI neurofeedback

Background

A pilot study has shown that real-time fMRI (rtfMRI) neurofeedback could be an alternative approach for chronic pain treatment. Considering the relative small sample of patients recruited and not strictly controlled condition, it is desirable to perform a replication as well as a double-blinded randomized study with a different control condition in chronic pain patients. Here we conducted a rtfMRI neurofeedback study in a subgroup of pain patients – patients with postherpetic neuralgia (PHN) and used a different sham neurofeedback control. We explored the feasibility of self-regulation of the rostral anterior cingulate cortex (rACC) activation in patients with PHN through rtfMRI neurofeedback and regulation of pain perception.

Methods

Sixteen patients (46–71 years) with PHN were randomly allocated to a experimental group (n = 8) or a control group (n = 8). 2 patients in the control group were excluded for large head motion. The experimental group was given true feedback information from their rACC whereas the control group was given sham feedback information from their posterior cingulate cortex (PCC). All subjects were instructed to perform an imagery task to increase and decrease activation within the target region using rtfMRI neurofeedback.

Results

Online analysis showed 6/8 patients in the experimental group were able to increase and decrease the blood oxygen level dependent (BOLD) fMRI signal magnitude during intermittent feedback training. However, this modulation effect was not observed in the control group. Offline analysis showed that the percentage of BOLD signal change of the target region between the last and first training in the experimental group was significantly different from the control group’s and was also significantly different than 0. The changes of pain perception reflected by numerical rating scale (NRS) in the experimental group were significantly different from the control group. However, there existed no significant correlations between BOLD signal change and NRS change.

Conclusion

Patients with PHN could learn to voluntarily control over activation in rACC through rtfMRI neurofeedback and alter their pain perception level. The present study may provide new evidence that rtfMRI neurofeedback training may be a supplemental approach for chronic clinical pain management.

Persistent decrease in alpha current density in fully remitted subjects with major depressive disorder treated with fluoxetine: A prospective electric tomography study

Major depressive disorder (MDD) is recurrent, and its pathophysiology is not fully understood. Studies using electric tomography (ET) have identified abnormalities in the current density (CD) of MDD subjects in regions associated with the neurobiology of MDD, such as the anterior cingulate cortex (ACC) and medial orbitofrontal cortex (mOFC). However, little is known regarding the long-term CD changes in MDD subjects who respond to antidepressants. The aim of this study was to compare CD between healthy and MDD subjects who received 1-year open-label treatment with fluoxetine. Thirty-two-channel electroencephalograms (EEGs) were collected from 70 healthy controls and 74 MDD subjects at baseline (pre-treatment), 1 and 2weeks and 1, 2, 6, 9 and 12months. Variable-resolution ET (VARETA) was used to assess the CD between subject groups at each time point. The MDD group exhibited decreased alpha CD (αCD) in the occipital and parietal cortices, ACC, mOFC, thalamus and caudate nucleus at each time point. The αCD abnormalities persisted in the MDD subjects despite their achieving full remission. The low sub-alpha band was different between the healthy and MDD subjects. Differences in the amount of αCD between sexes and treatment outcomes were observed. Lack of a placebo arm and the loss of depressed patients to follow-up were significant limitations. The persistence of the decrease in αCD might suggest that the underlying pathophysiologic mechanisms of MDD are not corrected despite the asymptomatic state of MDD subjects, which could be significant in understanding the highly recurrent nature of MDD.

EEG artifact removal-state-of-the-art and guidelines

This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

Voxel-based dipole orientation constraints for distributed current estimation

Distributed electroencephalography source localization is a highly ill-posed problem. With measurements on the order of 10(2), and unknowns in the range of 10(4)-10(5), the range of feasible solutions is quite large. One approach to reducing ill-posedness is to intelligently reduce the number of unknowns. Restricting solutions to gray matter is one approach. A further step is to use the anatomy of each patient to identify and constrain the orientation of the dipole within each voxel. While dipole orientation constraints for cortical patch-based approaches have been proposed, to our knowledge, no solutions for full volumetric localizations have been presented. Patch techniques account for patch surface area, but place dipoles only on the surface, rather than throughout the cortex. Variability in human cortical thickness means that thicker regions of cortex will potentially contribute more to the EEG signal, and should be accounted for in modeling. Additionally, patch models require cortical surface identification techniques, which can separate them from the extensive literature on voxel-based MR image processing, and require additional adaptation to incorporate more complex information. We present a volumetric approach for computing voxel-based distributed estimates of cortical activity with constrained dipole orientations. Using a tissue thickness estimation approach, we obtain estimates of the cortical surface normal at each voxel. These let us constrain the inverse problem, and yield localizations with reduced spatial blurring and better identification of signal magnitude within the cortex. This is demonstrated for a series of simulated and experimental data using patient-specific bioelectric models.

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

Contemporary active-EEG and EEG-imaging methods show particular promise for studying the development of action planning and social-action representation in infancy and early childhood. Action-related mu suppression was measured in eleven 3-year-old children and their mothers during a 'live,' largely unscripted social interaction. High-density EEG was recorded from children and synchronized with motion-captured records of children's and mothers' hand actions, and with video recordings. Independent Component Analysis (ICA) was used to separate brain and non-brain source signals in toddlers' EEG records. EEG source dynamics were compared across three kinds of epochs: toddlers' own actions (execution), mothers' actions (observation), and between-turn intervals (no action). Mu (6-9Hz) power was suppressed in left and right somatomotor cortex during both action execution and observation, as reflected by independent components of individual children's EEG data. These mu rhythm components were accompanied by beta-harmonic (~16Hz) suppression, similar to findings from adults. The toddlers' power spectrum and scalp density projections provide converging evidence of adult-like mu-suppression features. Mu-suppression components' source locations were modeled using an age-specific 4-layer forward head model. Putative sources clustered around somatosensory cortex, near the hand/arm region. The results demonstrate that action-locked, event-related EEG dynamics can be measured, and source-resolved, from toddlers during social interactions with relatively unrestricted social behaviors.

Perigenual anterior cingulate event-related potential precedes stop signal errors

Momentary lapses in attention disrupt goal-directed behavior. Attentional lapse has been associated with increased "default-mode" network (DMN) activity. In our previous fMRI study of a stop signal task (SST), greater activation of the perigenual anterior cingulate cortex (pgACC) - an important node of the DMN - predicts stop signal errors. In event-related potential (ERP) studies, the amplitude of an error-preceding positivity (EPP) also predicts response error. However, it is not clear whether the EPP originates from DMN regions. Here, we combined high-density array EEG and an SST to examine response-locked ERPs of error preceding trials in twenty young adult participants. The results showed an EPP in go trials that preceded stop error than stop success trials. Importantly, source modeling identified the origin of the EPP in the pgACC. By employing a bootstrapping procedure, we further confirmed that pgACC rather than the dorsal ACC as the source provides a better fit to the EPP. Together, these results suggest that attentional lapse in association with EPP in the pgACC anticipates failure in response inhibition.

Three-layer-isotropic skull conductivity representation in the EEG forward problem using spherical head models

We study the influence of different conductivity models within the framework of electroencephalogram (EEG) source localization on the white matter and skull areas. Particularly, we investigate five different spherical models having either isotropic or anisotropic conductivity for both considered areas. To this end, the anisotropic finite difference reciprocity method is used for solving the EEG forward problem. We evaluate a model of a numeric skull conductivity in terms of the minimum dipole localization/orientation error. As a result, both considered models of the skull reach the lowest dipole localization error (less than 6 mm), namely: i) single anisotropic layer and ii) three isotropic layers (hard bone/spongy bone/hard bone). Additionally, two different electrode configurations (10-20 and 10-10 systems) are tested showing that the error decreases almost as much as twice for the latter one though the computational burden significantly increases.

Influence of anisotropic white matter modeling on EEG source localization

We study the influence of the anisotropic white matter within the ElectroEncephaloGraphy source localization problem. To this end, we consider three cases of the anisotropic white matter modeled in two concrete cases: by fixed or variable ratio. We extract information about highly anisotropic areas of the white matter from real Diffusion Weighted Imaging data. To validate the compared anisotropic models, we introduce the localization dipole and orientation errors. Obtained results show that the white matter model with a fixed anisotropic ratio leads to values of dipole localization error close to 1cm and may be enough in those cases avoiding localized analysis of neural brain activity. In contrast, modeling based on the anisotropic variable rate assumption becomes important in tasks regarding analysis and localization of deep sources neighboring the white matter tissue.

The steady-state visual evoked potential in vision research: A review

Periodic visual stimulation and analysis of the resulting steady-state visual evoked potentials were first introduced over 80 years ago as a means to study visual sensation and perception. From the first single-channel recording of responses to modulated light to the present use of sophisticated digital displays composed of complex visual stimuli and high-density recording arrays, steady-state methods have been applied in a broad range of scientific and applied settings.The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.

Myalgic Encephalomyelitis: Symptoms and Biomarkers

Myalgic Encephalomyelitis (ME) continues to cause significant morbidity worldwide with an estimated one million cases in the United States. Hurdles to establishing consensus to achieve accurate evaluation of patients with ME continue, fueled by poor agreement about case definitions, slow progress in development of standardized diagnostic approaches, and issues surrounding research priorities. Because there are other medical problems, such as early MS and Parkinson's Disease, which have some similar clinical presentations, it is critical to accurately diagnose ME to make a differential diagnosis. In this article, we explore and summarize advances in the physiological and neurological approaches to understanding, diagnosing, and treating ME. We identify key areas and approaches to elucidate the core and secondary symptom clusters in ME so as to provide some practical suggestions in evaluation of ME for clinicians and researchers. This review, therefore, represents a synthesis of key discussions in the literature, and has important implications for a better understanding of ME, its biological markers, and diagnostic criteria. There is a clear need for more longitudinal studies in this area with larger data sets, which correct for multiple testing.

Advances in Electrophysiological Research

Electrophysiological measures of brain function are effective tools to understand neurocognitive phenomena and sensitive indicators of pathophysiological processes associated with various clinical conditions, including alcoholism. Individuals with alcohol use disorder (AUD) and their high-risk offspring have consistently shown dysfunction in several electrophysiological measures in resting state (i.e., electroencephalogram) and during cognitive tasks (i.e., event-related potentials and event-related oscillations). Researchers have recently developed sophisticated signal-processing techniques to characterize different aspects of brain dynamics, which can aid in identifying the neural mechanisms underlying alcoholism and other related complex disorders.These quantitative measures of brain function also have been successfully used as endophenotypes to identify and help understand genes associated with AUD and related disorders. Translational research also is examining how brain electrophysiological measures potentially can be applied to diagnosis, prevention, and treatment.

Hyperscanning neuroimaging technique to reveal the "two-in-one" system in social interactions

Using a technique for measuring brain activity simultaneously from two people, known as hyperscanning, we can calculate inter-brain neural effects that appear only in interactions between individuals. Hyperscanning studies using fMRI are advantageous in that they can precisely determine the region(s) involved in inter-brain effects. However, it is almost impossible to record inter-brain effects in daily life. By contrast, hyperscanning EEG studies have high temporal resolution and could be used to capture moment-to-moment interactions. In addition, EEG instrumentation is portable and easy to wear, offering the opportunity to record inter-brain effects during daily-life interactions. However, the disadvantage of this approach is that it is difficult to localize the epicenter of the inter-brain effect. fNIRS has better temporal resolution and portability than fMRI, but has limited spatial resolution and a limited ability to record deep brain structures. Future studies should employ hyperscanning EEG-fMRI, because this approach combines the high temporal resolution of EEG with the high spatial resolution of fMRI. Hyperscanning EEG-fMRI allows us to use inter-brain effects as neuromarkers of the properties of social interactions in daily life. We also wish to emphasize the need to develop a mathematical model explaining how two brains can exhibit synchronized activity.

Topographic distribution of EEG alpha attractor correlation dimension values in wake and drowsy states in humans

Organization of resting state cortical networks is of fundamental importance for the phenomenon of awareness, which is altered in the first part of hypnagogic period (Hori stages 1-4). Our aim was to investigate the change in brain topography pattern of EEG alpha attractor correlation dimension (CD) in the period of transition from Hori stage 1 to 4. EEG of ten healthy adult individuals was recorded in the wake and drowsy states, using a 14 channel average reference montage, from which 91 bipolar channels were derived and filtered in the wider alpha (6-14 Hz) range. Sixty 1s long epochs of each state and individual were subjected to CD calculation according to the Grassberger-Procaccia method. For such a collection of signals, two embedding dimensions, d={5, 10}, and 22 time delays τ=2-23 samples were explored. Optimal values were d=10 and τ=18, where both saturation and second zero crossing of the autocorrelation function occurred. Bipolar channel CD underwent a significant decrease during the transition and showed a positive linear correlation with electrode distance, stronger in the wake individuals. Topographic distribution of bipolar channels with above median CD changed from longitudinal anterior-posterior pattern (awake) to a more diagonal pattern, with localization in posterior regions (drowsiness). Our data are in line with the literature reporting functional segregation of neuronal assemblies in anterior and posterior regions during this transition. Our results should contribute to understanding of complex reorganization of the cortical part of alpha generators during the wake/drowsy transition.

Removal of pulse artefact from EEG data recorded in MR environment at 3T. Setting of ICA parameters for marking artefactual components: application to resting-state data

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.

Epileptogenic focus localization in treatment-resistant post-traumatic epilepsy

Pharmacologically intractable post-traumatic epilepsy (PTE) is a major clinical challenge for patients with penetrating traumatic brain injury, where the risk for this condition remains very high even decades after injury. Although over 20 anti-epileptic drugs (AED) are in common use today, approximately one-third of epilepsy patients have drug-refractory seizures and even more have AED-related adverse effects which compromise life quality. Simultaneously, there have been repeated recommendations by radiologists and neuroimaging experts to incorporate localization based on electroencephalography (EEG) into the process of clinical decision making regarding PTE patients. Nevertheless, thus far, little progress has been accomplished towards the use of EEG as a reliable tool for locating epileptogenic foci prior to surgical resection. In this review, we discuss the epidemiology of pharmacologically resistant PTE, address the need for effective anti-epileptogenic treatments, and highlight recent progress in the development of noninvasive methods for the accurate localization of PTE foci for the purpose of neurosurgical intervention. These trends indicate the current emergence of promising methodologies for the noninvasive study of post-traumatic epileptogenesis and for the improved neurosurgical planning of epileptic foci resection.

Electrophysiological evidence for a specific neural correlate of musical violation expectation in primary-school children

The majority of studies on music processing in children used simple musical stimuli. Here, primary schoolchildren judged the appropriateness of musical closure in expressive polyphone music, while high-density electroencephalography was recorded. Stimuli ended either regularly or contained refined in-key harmonic transgressions at closure. The children discriminated the transgressions well above chance. Regular and transgressed endings evoked opposite scalp voltage configurations peaking around 400ms after stimulus onset with bilateral frontal negativity for regular and centro-posterior negativity (CPN) for transgressed endings. A positive correlation could be established between strength of the CPN response and rater sensitivity (d-prime). We also investigated whether the capacity to discriminate the transgressions was supported by auditory domain specific or general cognitive mechanisms, and found that working memory capacity predicted transgression discrimination. Latency and distribution of the CPN are reminiscent of the N400, typically observed in response to semantic incongruities in language. Therefore our observation is intriguing, as the CPN occurred here within an intra-musical context, without any symbols referring to the external world. Moreover, the harmonic in-key transgressions that we implemented may be considered syntactical as they transgress structural rules. Such structural incongruities in music are typically followed by an early right anterior negativity (ERAN) and an N5, but not so here. Putative contributive sources of the CPN were localized in left pre-motor, mid-posterior cingulate and superior parietal regions of the brain that can be linked to integration processing. These results suggest that, at least in children, processing of syntax and meaning may coincide in complex intra-musical contexts.

On social neuroscience methodologies and their applicability to group processes and intergroup relations

Group processes and intergroup relations are one of the most important topics examined by social psychologists. Recent advancements in social neuroscience methodologies provide valuable insight into these processes by allowing researchers to examine different psychological phenomena via neural processes that instantiate them while individuals interact with ingroup and outgroup members. This includes responses that occur outside conscious awareness or are deemed undesirable to overtly express. The purpose of this review is to provide an overview of the different social neuroscience methodologies that afford these possibilities. Specifically, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), functional near infrared spectroscopy (fNIRS), transcranial magnetic stimulation (TMS), and genetic approaches will be discussed. Each section includes a discussion of what the methodology is and how it is used to assess neural function. A secondary goal of the review is to highlight recent studies that have utilized the aforementioned tools to better understand intergroup processes and interactions. Throughout, advantages and limitations of each approach are discussed, particularly with respect to the study of group processes and intergroup relations.

LORETA EEG phase reset of the default mode network

Objectives: The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG).

Methods: The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences.

Results: Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300–350 ms and (2) 350–450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas.

Conclusions: The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a “shutter” that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: a sLORETA study

The aim of the present study was to explore the modifications of EEG power spectra and EEG connectivity of resting state (RS) condition in patients with post-traumatic stress disorder (PTSD). Seventeen patients and seventeen healthy subjects matched for age and gender were enrolled. EEG was recorded during 5min of RS. EEG analysis was conducted by means of the standardized Low Resolution Electric Tomography software (sLORETA). In power spectra analysis PTSD patients showed a widespread increase of theta activity (4.5-7.5Hz) in parietal lobes (Brodmann Area, BA 7, 4, 5, 40) and in frontal lobes (BA 6). In the connectivity analysis PTSD patients also showed increase of alpha connectivity (8-12.5Hz) between the cortical areas explored by Pz-P4 electrode. Our results could reflect the alteration of memory systems and emotional processing consistently altered in PTSD patients.

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.

Optimizing real time fMRI neurofeedback for therapeutic discovery and development

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain–behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.

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Guidelines are proposed for studies of rtfMRI neurofeedback for clinical therapeutics.

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Evidence-based guidelines are needed for clinical trials of rtfMRI neurofeedback.

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These guidelines will shape the design of future clinical trials.

Does sensorimotor cortex activity change with quadriceps femoris torque output? A human electroencephalography study

Encoding muscular force output during voluntary contractions is widely perceived to result, at least in part, from modulations in neuronal activity within the sensorimotor cortex. However the underlying electrophysiological phenomena associated with increased force output remains unclear. This study directly assessed sensorimotor cortex activity using electroencephalography (EEG) in humans performing isometric knee-extensions at a range of discrete torque levels. Fifteen healthy males (age 24 (s=5) years) completed one familiarization and one experimental trial. Participants performed a cyclic series of 60 isometric knee-extension contractions with the right leg, including 15 contractions of a 5-s duration at each of four discrete torque levels: 15%, 30%, 45% and 60% of maximal voluntary torque (MVT). Isometric knee-extension torque, quadriceps electromyography and EEG were recorded at rest and throughout all the contractions. EEG (0.5-50 Hz) was collected using a 32-channel active-electrode cap. A voxel-based low-resolution brain electromagnetic tomography (LORETA) analysis calculated cortical activation within the sensorimotor cortex (one of 27 MNI coordinates) for the entire 0.5-50-Hz range (cortical current density (CCD)), as well as for each constituent frequency band in this range (delta, theta, alpha, beta and gamma). Gamma band (30-50 Hz) cortical activity increased with contraction torque (analysis of variance [ANOVA], P=0.03). Conversely, activity within the other frequency bands was not modulated by torque (P≥0.09), nor was overall CCD (P=0.11). Peripheral neuromuscular activation (quadriceps electromyography (EMG) amplitude) demonstrated distinct increases between each torque level (P<0.01). In conclusion, sensorimotor cortical activity within the gamma band demonstrated an overall increase with contraction torque, whereas both CCD and each of the other constituent frequency bands were not modulated by increments in torque magnitude during isometric knee-extension contractions up to 60%MVT.

Cortical source analysis of high-density EEG recordings in children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited, because the composition and spatial configuration of head tissues changes dramatically over development. In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.

Head model and electrical source imaging: a study of 38 epileptic patients

Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from scalp EEG. When applied to interictal epileptiform discharges (IEDs), this technique is of great use for identifying the irritative zone in focal epilepsies. Inaccuracies in the modeling of electro-magnetic field propagation in the head (forward model) may strongly influence ESI and lead to mislocalization of IED generators. However, a systematic study on the influence of the selected head model on the localization precision of IED in a large number of patients with known focus localization has not yet been performed. We here present such a performance evaluation of different head models in a dataset of 38 epileptic patients who have undergone high-density scalp EEG, intracranial EEG and, for the majority, subsequent surgery. We compared ESI accuracy resulting from three head models: a Locally Spherical Model with Anatomical Constraints (LSMAC), a Boundary Element Model (BEM) and a Finite Element Model (FEM). All of them were computed from the individual MRI of the patient and ESI was performed on averaged IED. We found that all head models provided very similar source locations. In patients having a positive post-operative outcome, at least 74% of the source maxima were within the resection. The median distance from the source maximum to the nearest intracranial electrode showing IED was 13.2, 15.6 and 15.6 mm for LSMAC, BEM and FEM, respectively. The study demonstrates that in clinical applications, the use of highly sophisticated and difficult to implement head models is not a crucial factor for an accurate ESI.