Review on solving the inverse problem in EEG source analysis

Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

Left-Hemispheric Asymmetry for Object-Based Attention: an ERP Study

It has been shown that selective attention enhances the activity in visual regions associated with stimulus processing. The left hemisphere seems to have a prominent role when non-spatial attention is directed towards specific stimulus features (e.g., color, spatial frequency). The present electrophysiological study investigated the time course and neural correlates of object-based attention, under the assumption of left-hemispheric asymmetry. Twenty-nine right-handed participants were presented with 3D graphic images representing the shapes of different object categories (wooden dummies, chairs, structures of cubes) which lacked detail. They were instructed to press a button in response to a target stimulus indicated at the beginning of each run. The perception of non-target stimuli elicited a larger anterior N2 component, which was likely associated with motor inhibition. Conversely, target selection resulted in an enhanced selection negativity (SN) response lateralized over the left occipito-temporal regions, followed by a larger centro-parietal P300 response. These potentials were interpreted as indexing attentional selection and categorization processes, respectively. The standardized weighted low-resolution electromagnetic tomography (swLORETA) source reconstruction showed the engagement of a fronto-temporo-limbic network underlying object-based visual attention. Overall, the SN scalp distribution and relative neural generators hinted at a left-hemispheric advantage for non-spatial object-based visual attention.

A Comparison of Point and Complete Electrode Models in a Finite Difference Model of Invasive Electrode Measurements

Invasive electrophysiological measurement of brain activity is commonly employed during epilepsy surgery to provide final validation of required resection regions. These data are critical to clinical decision making, but manual expert analysis of these data can be complicated by the need to relate individual electrode measurements to specific brain regions. To improve analysis of these data with source analysis, accurate bioelectric models are needed. Given the proximity of the measurement locations to the generating cortical sources, modeling of electrodetissue interactions is particularly important for invasive measurements. Here, we evaluate the effect of a finite difference complete electrode model on the accuracy of leadfield computations for invasive electrocorticography. Our results show that in the vicinity of electrode locations, use of the simpler point electrode model produces large topographic and magnitude differences that will likely impact the accuracy of computed source localizations.

Changes in the Resting-State Cortical Oscillatory Activity 6 Months After Modified Tinnitus Retraining Therapy

Although tinnitus retraining therapy (TRT) based on Jastreboff’s classical neurophysiological model is efficacious in most patients, its effects on the cortical activity changes responsible for the improvement of tinnitus are still unclear. In this study, we compared pre- and post-TRT resting-state quantitative electroencephalography (rs-qEEG) findings to identify power changes that could explain TRT-induced improvements. Thirty-seven patients with severe tinnitus were enrolled in the study, and rs-qEEG data recorded before the initial TRT sessions and 6 months after TRT were compared. In addition, associations between the changes in qEEG and percentage improvements in Tinnitus Handicap Inventory (THI) scores and numeric rating scale (NRS) scores of tinnitus loudness and tinnitus perception were examined. The mean THI score decreased significantly 6 months after the initial TRT session. Also, significant improvements were observed 6 months after the initial TRT session compared with the pre-treatment scores in NRS loudness, distress, and perception. As compared with the pre-TRT status, post-TRT 6 months status showed significantly decreased powers in the left primary and secondary auditory cortices for the gamma frequency band. Changes in the alpha 1 frequency band power in the right insula and orbitofrontal cortex (OFC) appeared to be positively correlated with the percentage changes in NRS distress. These results suggested that TRT improved tinnitus-related distress by reducing the power of the top-down autonomic response modulator or peripheral physiological responses to emotional experiences. That is, TRT induced habituation via modulation of functional connections between the auditory system and the limbic and autonomic nervous systems. Our results confer additional basis for understanding the neurophysiological model and the newly suggested integrative model of tinnitus by De Ridder et al. (2014) in the context of the long-term efficacy of TRT.

Bayesian inverse methods for spatiotemporal characterization of gastric electrical activity from cutaneous multi-electrode recordings

Gastrointestinal (GI) problems give rise to 10 percent of initial patient visits to their physician. Although blockages and infections are easy to diagnose, more than half of GI disorders involve abnormal functioning of the GI tract, where diagnosis entails subjective symptom-based questionnaires or objective but invasive, intermittent procedures in specialized centers. Although common procedures capture motor aspects of gastric function, which do not correlate with symptoms or treatment response, recent findings with invasive electrical recordings show that spatiotemporal patterns of the gastric slow wave are associated with diagnosis, symptoms, and treatment response. We here consider developing non-invasive approaches to extract this information. Using CT scans from human subjects, we simulate normative and disordered gastric surface electrical activity along with associated abdominal activity. We employ Bayesian inference to solve the ill-posed inverse problem of estimating gastric surface activity from cutaneous recordings. We utilize a prior distribution on the spatiotemporal activity pertaining to sparsity in the number of wavefronts on the stomach surface, and smooth evolution of these wavefronts across time. We implement an efficient procedure to construct the Bayes optimal estimate and demonstrate its superiority compared to other commonly used inverse methods, for both normal and disordered gastric activity. Region-specific wave direction information is calculated and consistent with the simulated normative and disordered cases. We apply these methods to cutaneous multi-electrode recordings of two human subjects with the same clinical description of motor function, but different diagnosis of underlying cause. Our method finds statistically significant wave propagation in all stomach regions for both subjects, anterograde activity throughout for the subject with diabetic gastroparesis, and retrograde activity in some regions for the subject with idiopathic gastroparesis. These findings provide a further step towards towards non-invasive phenotyping of gastric function and indicate the long-term potential for enabling population health opportunities with objective GI assessment.

Auditory Categorization of Man-Made Sounds Versus Natural Sounds by Means of MEG Functional Brain Connectivity

Previous neuroimaging studies have shown that sounds can be discriminated due to living-related or man-made-related characteristics and involve different brain regions. However, these studies have mainly provided source space analyses, which offer simple maps of activated brain regions but do not explain how regions of a distributed system are functionally organized under a specific task. In the present study, we aimed to further examine the functional connectivity of the auditory processing pathway across different categories of non-speech sounds in healthy adults, by means of MEG. Our analyses demonstrated significant activation and interconnection differences between living and man-made object sounds, in the prefrontal areas, anterior-superior temporal gyrus (aSTG), posterior cingulate cortex (PCC), and supramarginal gyrus (SMG), occurring within 80–120 ms post-stimulus interval. Current findings replicated previous ones, in that other regions beyond the auditory cortex are involved during auditory processing. According to the functional connectivity analysis, differential brain networks across the categories exist, which proposes that sound category discrimination processing relies on distinct cortical networks, a notion that has been strongly argued in the literature also in relation to the visual system.

Frequency-tagged visual evoked responses track syllable effects in visual word recognition

The processing of syllables in visual word recognition was investigated using a novel paradigm based on steady-state visual evoked potentials (SSVEPs). French words were presented to proficient readers in a delayed naming task. Words were split into two segments, the first of which was flickered at 18.75 Hz and the second at 25 Hz. The first segment either matched (congruent condition) or did not match (incongruent condition) the first syllable. The SSVEP responses in the congruent condition showed increased power compared to the responses in the incongruent condition, providing new evidence that syllables are important sublexical units in visual word recognition and reading aloud. With respect to the neural correlates of the effect, syllables elicited an early activation of a right hemisphere network. This network is typically associated with the programming of complex motor sequences, cognitive control and timing. Subsequently, responses were obtained in left hemisphere areas related to phonological processing.

A Novel Noninvasive Approach Based on SPECT and EEG for the Location of the Epileptogenic Zone in Pharmacoresistant Non-Lesional Epilepsy

Background and objectives: The aim of this study is to propose a methodology that combines non-invasive functional modalities electroencephalography (EEG) and single photon emission computed tomography (SPECT) to estimate the location of the epileptogenic zone (EZ) for the presurgical evaluation of patients with drug-resistant non-lesional epilepsy. Materials and Methods: This methodology consists of: (i) Estimation of ictal EEG source imaging (ESI); (ii) application of the subtraction of ictal and interictal SPECT co-registered with MRI (SISCOM) methodology; and (iii) estimation of ESI but using the output of the SISCOM as a priori information for the estimation of the sources. The methodology was implemented in a case series as an example of the application of this novel approach for the presurgical evaluation. A gold standard and a coincidence analysis based on measures of sensitivity and specificity were used as a preliminary assessment of the proposed methodology to localize EZ. Results: In patients with good postoperative evolution, the estimated EZ presented a spatial coincidence with the resection site represented by high values of sensitivity and specificity. For the patient with poor postoperative evolution, the methodology showed a partial incoherence between the estimated EZ and the resection site. In cases of multifocal epilepsy, the method proposed spatially extensive epileptogenic zones. Conclusions: The results of the case series provide preliminary evidence of the methodology’s potential to epileptogenic zone localization in non-lesion drug-resistant epilepsy. The novelty of the article consists in estimating the sources of ictal EEG using SISCOM result as a prior for the inverse solution. Future studies are necessary in order to validate the described methodology. The results constitute a starting point for further studies in order to support the clinical reliability of the proposed methodology and advocate for their implementation in the presurgical evaluation of patients with intractable non-lesional epilepsy.

Power Dissipation and Surface Charge in EEG: Application to Eigenvalue Structure of Integral Operators

OBJECTIVE

To demonstrate the role of surface charge and power dissipation in the analysis of EEG measurements.

METHODS

The forward EEG problem is formulated in terms of surface charge density. Using bounds based on power dissipation, the integral equations for forward solutions are shown to satisfy bounds on their eigenvalue structure.

RESULTS

We show that two physical variables, dissipated power and the accumulated charge at interfaces, can be used in formulating the forward problem. We derive the boundary integral equations satisfied by the charge and show their connection to the integral equations for the potential that are known from other approaches. We show how the dissipated power determines bounds on the range of eigenvalues of the integral operators that appear in EEG boundary element methods. Using the eigenvalue structure, we propose a new method for the solution of the forward problem, where the integral kernels are regularized by the exclusion of eigenvectors associated to a finite range of eigenvalues. We demonstrate the method on a head model with realistic shape.

CONCLUSION

The eigenvalue analysis of the EEG forward problem is given a clear interpretation in terms of power dissipation and surface charge density.

SIGNIFICANCE

The use of these variables enhances our understanding of the structure of EEG, makes connection with other techniques and contributes to the development of new analysis algorithms.

Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods

Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however, is a challenge: the amount of available M/EEG data typically is limited, labels can be unreliable, and raw signals often are contaminated with artifacts. To overcome some of these problems, simulation frameworks have been introduced which support the development of data-driven decoding algorithms and their benchmarking. For generating artificial brain signals, however, most of the existing frameworks make strong and partially unrealistic assumptions about brain activity. This limits the generalization of results observed in the simulation to real-world scenarios. In the present contribution, we show how to overcome several shortcomings of existing simulation frameworks. We propose a versatile alternative, which allows for an objective evaluation and benchmarking of novel decoding algorithms using real neural signals. It allows to generate comparatively large datasets with labels being deterministically recoverable from the arbitrary M/EEG recordings. A novel idea to generate these labels is central to this framework: we determine a subspace of the true M/EEG recordings and utilize it to derive novel labels. These labels contain realistic information about the oscillatory activity of some underlying neural sources. For two categories of subspace-defining methods, we showcase how such labels can be obtained-either by an exclusively data-driven approach (independent component analysis-ICA), or by a method exploiting additional anatomical constraints (minimum norm estimates-MNE). We term our framework post-hoc labeling of M/EEG recordings. To support the adoption of the framework by practitioners, we have exemplified its use by benchmarking three standard decoding methods-i.e., common spatial patterns (CSP), source power-comodulation (SPoC), and convolutional neural networks (ConvNets)-wrt. Varied dataset sizes, label noise, and label variability. Source code and data are made available to the reader for facilitating the application of our post-hoc labeling framework.

Investigating the vestibular system using modern imaging techniques-A review on the available stimulation and imaging methods

The vestibular organs, located in the inner ear, sense linear and rotational acceleration of the head and its position relative to the gravitational field of the earth. These signals are essential for many fundamental skills such as the coordination of eye and head movements in the three-dimensional space or the bipedal locomotion of humans. Furthermore, the vestibular signals have been shown to contribute to higher cognitive functions such as navigation. As the main aim of the vestibular system is the sensation of motion it is a challenging system to be studied in combination with modern imaging methods. Over the last years various different methods were used for stimulating the vestibular system. These methods range from artificial approaches like galvanic or caloric vestibular stimulation to passive full body accelerations using hexapod motion platforms, or rotatory chairs. In the first section of this review we provide an overview over all methods used in vestibular stimulation in combination with imaging methods (fMRI, PET, E/MEG, fNIRS). The advantages and disadvantages of every method are discussed, and we summarize typical settings and parameters used in previous studies. In the second section the role of the four imaging techniques are discussed in the context of vestibular research and their potential strengths and interactions with the presented stimulation methods are outlined.

Wavelet Entropy-Based Inter-subject Associative Cortical Source Localization for Sensorimotor BCI

We propose event-related cortical sources estimation from subject-independent electroencephalography (EEG) recordings for motor imagery brain computer interface (BCI). By using wavelet-based maximum entropy on the mean (wMEM), task-specific EEG channels are selected to predict right hand and right foot sensorimotor tasks, employing common spatial pattern (CSP) and regularized common spatial pattern (RCSP). EEG from five healthy individuals (Dataset IVa, BCI Competition III) were evaluated by a cross-subject paradigm. Prediction performance was evaluated via a two-layer feed-forward neural network, where the classifier was trained and tested by data from two subjects independently. On average, the overall mean prediction accuracies obtained using all 118 channels are (55.98±6.53) and (71.20±5.32) in cases of CSP and RCSP, respectively, which are slightly lower than the accuracies obtained using only the selected channels, i.e., (58.95±6.90) and (71.41±6.65), respectively. The highest mean prediction accuracy achieved for a specific subject pair by using selected EEG channels was on average (90.36±5.59) and outperformed that achieved by using all available channels (86.07 ± 10.71). Spatially projected cortical sources approximated using wMEM may be useful for capturing inter-subject associative sensorimotor brain dynamics and pave the way toward an enhanced subject-independent BCI.

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons-Applications to Connectivity Studies

Electrophysiological Source Imaging (ESI) is hampered by lack of "gold standards" for model validation. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) experiments (EECoG) are useful for this purpose, especially primate models due to their flexibility and translational value for human research. Unfortunately, there is only one EECoG experiments in the public domain that we know of: the Multidimensional Recording (MDR) is based on a single monkey ( www.neurotycho.org ). The mining of this type of data is hindered by lack of specialized procedures to deal with: (1) Severe EECoG artifacts due to the experimental produces; (2) Sophisticated forward models that account for surgery induced skull defects and implanted ECoG electrode strips; (3) Reliable statistical procedures to estimate and compare source connectivity (partial correlation). We provide solutions to the processing issues just mentioned with EECoG-Comp: an open source platform ( https://github.com/Vincent-wq/EECoG-Comp ). EECoG lead fields calculated with FEM (Simbio) for MDR data are also provided and were used in other papers of this special issue. As a use case with the MDR, we show: (1) For real MDR data, 4 popular ESI methods (MNE, LCMV, eLORETA and SSBL) showed significant but moderate concordance with a usual standard, the ECoG Laplacian (standard partial [Formula: see text]); (2) In both monkey and human simulations, all ESI methods as well as Laplacian had a significant but poor correspondence with the true source connectivity. These preliminary results may stimulate the development of improved ESI connectivity estimators but require the availability of more EECoG data sets to obtain neurobiologically valid inferences.

Somatosensory Cross-Modal Reorganization in Children With Cochlear Implants

Deprived of sensory input, as in deafness, the brain tends to reorganize. Cross-modal reorganization occurs when cortices associated with deficient sensory modalities are recruited by other, intact senses for processing of the latter's sensory input. Studies have shown that this type of reorganization may affect outcomes when sensory stimulation is later introduced via intervention devices. One such device is the cochlear implant (CI). Hundreds of thousands of CIs have been fitted on people with hearing impairment worldwide, many of them children. Factors such as age of implantation have proven useful in predicting speech perception outcome with these devices in children. However, a portion of the variance in speech understanding ability remains unexplained. It is possible that the degree of cross-modal reorganization may explain additional variability in listening outcomes. Thus, the current study aimed to examine possible somatosensory cross-modal reorganization of the auditory cortices. To this end we used high density EEG to record cortical responses to vibrotactile stimuli in children with normal hearing (NH) and those with CIs. We first investigated cortical somatosensory evoked potentials (CSEP) in NH children, in order to establish normal patterns of CSEP waveform morphology and sources of cortical activity. We then compared CSEP waveforms and estimations of cortical sources between NH children and those with CIs to assess the degree of somatosensory cross-modal reorganization. Results showed that NH children showed expected patterns of CSEP and current density reconstructions, such that postcentral cortices were activated contralaterally to the side of stimulation. Participants with CIs also showed this pattern of activity. However, in addition, they showed activation of auditory cortical areas in response to somatosensory stimulation. Additionally, certain CSEP waveform components were significantly earlier in the CI group than the children with NH. These results are taken as evidence of cross-modal reorganization by the somatosensory modality in children with CIs. Speech perception in noise scores were negatively associated with CSEP waveform components latencies in the CI group, suggesting that the degree of cross-modal reorganization is related to speech perception outcomes. These findings may have implications for clinical rehabilitation in children with cochlear implants.

Electrophysiological responses to lateral shifts are not consistent with opponent-channel processing of interaural level differences

Cortical encoding of auditory space relies on two major peripheral cues, interaural time difference (ITD) and interaural level difference (ILD) of the sounds arriving at a listener's ears. In much of the precortical auditory pathway, ITD and ILD cues are processed independently, and it is assumed that cue integration is a higher order process. However, there remains debate on how ITDs and ILDs are encoded in the cortex and whether they share a common mechanism. The present study used electroencephalography (EEG) to measure evoked cortical potentials from narrowband noise stimuli with imposed binaural cue changes. Previous studies have similarly tested ITD shifts to demonstrate that neural populations broadly favor one spatial hemifield over the other, which is consistent with an opponent-channel model that computes the relative activity between broadly tuned neural populations. However, it is still a matter of debate whether the same coding scheme applies to ILDs and, if so, whether processing the two binaural cues is distributed across similar regions of the cortex. The results indicate that ITD and ILD cues have similar neural signatures with respect to the monotonic responses to shift magnitude; however, the direction of the shift did not elicit responses equally across cues. Specifically, ITD shifts evoked greater responses for outward than inward shifts, independently of the spatial hemifield of the shift, whereas ILD-shift responses were dependent on the hemifield in which the shift occurred. Active cortical structures showed only minor overlap between responses to cues, suggesting the two are not represented by the same pathway.NEW & NOTEWORTHY Interaural time differences (ITDs) and interaural level differences (ILDs) are critical to locating auditory sources in the horizontal plane. The higher order perceptual feature of auditory space is thought to be encoded together by these binaural differences, yet evidence of their integration in cortex remains elusive. Although present results show some common effects between the two cues, key differences were observed that are not consistent with an ITD-like opponent-channel process for ILD encoding.

The neural sources of N170: Understanding timing of activation in face-selective areas

The N170 ERP component has been widely identified as a face-sensitive neural marker. Despite extensive investigations conducted to examine the neural sources of N170, there are two issues in prior literature: (a) few studies used individualized anatomy as head model for the cortical source analysis of the N170, and (b) the relationship between the N170 and face-selective regions from fMRI studies is unclear. Here, we addressed these questions by presenting pictures of faces and houses to the same group of healthy adults and recording structural MRI, fMRI, and high-density ERPs in separate sessions. Source analysis based on the participant's anatomy showed that the middle and posterior fusiform gyri were the primary neural sources for the face-sensitive aspects of the N170. Source analysis based on regions of interest from the fMRI revealed that the fMRI-defined fusiform face area was the major contributor to the N170. The current study suggests that the fusiform gyrus is a major neural contributor to the N170 ERP component and provides further insights about the spatiotemporal characteristics of face processing.

Postural and Cortical Responses Following Visual Occlusion in Adults With and Without ASD

Autism is associated with differences in sensory processing and motor coordination. Evidence from electroencephalography suggests individual perturbation evoked response (PER) components represent specific aspects of postural disturbance processing; P1 reflects the detection and N1 reflects the evaluation of postural instability. Despite the importance of these cortical responses to postural control, PERs to a perturbation in adults with autism spectrum disorder (ASD) have yet to be reported. The aim was to compare PERs to visual perturbation under varied postural stability conditions in adults with and without ASD. This study is the first to report that while the assessment of postural set is intact, adults with ASD use more cortical resources to integrate and interpret visual perturbations for postural control.

Spatiotemporal Analysis of Event-related Current Density Reveals Dissociable Effects of Arousal and Valence on Emotional Picture Processing

BACKGROUND

The processing of emotional visual stimulation involves the processing of emotional and visuoperceptual information. It is not completely revealed how the valence and arousal affect these two aspects. The objective was to investigate the effects of valence and arousal on spatiotemporal characteristics of cortical information processing using distributed source imaging of event-related current density (ERCD).

METHODS

Electroencephalograms (64 channels) were recorded from 19 healthy men while presenting affective pictures. Distributed source localization analysis was adopted to obtain the spatiotemporal pattern of ERCD on cortical surface in response to emotional visual stimulation. A nonparametric cluster-based permutation test was used to find meaningful time and space without prior knowledge.

RESULTS

Significant changes of ERCD in 400-800 ms among positive, negative, and neutral emotional conditions were found in left posterior cingulate cortex (PCC) and right inferior temporal cortex (ITC). In the PCC, the stimuli with higher arousal levels showed more negative ERCD than neutral stimuli. In the ITC, the ERCD for negative stimuli was significantly more negative than those of positive and neutral ones.

CONCLUSION

Arousal and valence had strong influence on memory encoding and visual analysis at late period. The location and time showing significant change in neural activity according to arousal and valence would provide valuable information for understanding the changes of cortical function by neuropsychiatric disorders.

Microelectrode array electrical impedance tomography for fast functional imaging in the thalamus

Electrical Impedance Tomography (EIT) has the potential to be able to observe functional tomographic images of neural activity in the brain at millisecond time-scales. Prior modelling and experimental work has shown that EIT is capable of imaging impedance changes from neural depolarisation in rat somatosensory cortex. Here, we investigate the feasibility of EIT for imaging impedance changes using a stereotaxically implanted microelectrode array in the thalamus. Microelectrode array EIT was simulated using an anatomically accurate marmoset brain model. Impedance imaging was validated and detectability estimated using physiological noise recorded from the marmoset visual thalamus. The results suggest that visual-input-driven impedance changes in visual subcortical bodies within 300 μm of the implanted array could be reliably reconstructed and localised, comparable to local field potential measurements. Furthermore, we demonstrated that microelectrode array EIT could reconstruct concurrent activity in multiple subcortical bodies simultaneously.

Psychopathic traits associated with abnormal hemodynamic activity in salience and default mode networks during auditory oddball task

Psychopathy is a personality disorder accompanied by abnormalities in emotional processing and attention. Recent theoretical applications of network-based models of cognition have been used to explain the diverse range of abnormalities apparent in psychopathy. Still, the physiological basis for these abnormalities is not well understood. A significant body of work has examined psychopathy-related abnormalities in simple attention-based tasks, but these studies have largely been performed using electrocortical measures, such as event-related potentials (ERPs), and they often have been carried out among individuals with low levels of psychopathic traits. In this study, we examined neural activity during an auditory oddball task using functional magnetic resonance imaging (fMRI) during a simple auditory target detection (oddball) task among 168 incarcerated adult males, with psychopathic traits assessed via the Hare Psychopathy Checklist-Revised (PCL-R). Event-related contrasts demonstrated that the largest psychopathy-related effects were apparent between the frequent standard stimulus condition and a task-off, implicit baseline. Negative correlations with interpersonal-affective dimensions (Factor 1) of the PCL-R were apparent in regions comprising default mode and salience networks. These findings support models of psychopathy describing impaired integration across functional networks. They additionally corroborate reports which have implicated failures of efficient transition between default mode and task-positive networks. Finally, they demonstrate a neurophysiological basis for abnormal mobilization of attention and reduced engagement with stimuli that have little motivational significance among those with high psychopathic traits.

Rapid Extraction of Emotion Regularities from Complex Scenes in the Human Brain

Adaptive behavior requires the rapid extraction of behaviorally relevant information in the environment, with particular emphasis on emotional cues. However, the speed of emotional feature extraction from complex visual environments is largely undetermined. Here we use objective electrophysiological recordings in combination with frequency tagging to demonstrate that the extraction of emotional information from neutral, pleasant, or unpleasant naturalistic scenes can be completed at a presentation speed of 167 ms (i.e., 6 Hz) under high perceptual load. Emotional compared to neutral pictures evoked enhanced electrophysiological responses with distinct topographical activation patterns originating from different neural sources. Cortical facilitation in early visual cortex was also more pronounced for scenes with pleasant compared to unpleasant or neutral content, suggesting a positivity offset mechanism dominating under conditions of rapid scene processing. These results significantly advance our knowledge of complex scene processing in demonstrating rapid integrative content identification, particularly for emotional cues relevant for adaptive behavior in complex environments.

Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity

The present study explored the possibility to use Steady-State Visual Evoked Potentials (SSVEPs) as a tool to investigate the core mechanisms in visual word recognition. In particular, we investigated three benchmark effects of reading aloud: lexicality (words vs. pseudowords), frequency (high-frequency vs. low-frequency words), and orthographic familiarity ('familiar' versus 'unfamiliar' pseudowords). We found that words and pseudowords elicited robust SSVEPs. Words showed larger SSVEPs than pseudowords and high-frequency words showed larger SSVEPs than low-frequency words. SSVEPs were not sensitive to orthographic familiarity. We further localized the neural generators of the SSVEP effects. The lexicality effect was located in areas associated with early level of visual processing, i.e. in the right occipital lobe and in the right precuneus. Pseudowords produced more activation than words in left sensorimotor areas, rolandic operculum, insula, supramarginal gyrus and in the right temporal gyrus. These areas are devoted to speech processing and/or spelling-to-sound conversion. The frequency effect involved the left temporal pole and orbitofrontal cortex, areas previously implicated in semantic processing and stimulus-response associations respectively, and the right postcentral and parietal inferior gyri, possibly indicating the involvement of the right attentional network.

Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the so-called EEG "inverse problem". Once the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, linearly constrained minimum variance beamforming and 'Exact' low-resolution tomography (eLORETA). In source space, directed connectivity was estimated using multi-variate Granger causality and time-reversed Granger causality, and compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.

Multimodal Imaging of Repetitive Transcranial Magnetic Stimulation Effect on Brain Network: A Combined Electroencephalogram and Functional Magnetic Resonance Imaging Study

Repetitive transcranial magnetic stimulation (rTMS) has been increasingly used to treat many neurological and neuropsychiatric disorders. However, the clinical response is heterogeneous mainly due to our inability to predict the effect of rTMS on the human brain. Our previous investigation based on functional magnetic resonance imaging (fMRI) suggested that neuroimaging-guided navigation for rTMS could be informed by understanding connectivity patterns that correlate with treatment response. In this study, 20 individuals with a balance disorder called Mal de Debarquement Syndrome completed high-density resting-state electroencephalogram (EEG) and fMRI recordings before and after 5 days of rTMS stimulation over both dorsolateral prefrontal cortices. Based on temporal independent component analysis of source-level EEG data, large-scale electrophysiological resting-state networks were reconstructed and connectivity values in each individual were quantified both before and after treatment. Our results show that high-density, resting-state EEG can reveal connectivity changes in brain networks after rTMS that correlate with symptom changes. The connectivity changes measured by EEG were primarily superficial cortical areas that correlate with previously shown default mode network changes revealed by fMRI. Further, higher baseline EEG connectivity values in the primary visual cortex were predictive of symptom reduction after rTMS. Our findings suggest that multimodal EEG and fMRI measures of brain networks can be biomarkers that correlate with the treatment effect of rTMS. Since EEG is compatible with rTMS, real-time navigation based on an EEG neuroimaging marker may augment rTMS optimization.

Magnetic Manipulation of Blood Conductivity with Superparamagnetic Iron Oxide-Loaded Erythrocytes

The active and passive electrophysiological properties of blood and tissue have been utilized in a vast array of clinical techniques to noninvasively characterize anatomy and physiology and to diagnose a wide variety of pathologies. However, the accuracy and spatial resolution of such techniques are limited by several factors, including an ill-posed inverse problem, which determines biological parameters and signal sources from surface potentials. Here, we propose a method to noninvasively modulate tissue conductivity by aligning superparamagnetic iron oxide-loaded erythrocytes with an oscillating magnetic field. A prototype device is presented, which incorporates a three-dimensional set of Helmholtz coil pairs and fluid-channel-embedded electrode arrays. Alignment of loaded cells (∼11 mM iron) within a field of 12 mT is demonstrated, and this directed reorientation is shown to alter the conductivity of blood by ∼5 and ∼0.5% for stationary and flowing blood, respectively, within fields as weak as 6-12 mT. Focal modulation of conductivity could drastically improve numerous bioimpedance-based detection modalities.

Three dimensional innervation zone imaging in spastic muscles of stroke survivors

OBJECTIVE

The outcome of botulinum toxin (BTX) therapy of post-stroke spasticity relies largely on accuracy of BTX injection to the proximity of innervation zones (IZs). Recently developed three-dimensional IZ imaging (3DIZI) is the only technique currently available to provide 3D distributions of IZs in vivo, yet its performance has not been validated under pathological conditions.

APPROACH

The performance of 3DIZI was evaluated in the spastic biceps brachii muscles of four chronic stroke subjects. High-density surface electromyography (sEMG) and intramuscular electromyography (iEMG) were simultaneously recorded. The IZ location in the 3D space of the spastic biceps calculated using the 3DIZI technique from sEMG recordings were compared against the IZ location detected using intramuscular wires.

MAIN RESULTS

3DIZI successfully reconstructed the IZs in the 3D space of the spastic biceps of all four stroke subjects, with a localization error of 4.7  ±  2.7 mm, and specifically a depth error of 1.8  ±  0.4 mm.

SIGNIFICANCE

Results have demonstrated the robust performance of 3DIZI under pathological conditions, laying a solid foundation for clinical application of 3D source imaging in leading precise BTX injections for spasticity management.

Enhanced Data Covariance Estimation Using Weighted Combination of Multiple Gaussian Kernels for Improved M/EEG Source Localization

In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback-Leibler divergence for associating each kernel weight to its relevance. From the obtained results of validation on nonstationary and non-Gaussian brain activity (simulated and real-world EEG data), WM-MK proves that the accuracy of the source estimation raises by more effectively exploiting the measured nonlinear structures with high time and space complexity.

Electrical Neuroimaging of Music Processing in Pianists With and Without True Absolute Pitch

True absolute pitch (AP), labeling of pitches with semitone precision without a reference, is classically studied using isolated tones. However, AP is acquired and has its function within complex dynamic musical contexts. Here we examined event-related brain responses and underlying cerebral sources to endings of short expressive string quartets, investigating a homogeneous population of young highly trained pianists with half of them possessing true-AP. The pieces ended regularly or contained harmonic transgressions at closure that participants appraised. Given the millisecond precision of ERP analyses, this experimental plan allowed examining whether AP alters music processing at an early perceptual, or later cognitive level, or both, and which cerebral sources underlie differences with non-AP musicians. We also investigated the impact of AP on general auditory cognition. Remarkably, harmonic transgression sensitivity did not differ between AP and non-AP participants, and differences for auditory cognition were only marginal. The key finding of this study is the involvement of a microstate peaking around 60 ms after musical closure, characterizing AP participants. Concurring sources were estimated in secondary auditory areas, comprising the planum temporale, all transgression conditions collapsed. These results suggest that AP is not a panacea to become a proficient musician, but a rare perceptual feature.

Human non-olfactory cognition phase-locked with inhalation

Olfactory stimulus acquisition is perfectly synchronized with inhalation, which tunes neuronal ensembles for incoming information. Because olfaction is an ancient sensory system that provided a template for brain evolution, we hypothesized that this link persisted, and therefore nasal inhalations may also tune the brain for acquisition of non-olfactory information. To test this, we measured nasal airflow and electroencephalography during various non-olfactory cognitive tasks. We observed that participants spontaneously inhale at non-olfactory cognitive task onset and that such inhalations shift brain functional network architecture. Concentrating on visuospatial perception, we observed that nasal inhalation drove increased task-related brain activity in specific task-related brain regions and resulted in improved performance accuracy in the visuospatial task. Thus, mental processes with no link to olfaction are nevertheless phase-locked with nasal inhalation, consistent with the notion of an olfaction-based template in the evolution of human brain function.

Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum

INTRODUCTION

Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self-regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience, 18, 86).

METHODS

To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self-regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self-regulation with and without providing neurofeedback. Ten participants freely chose one self-regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real-time based on activity in task-related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain-activation levels in the target region.

RESULTS

During gradual self-regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self-regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time-resolved single-trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self-regulation.

CONCLUSION

Overall, these findings support that neurofeedback contributes to self-regulation through task-general regions involved in feedback and reward processing.

Attentional processes in typically developing children as revealed using brain event-related potentials and their source localization in Attention Network Test

Attention-related processes include three functional sub-components: alerting, orienting, and inhibition. We investigated these components using EEG-based, brain event-related potentials and their neuronal source activations during the Attention Network Test in typically developing school-aged children. Participants were asked to detect the swimming direction of the centre fish in a group of five fish. The target stimulus was either preceded by a cue (centre, double, or spatial) or no cue. An EEG using 128 electrodes was recorded for 83 children aged 12-13 years. RTs showed significant effects across all three sub-components of attention. Alerting and orienting (responses to double vs non-cued target stimulus and spatially vs centre-cued target stimulus, respectively) resulted in larger N1 amplitude, whereas inhibition (responses to incongruent vs congruent target stimulus) resulted in larger P3 amplitude. Neuronal source activation for the alerting effect was localized in the right anterior temporal and bilateral occipital lobes, for the orienting effect bilaterally in the occipital lobe, and for the inhibition effect in the medial prefrontal cortex and left anterior temporal lobe. Neuronal sources of ERPs revealed that sub-processes related to the attention network are different in children as compared to earlier adult fMRI studies, which was not evident from scalp ERPs.

Handling anisotropic conductivities in the EEG forward problem with a symmetric formulation

The electroencephalography (EEG) forward problem, the computation of the electric potential generated by a known electric current source configuration in the brain, is a key step of EEG source analysis. In this problem, it is often desired to model the anisotropic conductivity profiles of the skull and of the white matter. These profiles, however, cannot be handled by standard surface integral formulations and the use of volume finite elements is required. Leveraging on the representation theorem using an anisotropic fundamental solution, this paper proposes a modified symmetric formulation for solving the EEG forward problem by a surface integral equation which can take into account anisotropic conductivity profiles. A set of numerical results is presented to corroborate theoretical treatments and to show the impact of the proposed approach on both canonical and real case scenarios.

Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest

We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.

Active information storage in Parkinson's disease: a resting state fMRI study over the sensorimotor cortex

Parkinson's disease (PD), the second most frequent neurodegenerative disease, affects significantly life quality by a combination of motor and cognitive disturbances. Although it is traditionally associated with basal ganglia dysfunction, cortical alterations are also involved in disease symptoms. Our objective is to evaluate the alterations in brain dynamics in de novo and recently treated PD subjects using a nonlinear method known as Active Information Storage. In the current research, Active Information Storage (AIS) was used to study the complex dynamics in motor cortex spontaneous activity captured using resting state functional Magnetic Resonance Imaging (rs-fMRI) at early-stage in non-medicated and recently medicated PD subjects. Supplementary to AIS, the fractional Amplitude of Low Frequency Fluctuation (fALFF), which is a better-established technique of analysis of rs-fMRI signals, was also evaluated. Compared to healthy subjects, the AIS values were significantly reduced in PD patients over the analyzed motor cortex regions; differences were also found at less extent using the fALFF measure. Correlations between AIS and fALFF values showed that the measures seem to capture similar neuronal phenomena in rs-fMRI data. The highest sensitivity when detecting group differences revealed by AIS, and not captured by traditional linear approaches, suggests that this measure is a promising tool for the analysis of rs-fMRI neural data in PD.

Mental processing comparison between past and future employing a novel EEG analysis based on radiated EM field estimation

BACKGROUND

The inverse problem solution in the field of ElectroEncephaloGraphy (EEG) analysis has been addressed in the scientific literature for many decades, utilizing either mathematical techniques for measurement fitting or pure ElectroMagnetic (EM) methods involving complex head models for the prediction of the near field.

NEW METHOD

A novel radiated EM field estimation analysis scheme is proposed for EEG analysis, based on the determination of a grid of equivalent distributed EM sources with equal magnetic moments, in order to compute the extrapolated far field. A Pattern Search approach is adopted to minimize the Mean Absolute Relative Error between the EM near field created by the source grid and the EM field extracted by the measurements.

RESULTS

The application of the method on a subject's brain activity recordings in the context of "Protagoras" mental-auditory experiment demonstrates the capability of the proposed scheme to compare the subject's concentration differences between the limit of present and past versus the limit of present and future.

COMPARISON WITH EXISTING METHODS

The proposed method combines features from different existing methods, both in terms of mathematical and EM theory techniques, in order to extend their capabilities and transform the conventional analysis of EEG recordings to a far field radiation basis.

CONCLUSIONS

The treatment of the brain as an equivalent far field radiator can be a useful and promising new perspective to the established analysis of EEG recordings arising from brain activity during mental processing.

Certainty-Based Reduced Sparse Solution for Dense Array EEG Source Localization

The EEG source localization is an ill-posed problem. It involves estimation of the sources which outnumbers the number of measurements. For a given measurement at a given time all sources are not active this makes the problem as sparse inversion problem. This paper presents a new approach for dense array EEG source localization. This paper aims at reducing the solution space to only most certain sources and thereby reducing the problem of ill-posedness. This employs a two-stage method, where the first stage finds the most certain sources that are likely to produce the observed EEG by using a statistical measure of sources, the second stage solves the inverse problem by restricting the solution space to only most certain sources and their neighbors. This reduces the solution space for other source localization methods hence improvise their accuracy in localizing the active neurological sources in the brain. This method has been validated and applied to real 256 channel data and the results were analyzed.

Tracking Economic Value of Products in Natural Settings: A Wireless EEG Study

Economic decision making refers to the process of individuals translating their preference into subjective value (SV). Little is known about the dynamics of the neural processes that underpin this form of value-based decision making and no studies have investigated these processes outside of controlled laboratory settings. The current study investigated the spatio-temporal dynamics that accompany economic valuation of products using mobile electroencephalography (EEG) and eye tracking techniques. Participants viewed and rated images of household products in a gallery setting while EEG and eye tracking data were collected wirelessly. A Becker-DeGroot-Marschak (BDM) auction task was subsequently used to quantify the individual's willingness to pay (WTP) for each product. WTP was used to classify products into low, low medium, high medium and high economic value conditions. Eye movement related potentials (EMRP) were examined, and independent component analysis (ICA) was used to separate sources of activity from grand averaged EEG data. Four independent components (ICs) of EMRPs were modulated by WTP (i.e., SV) in the latency range of 150-250 ms. Of the four value-sensitive ICs, one IC displayed enhanced amplitude for all value conditions excluding low value, and another IC presented enhanced amplitude for low value products only. The remaining two value-sensitive ICs resolved inter-mediate levels of SV. Our study quantified, for the first time, the neural processes involved in economic value based decisions in a natural setting. Results suggest that multiple spatio-temporal brain activation patterns mediate the attention and aversion of products which could reflect an early valuation system. The EMRP parietal P200 component could reflect an attention allocation mechanism that separates the lowest-value products (IC7) from products of all other value (IC4), suggesting that low-value items are categorized early on as being aversive. While none of the ICs showed linear amplitude changes that parallel SV's of products, results suggest that a combination of multiple components may sub-serve a fine-grained resolution of the SV of products.

Semantic Features Reveal Different Networks During Word Processing: An EEG Source Localization Study

The neural principles behind semantic category representation are still under debate. Dominant theories mostly focus on distinguishing concrete from abstract concepts but, in such theories, divisions into categories of concrete concepts are more developed than for their abstract counterparts. An encompassing theory on semantic category representation could be within reach when charting the semantic attributes that are capable of describing both concept types. A good candidate are the three semantic dimensions defined by Osgood (potency, valence, arousal). However, to show to what extent they affect semantic processing, specific neuroimaging tools are required. Electroencephalography (EEG) is on par with the temporal resolution of cognitive behavior and source reconstruction. Using high-density set-ups, it is able to yield a spatial resolution in the scale of millimeters, sufficient to identify anatomical brain parcellations that could differentially contribute to semantic category representation. Cognitive neuroscientists traditionally focus on scalp domain analysis and turn to source reconstruction when an effect in the scalp domain has been detected. Traditional methods will potentially miss out on the fine-grained effects of semantic features as they are possibly obscured by the mixing of source activity due to volume conduction. For this reason, we have developed a mass-univariate analysis in the source domain using a mixed linear effect model. Our analyses reveal distinct networks of sources for different semantic features that are active during different stages of lexico-semantic processing of single words. With our method we identified differences in the spatio-temporal activation patterns of abstract and concrete words, high and low potency words, high and low valence words, and high and low arousal words, and in this way shed light on how word categories are represented in the brain.

Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.

Intracortical Causal Information Flow of Oscillatory Activity (Effective Connectivity) at the Sleep Onset Transition

We investigated the sleep onset transition in humans from an effective connectivity perspective in a baseline condition (approx. 16 h of wakefulness) and after sleep deprivation (40 h of sustained wakefulness). Using EEG recordings (27 derivations), source localization (LORETA) allowed us to reconstruct the underlying patterns of neuronal activity in various brain regions, e.g., the default mode network (DMN), dorsolateral prefrontal cortex and hippocampus, which were defined as regions of interest (ROI). We applied isolated effective coherence (iCOH) to assess effective connectivity patterns at the sleep onset transition [2 min prior to and 10 min after sleep onset (first occurrence of stage 2)]. ICOH reveals directionality aspects and resolves the spectral characteristics of information flow in a given network of ROIs. We observed an anterior-posterior decoupling of the DMN, and moreover, a prominent role of the posterior cingulate cortex guiding the process of the sleep onset transition, particularly, by transmitting information in the low frequency range (delta and theta bands) to other nodes of DMN (including the hippocampus). In addition, the midcingulate cortex appeared as a major cortical relay station for spindle synchronization (originating from the thalamus; sigma activity). The inclusion of hippocampus indicated that this region might be functionally involved in sigma synchronization observed in the cortex after sleep onset. Furthermore, under conditions of increased homeostatic pressure, we hypothesize that an anterior-posterior decoupling of the DMN occurred at a faster rate compared to baseline overall indicating weakened connectivity strength within the DMN. Finally, we also demonstrated that cortico-cortical spindle synchronization was less effective after sleep deprivation than in baseline, thus, reflecting the reduction of spindles under increased sleep pressure.

Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.

The inferior occipital gyrus is a major cortical source of the face-evoked N170: Evidence from simultaneous scalp and intracerebral human recordings

The sudden onset of a face image leads to a prominent face-selective response in human scalp electroencephalographic (EEG) recordings, peaking 170 ms after stimulus onset at occipito-temporal (OT) scalp sites: the N170 (or M170 in magnetoencephalography). According to a widely held view, the main cortical source of the N170 lies in the fusiform gyrus (FG), whereas the posteriorly located inferior occipital gyrus (IOG) would rather generate earlier face-selective responses. Here, we report neural responses to upright and inverted faces recorded in a unique patient using multicontact intracerebral electrodes implanted in the right IOG and in the OT sulcus above the right lateral FG (LFG). Simultaneous EEG recordings on the scalp identified the N170 over the right OT scalp region. The latency and amplitude of this scalp N170 were correlated at the single-trial level with the N170 recorded in the lateral IOG, close to the scalp lateral occipital surface. In addition, a positive component maximal around the latency of the N170 (a P170) was prominent above the internal LFG, whereas this region typically generates an N170 (or "N200") over its external/ventral surface. This suggests that electrophysiological responses in the LFG manifest as an equivalent dipole oriented mostly along the vertical axis with likely minimal projection to the lateral OT scalp region. Altogether, these observations provide evidence that the IOG is a major cortical generator of the face-selective scalp N170, qualifying the potential contribution of the FG and questioning a strict serial spatiotemporal organization of the human cortical face network.

A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder

Imagery of facial expressions in Autism Spectrum Disorder (ASD) is likely impaired but has been very difficult to capture at a neurophysiological level. We developed an approach that allowed to directly link observation of emotional expressions and imagery in ASD, and to derive biomarkers that are able to classify abnormal imagery in ASD. To provide a handle between perception and action imagery cycles it is important to use visual stimuli exploring the dynamical nature of emotion representation. We conducted a case-control study providing a link between both visualization and mental imagery of dynamic facial expressions and investigated source responses to pure face-expression contrasts. We were able to replicate the same highly group discriminative neural signatures during action observation (dynamical face expressions) and imagery, in the precuneus. Larger activation in regions involved in imagery for the ASD group suggests that this effect is compensatory. We conducted a machine learning procedure to automatically identify these group differences, based on the EEG activity during mental imagery of facial expressions. We compared two classifiers and achieved an accuracy of 81% using 15 features (both linear and non-linear) of the signal from theta, high-beta and gamma bands extracted from right-parietal locations (matching the precuneus region), further confirming the findings regarding standard statistical analysis. This robust classification of signals resulting from imagery of dynamical expressions in ASD is surprising because it far and significantly exceeds the good classification already achieved with observation of neutral face expressions (74%). This novel neural correlate of emotional imagery in autism could potentially serve as a clinical interventional target for studies designed to improve facial expression recognition, or at least as an intervention biomarker.

Brain Responses to Emotional Faces in Natural Settings: A Wireless Mobile EEG Recording Study

The detection of a human face in a visual field and correct reading of emotional expression of faces are important elements in everyday social interactions, decision making and emotional responses. Although brain correlates of face processing have been established in previous fMRI and electroencephalography (EEG)/MEG studies, little is known about how the brain representation of faces and emotional expressions of faces in freely moving humans. The present study aimed to detect brain electrical potentials that occur during the viewing of human faces in natural settings. 64-channel wireless EEG and eye-tracking data were recorded in 19 participants while they moved in a mock art gallery and stopped at times to evaluate pictures hung on the walls. Positive, negative and neutral valence pictures of objects and human faces were displayed. The time instants in which pictures first occurred in the visual field were identified in eye-tracking data and used to reconstruct the triggers in continuous EEG data after synchronizing the time axes of the EEG and eye-tracking device. EEG data showed a clear face-related event-related potential (ERP) in the latency interval ranging from 165 to 210 ms (N170); this component was not seen whilst participants were viewing non-living objects. The face ERP component was stronger during viewing disgusted compared to neutral faces. Source dipole analysis revealed an equivalent current dipole in the right fusiform gyrus (BA37) accounting for N170 potential. Our study demonstrates for the first time the possibility of recording brain responses to human faces and emotional expressions in natural settings. This finding opens new possibilities for clinical, developmental, social, forensic, or marketing research in which information about face processing is of importance.

A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media

Accurate source localization of electroencephalographic (EEG) signals requires detailed information about the geometry and physical properties of head tissues. Indeed, these strongly influence the propagation of neural activity from the brain to the sensors. Finite difference methods (FDMs) are head modelling approaches relying on volumetric data information, which can be directly obtained using magnetic resonance (MR) imaging. The specific goal of this study is to develop a computationally efficient FDM solution that can flexibly integrate voxel-wise conductivity and anisotropy information. Given the high computational complexity of FDMs, we pay particular attention to attain a very low numerical error, as evaluated using exact analytical solutions for spherical volume conductor models. We then demonstrate the computational efficiency of our FDM numerical solver, by comparing it with alternative solutions. Finally, we apply the developed head modelling tool to high-resolution MR images from a real experimental subject, to demonstrate the potential added value of incorporating detailed voxel-wise conductivity and anisotropy information. Our results clearly show that the developed FDM can contribute to a more precise head modelling, and therefore to a more reliable use of EEG as a brain imaging tool.

Source-Informed Segmentation: A Data-Driven Approach for the Temporal Segmentation of EEG

GOAL

Understanding the dynamics of brain function through non-invasive monitoring techniques requires the development of computational methods that can deal with the non-stationary properties of recorded activities. As a solution to this problem, a new data-driven segmentation method for recordings obtained through electroencephalography (EEG) is presented.

METHODS

The proposed method utilizes singular value decomposition (SVD) to identify the time intervals in the EEG recordings during which the spatial distribution of clusters of active cortical neurons remains quasi-stationary. Theoretical analysis shows that the spatial locality features of these clusters can be, asymptotically, captured by the most significant left singular subspace of the EEG data. A reference/sliding window approach is employed to dynamically extract this feature subspace, and the running projection error is monitored for significant changes using Kolmogorov-Smirnov test.

RESULTS

Simulation results, for a wide range of possible scenarios regarding the spatial distribution of active cortical neurons, show that the algorithm is successful in accurately detecting the segmental structure of the simulated EEG data. The algorithm is also applied to experimental EEG recordings of a modified visual oddball task. Results identify a unique sequence of dynamic patterns in the event-related potential (ERP) response to each of the three involved stimuli.

CONCLUSION

The proposed method, without using source localization methods or scalp topographical maps, is able to identify intervals of quasi-stationarity in the EEG recordings.

SIGNIFICANCE

The proposed segmentation technique can offer new insights on the dynamics of functional organization of the brain in action.

simBCI - A framework for studying BCI methods by simulated EEG

Brain-Computer Interface (BCI) methods are commonly studied using Electroencephalogram (EEG) data recorded from human experiments. For understanding and developing BCI signal processing techniques real data is costly to obtain and its composition is apriori unknown. The brain mechanisms generating the EEG are not directly observable and their states cannot be uniquely identified from the EEG. Subsequently, we do not have generative ground truth for real data. In this paper we propose a novel convenience framework called simBCI to alleviate testing and studying BCI signal processing methods in simulated, controlled conditions. The framework can be used to generate artificial BCI data and to test classification pipelines with such data. Models and parameters on both data generation and the signal processing side can be iterated over to examine the interplay of different combinations. The framework provides the first time open source implementations of several models and methods. We invite researchers to insert more advanced models. The proposed system does not intend to replace human experiments. Instead, it can be used to discover hypotheses, study algorithms, to educate about BCI, and to debug signal processing pipelines of other BCI systems. The proposed framework is modular, extensible and freely available as open source1. It currently requires Matlab.