Objectives: Although many studies in epilepsy have examined the synaptic mechanisms constituting the basis for most of the current principles of brain activity, relatively less studies have tried to characterize changes in the cellular environment that might predispose a network to pathologic synchronization.

Methods: In this study, near-infrared optical imaging was used with ECoG and EEG to investigate variations in the optical properties of cortical tissue directly associated with neuronal activity in 15 rats and 3 human epileptic patients. Time-frequency analysis was also used to track variations of (de)synchronization concomitantly with changes in optical signals during IES.

Results: Changes in Fast optical signals (FOS) occurred 320 msec before to 370 msec after the IES peak. These changes started before any changes in ECoG signal. In addition, time-frequency domain ECoG revealed an alternating decrease-increase-decrease in the ECoG spectral power (pointing to desynchronization-synchronization-desynchronization), which occurred concomitantly with an increase-decrease-increase in relative optical signal (pointing to shrinking-swelling-shrinking of the neuronal assembly) during the IES.

Discussion: These relationships between electrical and optical changes highlights the complexity of the interplay between the neuronal network activity and its environment around an IES.

Conclusions: These changes in the neuronal environment around IESs raise new questions about the mechanisms that provides the suitable conditions for the neuronal synchronization during IESs.

Significance: The multimodal-multiscale FOS-ECoG approach opens new avenues to better analyze the mechanisms of neuronal synchronization in the pathologic epileptic brain, which is applicable in clinic.

The interpretation of EEG is improved using source reconstruction. Accurate reconstructions necessitate anatomical models and electrode coregistration. The golden standard consists of anatomical MRIs and an electromagnetic digitizer for the electrodes. These are costly, require considerable time, and are not always feasible. In this study we investigated an 800 euro optical 3-D scanner as an alternative.

We scanned 49 subjects with MRI, Polhemus digitizer and a 3-D scanner. We created volume conduction models from this and from template models. We used these to compute the EEG scalp distribution for sources distributed over the cortical sheet. We compared these to evaluate the difference between golden standard individual models, individualized template models and a common template model.

With on average 2 minutes lab-time, the 3D scan procedure is considerably faster. The quality of the electrode model is significantly better than a common electrode model, although the magnetic digitizer remains more accurate. The quality of the head model is not significantly different than a common template model. The quality of individual MRI-based head models is not reached.

Our model comparisons show a strong improvement if individual electrode positions are considered. Optical 3D scanners are a cost and time efficient alternative for recording these. The difference between individualized template head models and a common template is not significant. The golden standard is not to be replaced where applicable, but optical 3-D scanner based electrode models are better than template electrodes. Hence we recommend using an optical 3-D scanner to improve source reconstruction.

High-density-EEG is a powerful tool to estimate brain connectivity, but the choice of parameters, i.e., data pre-processing steps, model order p of the multivariate-autoregressive (MVAR) model, number of samples and level of signal-to-noise-ratio (SNR), in the connectivity pipeline is fundamental to avoid spurious connectivity. We provide a simulation framework to test the influence of the choice of the model order p in the estimation of connectivity based on information partial directed coherence (iPDC). The time-course of four sources J(t) was reconstructed imposing the connectivity structure by manipulating the autoregressive matrices A_p of the following equation: J(t)=∑_(p=1:5) A_p J(t-p)+w(t) where w(t) is zero-mean gaussian noise and SNR is equal to 5. Then, iPDC was estimated from 100 data sets varying p in the range [2 20]. We also simulated 100 data sets of gaussian noise to compute iPDC thresholds. We evaluated the performance in connectivity estimation by computing sensitivity, specificity and accuracy from iPDC results. The true connections were detected in a narrow range order of p=[4 9] obtaining an average accuracy in the interval [0.995 0.999]. False positive connections appeared both for orders minor to 4 and major to 9 with a quickly deterioration of accuracy results [up to 20%]. In this work, we proved how much the choice of p order matters in the connectivity estimation. Future developments of this work will be simulations involving interacting sources at different frequency range to test both the different connectivity methods and the effects of the other parameters in the connectivity pipeline.

Model individualisation is a key factor to increase the accuracy in the EEG forward problem (EEG-FP), and consequently in source localisation. To this end, there exist methods accepted in the community for acquiring the necessary data to make these computational models as personalised as possible. However, there is one factor that is generally neglected in the model individualisation process: the electrical conductivity. Although few methodologies exist to deal with this issue, they usually rely on iterative solutions in which the complete lead-field matrix is computed (i.e. refined) in each step. This makes such methods computationally expensive, for which the convergence could take several hours or days even in simplistic scenarios.

In this work, we present a solution to this problem by applying a reduced order methodology to the dual version of the EEG-FP. This technique exploits the affine dependence of the stiffness matrix and load vector with respect to the electrical conductivities to speed-up the calculation of lead-field matrices. This is done by computing a set of suitable, problem-dependent basis functions to express any solution of the EEG-FP, considering arbitrary electrical conductivities, up to a certain error. The EEG-FP is then solved in the reduced space. Using a five-layered model, we found that approximations with a relative error of 10^-5 with respect to the high-fidelity solutions are obtained considering less than 20 basis functions, allowing to compute accurate lead-field matrices in less than a second. The convergence of the method and other theoretical and practical aspects are also discussed.

The characterisation of electrical sources of brain activity by means of EEG is fundamental for understanding brain processes. The accuracy with which we perform such analysis is limited by the models used to represent the sources of electrical activity (among other factors). In this regard, the dipolar model is generally adopted. Although useful, it was shown to be limited to represent sources with non-negligible spatial extent. To increase the reliability, Jerbi et al. (2004) proposed to use multipolar source components, which they show to increase the accuracy of the source estimation process using MEG recordings. Even though this framework showed great improvements with respect to the standard dipolar models, it was presented for MEG only and considering spherical head models. This limits the applicability of the technique to individualised models, for which numerical methodologies need development.

In this work, we present a full subtraction version of the finite element method for solving the EEG forward problem considering multipolar source models. This framework allows to perform computational simulations of electrical brain activity utilising multipolar sources in anisotropic and personalised head models. In particular, we analysed the cases of dipolar and quadrupolar source components. Numerical solutions are compared with analytical formulas in a multi-layered spherical model with anisotropic electrical conductivity. These formulas are available in the case of dipolar sources, and here derived for quadrupolar components. Results in idealised and realistic head models show the reliability of the method for further multipolar characterisation of electrical brain sources.

We present a subtraction approach for solving the EEG forward problem (EEG-FP) considering the complete electrode model (CEM) and multipolar source models. Differently from other approaches, we deal with the singularity in the source term by splitting the electric potential into a singularity potential, for which analytical expressions are available, and a singularity-free correction potential that is approximated using the finite element method (FEM). This approach allows the use of standard finite elements for solving the EEG-FP in personalised head models with anisotropic electrical conductivity field. Moreover, the subtraction method is used to show the existence and uniqueness of the solution, extending the results previously obtained for dipolar sources and the point electrode model (Wolters et al., 2007). The methodology here presented consists in an alternative to the approach proposed by Pursiainen et al. (2017) based on Whitney basis functions, for which the simulation of sources in arbitrary locations would require extra efforts. Numerical experiments are shown considering the full and projected FEM versions.

Several methods have been proposed for the inversion of the magnetoencephalography (MEG) and the Electroencephalography (EEG) problems, i.e. the localization of the active brain regions from the measured M-EEG signals, and all of them require two main ingredients: the forward M-EEG model and the source space. The forward model relates the electric potential/magnetic field at sensors' positions produced by a known neuroelectric current distribution while the source space reflects our a priori knowledge on the current flowing inside the brain. An accurate source space, approximating the cortical surface, consists of thousand of points and this large number of possible sources is responsible for the high computational cost for the computation of the forward model first and then of the solution of the inverse problem.

Here, we propose the RAndoM Sampling invErSion method (RAMSES) which uses a sampling procedure to significantly reduce the dimension of the source space. The accuracy of the method in localizing brain activity is investigated using both synthetic and real MEG data. We employ three different model for the forward solution -a BEM model, a spherical model and a less sophisticated method based on the Biot-Savart operator- and compare the results of RAMSES when employing different methods to solve the inverse problem -a simple least square (LSQR) Matlab routine, dynamic statistical parametric map (dSPM), weighted Minimum Norm Estimates (wMNE).

The tests show that the random sampling procedure does not compromise the capability of localizing brain activity.

Introduction

Accurate segmentation of an individual human head in conjunction with the resulting volume conductor model has been shown to improve the results of EEG source analysis. However, comparison of some state-of-the-art segmentation techniques and their effectiveness in source analysis, namely Multi-Atlas and Convolutional Neural Networks (CNN) segmentation, is lacking. We present a comparison between these techniques to segment five tissues using ground-truth data from BrainWeb.

Method

For the Multi-Atlas method, a 2-step registration process with affine and non-rigid registration along with a classification step involving label-fusion from registered atlases was performed, using weighted local similarity. For CNN, a 3D multi-path pipeline with an 8-layer architecture, a kernel of size 3x3x3, and receptive-field of size 25x25x25, was designed. An extra pathway for sub-sampled images was used to exploit spatial cues. We calculated the lead-field matrix for each segmentation result using 2000 sources and 97 scalp electrodes.

Results

Dice scores for cerebral spinal fluid (CSF), grey matter (GM), white matter (WM), muscle/skin and skull were 0.85, 0.94, 0.94, 0.96, 0.91 and 0.78, 0.82, 0.83, 0.94, 0.88 for CNN and multi-atlas respectively. Mean values of the relative and magnitude difference measure for computing lead-field matrices were 0.1071, 0.9808 and 0.2089, 0.9544 for CNN and multi-atlas respectively.

Conclusion

CNN outperforms multi-atlas method in segmenting CSF, GM, and WM, primarily due to the tissue’s structural variability across subjects which affects consensus-based algorithms. The CNN lead-field matrix values were closest to those of the ground-truth; the effect on source analysis will be investigated further.

Localizing the seizure onset zone (SOZ) from ictal EEGs is a difficult process since different physiological and non-physiological noises occurring during the seizure. Therefore, ictal EEG source imaging (ESI) is currently not used in the presurgical evaluation for epilepsy patients. To localize the SOZ, we propose a methodology coupling decomposition with ESI.

We decomposed 68 artifact-free ictal epochs recorded in 18 patients having Engel Class I surgical outcome. The decomposition techniques were independent component analysis (ICA), canonical polyadic decomposition (CPD) and block term decomposition (BTD). Based on the clinical reports of ictal discharges, ictal component was manually identified. Then, ictal source was generated from the maximum of sLORETA reconstruction. To investigate the decomposition effects on localization, ESI was also applied on the aforementioned epochs without decomposition. For both approaches, the distance between the estimated SOZ and the border of resected zone (RZ) was calculated.

By considering the estimated SOZ inside the RZ (or within 20mm from the RZ) ESI alone was correct in 13%(34%) of the seizures, while it increased to 18%(43%), 22%(38%) and 26%(46%) by combining ESI with BTD, CPD, and ICA, respectively. Without decomposition, 39% of patients had more than 2/3 of their seizures localized within 20mm from the RZ. By including decomposition, this increased to 56%, 61% and 78% for BTD,CPD and ICA, respectively.

We showed that decomposing the ictal EEG before applying ESI is beneficial to localize the SOZ. The technique is promising, but currently the accuracy still needs to be improved for clinical application.

Our objective was to determine if there are any distinguishable phase clustering patterns present before, during and after the onset of epileptic spikes. The phase clustering activity was derived from 256-channel high density (dEEG) data of an adult patient who had epileptic activity in the left central and parietal areas as determined from invasive subdural recordings. The analysis was performed in the ripple band ( 80-150 Hz) and in the low gamma band (30-50 Hz). The dEEG data was filtered in the appropriate band. Hilbert transform was applied to compute the analytic phase and it was unwrapped. Spatiotemporal contour plots of the unwrapped analytic phase with 1.0 ms intervals were constructed using a montage layout of 256 electrode positions. These plots exhibited dynamic formation of phase cones which are similar to bubbles in boiling water. Several criterions were applied to select stable phase cone patterns. These included: phase frequency was within the temporal band, sign of spatial gradient did not change for at least 3 time samples and the frame velocity should be within the range of propagation velocities of cortical axons. Analysis was performed during ±5 seconds from the location of spike with a resolution of one sec. Stable phase cluster patterns were higher in the seizure area as compared with the nearby surrounding brain areas. Spatiotemporal oscillatory patterns were also visible during ±5 sec period. These results show the feasibility to localize epileptic spikes and also to study the dynamics of cortical neurons.

Being given pointwise measurements of the electric potential taken by electrodes on part of the scalp, the EEG (electroencephalography) inverse problem consists in estimating current sources within the brain that account for this activity.

A model for the behaviour of the potential rests on Maxwell equation in the quasi-static case, under the form of a Poisson-Laplace equation.

We will describe our approach for solving the inverse problem in spherical geometry, for piecewise constant electric conductivity values, and pointwise dipolar source terms.

It relies on consecutive steps, consisting of (see [CLMP]):

(i) singular value decomposition, in order to separate the time independant activities;

(ii) spherical harmonics expansion, for data transmission from scalp to cortex ("cortical mapping") using best constrained approximation;

(iii) best rational approximation on 2D slices in order to compute singularities in circular sections;

(iv) clustering of these singularities in order to localize the sources, dipole fitting, moment computation.

The algorithm has been encoded in the software FindSources3D (see http://www-sop.inria.fr/apics/FindSources3D/). Numerical simulations will be presented.

[CLMP] M. Clerc, J. Leblond, J.-P. Marmorat, T. Papadopoulo, Source localization in EEG using rational approximation on plane sections, Inverse Problems, 28, 055018, 2012.

Magnetoencephalography (MEG) and Electroencephalography (EEG) are widely used systems to study the electrophysiological dynamics of the human brain. MEG/EEG are able to provide data streams with millisecond-temporal resolution. This makes them ideal candidates for real-time monitoring and processing of neuronal activity. In conjunction with neurofeedback sce-narios, real-time MEG/EEG data processing allows the adaption of the experiment to the subject’s reaction, creating a whole set of new options and possible experiments. By further advancing the open source MNE-CPP project we want to provide a state of the art framework, which offers tools to develop novel real-time processing methods and to build standalone applications for electrophysiological data processing.

The MNE-CPP project is structured into libraries, which guarantee a modular and easily extendable architecture. MNE-CPP hosts libraries to support the Fiff and FreeSurfer data format as well as source estimation and 2D/3D displaying routines. We have kept the external dependencies to a minimum, namely Qt5 and Eigen. We were able to build several MNE-CPP based soft-ware applications for real-time (MNE Scan) as well as offline (MNE Browse) data processing. Next to usage in research envi-ronments, MNE-CPP applications can also function in clinical environments with regulatory requirements (BabyMEG). We recently added new EEG device support (BrainAmp, EGI) to MNE Scan and further improved overall 3D visualization, i.e. by including real-time smoothing of cortical activity. Furthermore, we added tools to track head motion in MEG scenarios via HPI coils. The new tracking tools provide 3D visualization of the subject’s head relative to the sensors in real-time.

The surgical treatment of patients with complex epilepsies is changing from open, invasive surgery towards minimally invasive, image guided treatment. Brain imaging is becoming more and more important for preoperatively identifying the region of the brain which is responsible for the epilepsy of the patient. The ultimate aim is to provide the neurosurgeon with a clear, intuitive image of the targeted epileptogenic region, to enable a resection which renders the patient seizure free while avoiding damage to the cortex.

A software product is developed for the visualization of multi-modal brain images and analysis results of non-invasive and invasive epilepsy recordings. The software is designed for three main tasks. At the preparation step the data is collected, pre-processed and saved together with the patient info in the application database. During the exploration step, different aspects of the data can be investigated and at the final step of visualization, individual images can be combined in multi-modal 2D- and 3D-MRI viewports. The software contains several pre-programmed sequences for creating multi-modal visualizations used to identify epileptic tissue versus functional areas, like visualizations of inverse solutions of high density EEG and MEG, EEG informed functional MRI visualizations and functional Near-infrared Spectroscopy projections. The end result is a software tool that supports the decision process involving the preoperative planning of surgical resections of epileptic tissue, which is less time consuming and yields a more precise delineation of epileptic tissue with a higher success rate in case of surgery.

OpenMEEG implements boundary element solutions for simulating electromagnetic fields in the quasistatic regime. Originally designed for the forward EEG and MEG problems (MEEG collectively), it has also been applied to compute forward solutions for ECoG, for implanted EEG, for cochlear implant stimulation, for tDCS and other electrostimulation settings. In this presentation we detail the features of the latest release of OpenMEEG.

Geometrical models have now been extended to handle non-nested geometries: the various domains must still have a constant isotropic conductivity but they need no longer be nested inside one another. OpenMEEG supports CGAL meshing tools (allowing to remesh or decimate existing meshes, or to mesh a levelset). Linear algebra packages MKL and OpenBLAS are now supported on all platforms (Linux, MacOS and Windows). Interface with Python is improved with wrappers that allow to pass data to Python without memory copies. Gifti and VTK mesh formats are supported, and some visualization tools are provided (VTK or mayavi). More tests have been included, and last but not least, some bugs fixed.

The effect of the choice of the inverse method on the cortico-cortical coupling analysis has been largely overlooked in the literature. Here, we set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, we created thousands of randomly located pairs of sources and varied their inter- and intra-source correlation strength, source size and spatial configuration. Then, we used the simulated pairs of sources to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, we reconstructed the sources using L2-Minimum-Norm Estimate (MNE), Linearly Constrained Minimum Variance (LCMV) beamforming, and Dynamic Imaging of Coherent Sources (DICS) beamforming; and calculated source level power and coherence maps. We evaluated the performance of the methods using the Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions of interacting point-like sources; but MNE provides better connectivity estimation than beamformers of interacting extended cortical patches, if each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each cortical patch is simulated with partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. Our conclusions can guide method selection and help improve data interpretation regarding MEG connectivity estimation.

The bioelectromagnetic inverse problem cannot be solved based on EEG/MEG data alone and requires additional assumptions. In linear reconstruction methods, spatial smoothness is often used as an additional constraint. This is equivalent to the prior assumption of a particular source covariance structure. Recent publications (Knösche et al., NeuroImage 2013) have suggested altering this spatial correlation structure such that it reflects available knowledge on the functio-anatomical organization of the brain. In particular, it is possible to derive borders between different brain areas from various types of brain images. This allows assuming that sources located within the same area exhibit similar activity and sources in different areas are mutually uncorrelated. We present a technique based on the well-known LORETA method (Pascual-Marqui et al., Int. J. Psychophysiol. 1994), which is capable of incorporating such function-anatomical priors. We show that our method embodies the intended prior knowledge in the prior source covariance in an unbiased way. We present Monte-Carlo simulations, which provide a systematic evaluation of how the prior knowledge influences the estimate of different linear inverse procedures. The study answers questions like “What happens if the course of boundaries is uncertain?”, “What if our knowledge on functional areas is limited to certain cortical regions?” and “Can prior knowledge improve source localization?”. Besides presenting answers to these questions we demonstrate our method to localize auditory N100 activity from experimental EEG/MEG data. The results clearly suggest that spatially informed linear inverse methods provide very plausible reconstruction results.

Redundancy in high-resolution electroencephalography (EEG) may cause numerical instability and inaccuracies in source reconstruction. Dimensionality reduction via spatial projection, which is based on singular value decomposition (SVD), suppresses this redundancy while largely preserving the benefits of improved head coverage and higher spatial resolution for surface-EEG. The authors have successfully used spatial projection in conjunction with the spatiotemporal Kalman filter (STKF) to alleviate this problem. The choice of the optimal threshold value for spatial projection, however, has not yet been investigated. This proof-of-principle work uses different threshold values for spatial projection and studies the effect thereof on the accuracy and spatial resolution of source reconstruction via STKF and its generalized variant, the regional spatiaotemporal Kalman filter (RSTKF). RSTKF allows for region-specific dynamics in the state-space model to approximate the brain’s modularity. In this work we use 256-electrode EEG recordings from a patient with bilateral temporal lobe epilepsy caused by hippocampal sclerosis. The patient was operated in the right temporal lobe and is now seizure-free. The reconstructed source will be compared to the resected volume from the post-operative magnetic resonance image (MRI). First results show a reduction in spatial blurring for the source in the temporal lobe with decreasing threshold values for STKF until the point when redundancy dominates. Compared to the STKF, we expect the RSTKF to be more robust to redundancy and produce results with a higher accuracy and a better spatial resolution for the same threshold value, since its dynamical model is more sophisticated than that of the STKF.

Deep brain stimulation (DBS) is considered by some authors as the second most important therapeutic advance in Parkinson's disease (PD) after the introduction of L-dopa and dopamine agonists. We acquired scalp 256-channel EEG data from 10 PD patients with DBS of subthalamic nucleus (STN) during DBS ON and OFF state while performing 3-stimulus visual oddball task.

The preprocessing steps include DBS artefact filtering, bandpass filtering 1-40Hz, ICA for cardiac and eye-blinking artefact suppression, interpolation of bad channels and manual detection of bad segments.

The usage of event related potentials (ERP) analysis as an exploratory technique can be demanding for such high-density EEG. Thus, we decided to apply Parallel Factor Analysis (PARAFAC) on 3-way data array composed by averaged trials from all patients, both states (DBS ON/OFF) and all stimulus types (frequent, target, distractor). The resulting estimated PARAFAC components have 3 signatures - topography, time series and subject/state/stimulus type loadings for particular averaged trials. Finally, we compared loadings between trial types during both states by Wilcoxon test.

PARAFAC decomposition revealed evoked activity which showed significant difference between loadings of frequent and target stimuli in the DBS ON state and no difference in DBS OFF state. Thereafter we transformed the topography of the component into the source space, which points to areas of the fronto-parietal attention network.

We conclude that our results support a hypothesis that the DBS improves not only motor control but also affects cognitive networks.

Sleep induce changes in human brain connectivity/excitability [1]. According to [2], these modifications can be attributed to changes in the dynamics of neuronal responsiveness. In the epileptic brain, these activities and networks are also affected by these changes of state between sleep and wakefulness. The aim of this work is to estimate the sleep-induced changes in connectivity maps based on Cortico Cortical Evoqued Potentials (CCEP) for structures close to the epileptogenic zone in temporal lobe epilepsy. Intra-cerebral electrical stimulations are used to produce the CCEP, the resulting electrophysiological responses and connectivities are analyzed using SEEG recordings. Seven drug resistant epileptic patients were stimulated during 30 seconds in different sites during both sleep/wake states. CCEP are immediate causal response and can then be contaminated by the stimulation artefact. The first pre-processing step consists in removing this artefact while preserving the underlying CCEP. Two methods are evaluated, based on subspace decompositions: the Generalised Eigen Value Decomposition (GEVD) and the Common Spatial Subspace Decomposition (CSSD). The best separation results between the CCEP and the stimulation artefact is achieved using CSSD. Temporal connectivity Graph based on parametric model (DTF, PDC...) are then estimated in the sensor as well as in the reconstructed source space, for both sleep and wake states. The identified networks are validated by experts.

[1] Pigorini et. al., Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep, 2015.

[2] Massimini et al., Cortical mechanisms of loss of consciousness: insight from TMS/EEG studies. Archives italiennes de biologie, 2012.

Introduction: Electrocorticography (ECoG) is an invasive technique commonly used in patients or animals. ECoG measures the electrical potential using strips of electrodes placed directly onto the cortex. The high SNR allows to infer about brain activity with millimetric spatial resolution under the strips. This resolution can be improved using source estimation techniques (Cho, 2011) though it strongly decreases away from the electrodes (Zhang, 2008). Our goal is to characterize the resolution properties of eLORETA and MNE at increasing distance from the electrodes. This is particularly interesting in studies where the strip doesn’t completely cover the areas of interest.

Methods: A realistic setup from a monkey ECoG study with 128 channels (Nagasaka, 2011) was considered. The source-to-sensor mapping was implemented using a FEM approach (Simbio, 2014) for grids with different electrode number. We characterized the resolution properties with metrics quantifying the localization error, the activity spread and the relative sensitivity of source estimates (Hauk, 2011) for Point-Spread-Functions (PSF) and Cross-Talk-Functions (CTF) in the whole source space.

Results: For a single active source (PSF), as its distance from the electrodes increases the eLORETA spread increases while the localization error is always zero, whereas for MNE all metrics increase. When all sources are simultaneously active (CTF), for both inverse algorithms the resolution metrics depend on the distance from the grid, slightly less for MNE.

Discussion: Source estimate from ECoG is reliable only near the electrodes and must be carefully interpreted accordingly to the resolution properties of the inverse algorithm.

Dynamic Granger-causality methods aim to quantify directed interaction strengths between brain areas with high temporal resolution, using simultaneously recorded electrophysiological signals. These methods are often based on time-varying multivariate autoregressive (tvMVAR) modeling, and while several such approaches have been proposed there currently is a lack of unbiased analyses and comparisons of their performance. Our aim was to compare the performance of commonly used tvMVAR methods using numerical simulations and real benchmark EEG data along fixed criteria. We compared classical Kalman filter (MVAAR), Dual Extended Kalman Filter (DEKF), Recursive Least Squares (RLS) and General Linear Kalman Filter (GLKF), and two ways of exploiting repeated observations: 1) single-trial modeling followed by averaging, and 2) multi-trial modeling where one tvMVAR model is fitted across trials. Our results show that while most approaches can adequately model simulated and real data, the best performance was often achieved with GLKF and a multi-trial approach. This approach’s accuracy, however, more strongly depended on model order choice and sampling rate. In fact, this algorithm produced highly variable estimates at high sampling rate and when large model order was required. In this scenario downsampling successfully reduced the estimates’ variability. Single-trial approaches using GLKF and MVAAR were more robust against setting model order too high and showed good performance at high sampling rates. For these algorithms downsampling degraded performance, because of slower adaptation speeds. Our findings help understand the strengths of various tvMVAR approaches and provide practical recommendations for their use in modeling dynamic directed interactions from electrophysiological signals.

Simulated MRI data have great value for segmentation algorithm development since they provide a large set of priors. However, such simulated MRI are not readily available for children’s heads. This along with the prevalence of adult simulated data frequently used as priors (BrainWeb), was our motivation to build an MR simulator for children.

The simulator works similarly to the adult version but with a child head phantom. Although some ground-truth datasets for children’s brains are available, data covering all tissue types of children’s heads for use as a phantom is not readily available.

Our head phantom was created in two steps. In the first step, the skull and scalp from an MRI-CT pair obtained from the Retrospective Image Registration Evaluation (RIRE) database was extracted. Skull extraction used thresholding applied to the CT image at 700 Hounsfield units (HU), followed by a morphological step to remove any anomalies.

In the second step, an MRI ground-truth pair was obtained from the UNC Infant Atlas. Non-rigid registration between MRIs from UNC and RIRE databases was performed. A phantom was created by fusing the skull data with the ground-truth of the brain, with visual validation. MR simulation of this phantom was performed using a hybrid of Bloch equation and tissue template simulation, enabling simulation of image contrast, partial volume, and noise.

The ground-truth and MRI obtained from the simulation can be used as priors for segmentation algorithms of complete children’s heads, with the aim of creating realistic head models for EEG/MEG source analysis.

Intro

Cortico-cortical Evoked Potentials (CCEPs), through the use of comparable bi-directional stimulations in pairs of ROIs, allow studying the directionality and reciprocity of functional connections. This paradigm is compatible with the concept of effective connectivity which evaluates the influence of one neural population onto another.

Brain activity changes according to the vigilance state (wakefulness/non-REM sleep). In intracerebral and scalp EEG recordings, it was demonstrated that irrative zones are more active (spikes) and large during sleep than wakefulness (R.Rocamora et al. 2015). In this study, we aim to investigate the influence of vigilance state on cortical reactivity.

Material and methods

We included one drug-resistant epileptic patient from a cohort of 5 patients.

Intracerebral electrical stimulations were performed using biphasic pulses (1050µs, 1mA,1Hz) in 120 intracerebral contact pairs during non-REM sleep and wakefulness conditions.

5 anatomical ROI were selected: amygdala, anterior and posterior hippocampus, entorhinal cortex and temporal pole. To characterize and compare the cortical reactivity during non-REM sleep and wakefulness, we compared the occurences, the averaged CCEPs amplitudes and latencies.

All CCEPs were analyzed using EEGLab and homemade algorithms. Data analysis comprised: 1.Filtering, 2.Stimulation peak detection, 3.Time boxes creation around the peak, 4.Artefact rejection, 5.Time boxes averaging around the peak and 6.CCEPs detection using a permutation and t-test.

Preliminary results

We observe a generally larger amplitude and spatial distribution of the CCEPs during non-REM sleep than wakefulness. These results are in accordance with the irrative zone behavior observed in epilepsy. No significant delay was observed between sleep and wakefulness latencies.

A previously introduced method for source connectivity analysis allows the projection of multivariate autoregressive (MVAR) model coefficients from MEG sensor space to source space using a lead-field matrix and a lead-field based inverse operator (weight matrix). However, the method shows deficits when source positions are not a priori known. This could be due to the fact that the product of the weight and lead-field matrices is not an identity matrix and to the crosstalk between sources. We improved the method to mitigate these drawbacks and examined the improved method using simulations and a real MEG dataset. For the estimation of MVAR model coefficients in source space, we used an inverse of the weight matrix instead of the lead-field matrix to reduce errors caused by the assumption that multiplying weight and lead-field matrices results in an identity matrix, and we applied a nulling beamformer for crosstalk suppression between sources. The partial directed coherence (PDC) was used as a connectivity measure calculated from the estimated MVAR model coefficients. In simulations, applying the inverse of the weight matrix reduced the errors in in/out-degree of the PDC, and spurious connections were reduced by using the nulling beamformer. In a case study, we applied our method to the interictal MEG recordings and could identify information flows from left to right regions nearby the focal cortical dysplasias found in MRI. These results suggest that the proposed method has considerable potential as a non­invasive approach for source connectivity analysis without a priori knowledge about source locations.

Introduction: Measurements on animals are important in clinical practice. The aim of our study was to develop a software module for statistical brain-mapping. This study compares brain activity during application of psilocin.

Methods: In this study we measured electrical activity of 9 rat’s brain at 4 times and computed absolute spectrum of each signal. Splines mapping was used for imaging electrical activity of the brain. Statistical differences were calculated using one way ANOVA. Subsequently color was assigned to individual points of the 3D map at 3 different levels of significance: α = 0.05; α = 0.01 and α = 0.001.

Results: The module for significant probability mapping (SPM) was used to find differences between repeated measures EEG on rats. We proofed that there is a significant difference after application of psilocin to rats.

Conclusion: The module for significant probability mapping (SPM) was successfully implemented. MATLAB was used as a programming language. Validity of brain model was confirmed.

This study was supported by projects LO1611/NPUI, MICR VI20172020056; Progress Q35; European Regional Development Fund and by Czech Technical University research program SGS (SGS15/229/OHK4/3T/17).

Visual inspection of the EEG to determine the seizure onset zone (SOZ) in the context of the presurgical evaluation in epilepsy is time-consuming and often challenging or impossible. We offer an approach that uses EEG source imaging (ESI) in combination with functional connectivity analysis (FC) to localize the SOZ from ictal EEG.

Ictal low-density scalp EEG from 111 seizures in 27 patients who were rendered-seizure free after surgery was analyzed. For every seizure, ESI (LORETA) was applied on an artifact-free epoch (1-5s) selected around the seizure onset. Additionally, FC (swADTF) was applied on the reconstructed sources. We estimated the SOZ in two ways: the source with (i)highest power after ESI and (ii)the most outgoing connections after ESI and FC. For both approaches, the distance between the estimated SOZ and the resected zone (RZ) of the patient were calculated.

Using ESI alone, the SOZ was estimated inside the RZ in 31% of the seizures and within 10mm from the border of the RZ in 42%. For 18.5% of the patients, all seizures were estimated within 10mm of the RZ. Using ESI and FC, 72% of the seizures were estimated inside the RZ, and 94% within 10mm. For 85% of the patients, all seizures were estimated within 10mm of the RZ. FC provided a significant added value to ESI alone (p<0.001).

ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy.

EEG-fMRI maps the generators of interictal epileptiform discharges (IEDs) in epilepsy patients to aid pre-surgical evaluation. This requires 1) EEG-defined IED timings and 2) a model of the haemodynamic response (HRF). Research suggests the standard HRF used in cognitive neuroscience may be inappropriate for modelling IEDs. We aimed to derive the HRF to IEDs in a group of children with focal epilepsy. We then tested if this derived HRF could improve EEG-fMRI maps for pre-surgical evaluation.

We collected simultaneous EEG-fMRI data at 1.5T using a 64-channel EEG system. Epileptic events were visually coded following artefact correction. Twenty seven drug resistant focal epilepsy patients in whom the epileptogenic region was confirmed were recruited (post-surgical Engel outcome=1 or visible MR lesion).

Sixteen of these patients had concordant EEG-fMRI maps when using the canonical HRF. This group was used to generate a new HRF using a smooth FIR deconvolution (time window of -22 to 22sec surrounding the IED onset). Subsequently a principal component analysis was used to determine the IED-HRF response across this group. The remaining 11 patients with discordant EEG-fMRI maps were used to assess the improvement this new HRF had on localisation.

Haemodynamic changes up to ~20sec prior to IEDs onset were observed. This early response may represent a metabolic change in state that is predictive of the epileptiform activity. In subjects where the standard canonical basis set failed the IED-HRF was able to localise in 64% of patients. This could potentially increase the clinical yield of EEG-fMRI.

The neural overlap between spectrotemporal sound feature representations in the human cortex during listening to speech and music still remains unclear. To assess this we recorded electrocorticographic data from 8 epileptic patients. Participants listened to natural speech and a music stimulus. For both conditions we built encoding (predicts high gamma neural activity [70-150 Hz] using the spectrogram representation of the sound) and decoding models (predicts the sound spectrogramn by using the high gamma neural activity). Further we used a cross-condition analysis by applying the decoding model built on the speech condition on the music condition and vice versa. We found robust overlaps between the speech and the music condition in terms of anatomical location and frequency tuning in the auditory areas.

This work aims at building a pipeline to analyze the recorded magnetic field from the human heart. We constructed the pipeline using simplified meshes of the heart and torso and simple simulated signals at first and we will continue with sophisticated simulations and validated recordings. The pipeline contains two main parts, namely the forward and the inverse solutions.

The forward solution starts with segmenting an individual Magnetic resonance image (MRI), which we manually segmented into two triangular surface meshes for heart and torso, as a preliminary model, and then we constructed cubic meshes for heart and torso. We also simulated one dipole at a single time point in the middle of the heart, we considered a vertical direction (from head to feet) to represent the potential from the bundle branches, and then we calculated the forward solution using Finite Element Method (FEM) for a magnetometer sensors model. We used the Simbio software for the forward calculation. In order to solve the inverse problem, we also calculated the Lead Field Matrix (LFM) by defining a volumetric grid in the heart.

We performed the inverse solution using two methods, namely low-resolution electromagnetic tomography (LORETA) and Spatiotemporal Kalman Filtering (STKF), which is based on linear state space modeling. We applied the pipeline on two magnetocardiographic (MCG) datasets and we will present our first localization results.

In the future, we intend to use a nonlinear state space model for the STKF, so that it can better describe the dynamics of the heart signal.

Background: Brain source mapping from distant measurements such as M/EEG brings more insight into brain normal and pathological functioning. This inverse problem is commonly carried out based on non-invasive electrode setup, however these surfacic data do not well capture the activities of deep structures. To get a wider picture of the brain activation map, we propose to use the Stereo-EEG (SEEG) setup, consisting in shaft electrodes implanted within the structures of interest.

Methods: Following preliminary works [1] dealing with the necessary conditions for successfull dipole localization from SEEG, we adress the distributed source imaging problem [2]. In particular, we explicitely take into account the forward model uncertainties. Using a variational Bayesian framework, the source time-course and the dipole projection gains are simultaneously optimized. The gain posterior distributions are constrained to remain close to a confident physical model through the introduction of multivariate Gaussian priors, preventing from non-physiological estimates.

Results: We demonstrate under simulations that the method enhance the accuracy of the source time-course estimates as well as the sparsity of the resulting source map. The approach is validated on data of intra-cranial stimulations (for which the position of the source is known), as well as on SEEG data of epileptic spikes, validated by the surgery outcomes.

Références:

[1] V. Caune et. al., Evaluating dipolar source localization feasibility from intracerebral SEEG recordings, NeuroImage, 2014.

[2] S. Le Cam et. al., SEEG dipole source localization based on an empirical Bayesian approach taking into account forward model uncertainties, NeuroImage, 2017.

The uncertainty in MEG-to-MRI co-registrations propagates through the forward model to uncertainty in source reconstruction results of MEG data. However, the common tools for source reconstruction in MEG or EEG analysis do not account for that source of uncertainty and usually only the variance of the noise is considered in the assessment of the standard error or covariance of source parameters. For realistic head models, the computational costs of forward modeling are unfeasible for straightforward Monte Carlo simulations. To overcome this problem, a polynomial expansion of the forward model is constructed as a function of the co-registration parameters.

The six-dimensional co-registration space of three rotation and three translation parameters is sampled using a Metropolis algorithm. The eigen-decomposition of the Metropolis sample covariance matrix provides a map to a six-dimensional uncorrelated parameter space. Based on the uncorrelated parameters, the forward model matrix is expanded in terms of Hermite polynomials. The number of polynomial terms is limited using an adaptive expansion with an error tolerance of 1% resulting in 61 terms for our demonstration model.

For this expansion, the forward model is evaluated at 97 different co-registration parameterizations. The polynomial expansion is used as a computationally cheap surrogate of the forward model construction.

We demonstrate the benefit of the expansion by drawing 10000 independent samples of the linearly constrained minimum variance beamformer for 42 target sources.

Our methods provide a computationally feasible assessment of the distribution of source estimates, e.g. the beamformer activation maxima, based on the uncertainty in MEG-to-MRI co-registrations.

Transcranial electric stimulation (TES) is a non-invasive technique for cortical stimulation. Depending on the electrode positions and polarity, the current density distribution in the head changes its amplitude and orientation. For treatment of depression, several stimulation montages for targeting the dorsolateral prefrontal cortex (DLPFC), were introduced.

With the present study, we aimed to evaluate magnitude and orientation differences in the DLPFC originating from different stimulation montages.

We generated an individual five compartment finite element model from structural magnetic resonance images of a volunteer (age 23 years). For TES simulations with 1 mA current strength, we placed 5x7 cm patch electrodes as anode at position F3 and used a cathode at positions Fp2, F4, F8 and P2. We analysed the amplitude and orientation of the resulting current density distributions in the DLPFC.

The mean current density in the DLPFC was 0.11±0.03 mA/m² for F3/Fp2, 0.06±0.01 mA/m² for F3/F4, 0.10±0.02 mA/m² for F3/F8 and 0.08±0.01 mA/m² for F3/P2. The current density orientation difference in the DLPFC between the montages F3/F4 and F3/Fp2 was 32.9±6.4 degrees, between F3/F4 and F3/F8 27.6±5.6 degrees, and between F3/F4 and F3/P2 41.6±6.6 degrees.

Our simulation results demonstrate considerable effects of the stimulation montage on the amplitude and the orientation on the current density in the DLPFC. Bai et al. (2014) compared similar stimulation montages and also found highest stimulation intensities in the DLPFC for the F3/F4 montage. With our results, we underline the importance of detailed models in TES simulations and the consideration of the stimulation montage.

The N1 component of the visual ERP is considered to be sensitive to print, as it becomes larger for words than for symbols during learning to read in children. In adults, however, the N1 in response to visual words typically corresponds to a lengthy GFP segment, which opens the question whether different neural processes occur in this time range. Here, we summarize 4 different ERP studies in adults with various designs and in different writing systems. The studies analyze different parts of the N1 component in order to test differential sensitivity to various aspects of visual word processing. Study 1 shows that lexicality effects occur in the late N1, but not in the early N1 in German readers. Study 2 shows that repetition effects in Chinese readers occur in the late N1, but not in the early N1. Similarly, study 3 shows masked priming effects in Chinese readers in the late N1, but not in the early N1. Finally, study 4 shows sensitivity for print irrespective of task in Chinese readers in the N1 onset, but task modulation in the N1 offset. Taken together, the results suggest that the N1 component is not as functionally homogeneous as previously thought and that during the lengthy N1 duration, different visual-orthographic processes are unfolding.

[Introduction] We investigated the changes in directional cross frequency interactions between theta and alpha oscillations, across six cortical regions, induced by a symptom provocation procedure, in patients with obsessive compulsive disorder (OCD), and in a normal control group.

[Methods] Nine OCD outpatients and nine healthy controls participated in this study. Awake, eyes closed EEG was recorded under conditions (C1) initial rest, (C2) while gently holding a clean paper towel, and (C3) under the instruction to imagine that the towel is repulsively contaminated (symptom provocation, SP). For each condition, signals of cortical electric neuronal activity (i.e. current density) were calculated with sLORETA at medial-prefrontal, precuneus, inferior-parietal, and dorsolateral-prefrontal cortices. Instantaneous amplitudes for the theta (4-8Hz) and alpha (9-14Hz) bands were obtained and used for computing Granger causal directional cross-frequency, cross-cortical interactions.

[Results] The effect of SP was assessed by comparing conditions C2 minus C3. In controls, SP is characterized by a significant increase in mPFC theta due to right fronto-parietal alpha. In contrast, SP in the OCD group mainly displayed alpha-alpha RIPL alpha decrease due to RDLPFC. A direct comparison of OCD and normal controls in the SP condition (C2) showed significant frontal decreases of theta-alpha interactions.

[Discussion] The symptom provocation procedure induced functional changes of cross-frequency connections both in OCD and control mainly involving core right hemisphere network nodes. Functional cross-frequency interactions involving all frontal nodes were decreased in OCD compared to controls during SP. These results support the use of cross-frequency interactions a possible trait marker of OCD.

Background: Neurexan® is a medicinal product containing four ingredients; passionflower, oats, coffee and zinc valerianate. It has been shown to reduce nervousness, restlessness, acute stress, and insomnia by modulating biological auto-regulating processes. Induced stress sensitizes the amygdala, which increases vigilance and in turn drives the stress response. This is mediated by an amygdala-prefrontal cortex circuit, in which stress impairs the top-down cognitive functions of prefrontal regions, while strengthening the emotional bottom-up responses of the amygdala. We therefore hypothesized that Neurexan® may induce changes in amygdala activation during emotion processing elicited by an emotional expectancy task.

Method: The drug effect was investigated in a randomized, placebo-controlled, double-blind, two-period-crossover clinical trial. The brain response of 37 male subjects to the emotional expectancy paradigm was measured after intake of a single dose of Neurexan® or placebo by 3T functional magnetic resonance imaging. During the task, negative, positive, and neutral IAPS pictures were presented, half of them preceded by visual cues. The visual cues before picture presentation were arrows pointing up (positive picture), down (negative picture) and to the right (neutral picture). The drug effect was assessed with paired t-test comparing drug and placebo condition in the contrast expectancy positive > expectancy negative.

Results: We found amygdala activation in response to expected pictures. Furthermore, we observed significant differences in activation of the left amygdala during the expectancy task under drug compared to placebo condition. The differences in activation are explained by reduced changes in amygdala reactivity modulated by Neurexan® during expectancy of emotional pictures.

Objectives: Neurexan® is a medicinal product containing four ingredients, passionflower, oats, coffee, and zinc valerianate. Neurexan® has been shown to reduce nervousness, restlessness, acute stress, and insomnia. Recent research suggested that it attenuates the neuroendocrine stress response in healthy volunteers. It is known that acute stress initiates changes in functional connectivity (FC) between amygdala and cortical regions. Additionally, changes in amygdala centered resting state functional connectivity (rs-FC) are also associated with trait and pathological anxiety. In the present study, we explored if Neurexan® moderates the effect of acute stress on the amygdala-centered rs-FC and if this is further influenced by trait anxiety or behaviors related to anxiety symptoms.

Methods: Thirty-nine healthy male subjects (age=43.7±9.8) participated in a double-blind, randomized, placebo-controlled, crossover clinical trial employing fMRI imaging. In each scanning session, an 11-min resting state (RS) measurement was performed at two time points: after the intake of a single dose of Neurexan® or placebo (RS1), and after the participants completed the stress task (RS2). Bilateral centromedial (CeA) and basolateral (BLA) subregions of the amygdala were used as seeds to calculate resting state FC maps. The treatment effects were analyzed with whole–brain within–subject ANOVA.

Results: A significant effect of Neurexan® was found on rs-FC between left centromedial amygdala and cortical regions including the left PFC and IFG as well as right centromedial amygdala and precuneus, right IFG and IPL. Furthermore, anxiety measures predicted the Neurexan® effect on stress-induced rs-FC changes from right BLA to vmPFC, left Amygdala, and right IFG.

Objectives: Cardiovascular function is critical to adaptive behavior and can be measured by heart rate variability (HRV) which is controlled by the Autonomic Nervous System (ANS). Previous studies have shown that the ongoing variability in ANS tone measured by HRV is associated with stress-induced changes in dACC and amygdala functional connectivity. Neurexan®, a medicinal product sold over the counter, contains passionflower, oats, coffee and zinc valerianate. It has been investigated in patients with symptoms related to acute stress, nervousness/restlessness, and insomnia. The previous research suggested attenuated neuroendocrine stress response in healthy volunteers, altered stress reactivity, and lowered amygdala activation after Neurexan® intake. This study explores the effects of Neurexan® on the sympathetic and parasympathetic nervous systems and the stress responsivity in ANS measured by HRV.

Methods: Thirty-nine healthy male subjects participated in a double-blind, randomized, placebo-controlled, two-period crossover study assessing HRV during the scanning sessions. On the first treatment day, half of the participants took Neurexan® (N=20) or placebo (N=19) and vice versa on the second day. The participants performed the ScanSTRESS, which uses arithmetic and mental rotation tasks to induce stress, after intake of Neurexan® or placebo, respectively. Heart rate was continuously measured by photoplethysmography.

Results: To measure the drug effect, we performed paired t-test between drug and placebo conditions during the stress task. We found a significant drug effect in low frequency and high frequency ratio (LF/HF ratio) (p = 0.01; T=-2.5). Under drug LF/HF was significantly reduced compared to placebo, suggesting a dampened stress response.

The P300 is one of the most investigated event-related potentials (ERPs) in the study of psychiatric disorders. Nevertheless it suffers from a lack of specificity and sensitivity. In previous studies (Campanella et al., 2010; 2012), the application of a more ecological bimodal oddball design has shown an increased sensitivity of the P300 component .

In the present ongoing study we compare the results of a classic oddball design procedure with those of a more ecological bimodal design in three groups of patients presenting schizophrenia (SZ), schizoaffective (SA) and bipolar (BD) disorder matched to a group of healthy controls (HC). Patients were examined at three times: T0: admission at the hospital; T1: before leaving the hospital; and T2: six months after leaving the hospital. Patients were assessed through a structured clinical interview (SCID, ref), and a completion of different clinical evaluation scales (PANSS, Young Mania Scale, Beck, Stai,…). All groups were then confronted to an EEG recording (20 channels, A.N.T. software) during successive oddball tasks.

The objectives of this study are twofold: (1) investigate whether the use of a specific oddball task (visual vs. auditory vs. cross-modal) allowed to enhance the discriminative power of the P300 between psychotic patients and healthy controls; and (2) investigate the correlations between the evolution from To to T2 of the P300 and the evolution of the clinical evaluation of the patient.

Here we would like to present further results of this clinical study, especially those of the group of patients presenting schizophrenia.

Background: Neurexan®, a medicinal product sold over the counter, contains four ingredients; passionflower, oats, coffee and zinc valerianate. Neurexan® has been investigated in patients with symptoms related to acute stress, nervousness/restlessness, and insomnia. The previous research suggested an attenuated neuroendocrine stress response in healthy volunteers induced by Neurexan®. This study further explores the effects of Neurexan® on cognitive performance and attention that can be assessed by the oddball paradigm. It is generally recognized that stress is associated with cognitive impairments. Expecting that Neurexan® reduces the stress level, we hypothesized that subjects in the placebo group would be more susceptible to distraction compared to treatment group.

Methods: In a randomized, placebo-controlled, double-blind, two-period crossover trial, brain responses of 39 healthy, moderately stressed males were measured during an unattended auditory oddball paradigm via 64-channel electroencephalogram (EEG) after intake of a single dose of Neurexan® or placebo. The paradigm consisted of 80% standard tones and two types of deviant tones (10% frequency deviant; 10% duration deviant), presented in a pseudo-randomized order.

Results: Significant effect of Neurexan® on the latency of mismatch negativity decreased latency under treatment) was observed with repeated-measures ANOVA. The main effect of treatment (F(1,37)=4.297, p=.045, η2=.104) and significant treatment x deviant-type interaction (F(1,37)=8.828, p=.005, η2=.193) were found. Further Wilcoxon-test for paired samples showed that reduction of latency was present for the frequency deviant stimuli (z(37)=-2.85, p=.004).

Conclusion: Significant reduction of MMN latency under drug suggests that Neurexan® leads to subtle primary processing changes in term of reaction time.

Since the discovery of LSD, serotonergic hallucinogens have been used to experimentally model psychosis, even in animals. Its translational validity can be verified by the degree of similarity between various measurable values in both species. Consensus in behavior (disorganization, increased anxiety, occurrence of stereotypical movements) and information processing was already observed. Similarity in the EEG signal between psychotic patients and intoxicated rats has not yet been verified.

The aim of this study was to describe changes in cortical EEG after administration of various serotonergic hallucinogens – psilocin, LSD, mescaline and DOB and compare these results with human data of psychotic patients.

12 rats for each group were implanted with 14 cortical electrodes. EEG was recorded seven days later. After the first ten minutes of recording (baseline), the treatment was applied and recording continued for another 90 minutes. Only signal of selected time segments of behavioral inactivity was evaluated. Quantitative (spectral and coherence) analysis in the Neuroguide program was performed.

EEG power spectral analysis revealed a general decrease in absolute EEG power in all frequency bands in all drug conditions. A common reduction in coherences, especially fronto-temporal in higher frequencies has also been shown.

Serotonergic hallucinogens cause profound electrophysiological changes in the rat brain. The results also revealed some characteristic patterns in the EEG of individual substances. The observed decrease in functional connectivity in rats was also observed in human subjects under the influence of psychedelics and is, in some cases, a common finding in psychotic patients.

Introduction: The presented method deals with changes in human and rats brain functional EEG connectivity conditioned by administration of psilocin (in rats) and psilocybin (in human). The searched similarities between rats and human models are difficult to be found when preserving low-level information e.g. all combinations of connections within all frequency bands in all epochs. Here, we show that unique functional brain clusters coherently modulated by a particular substance are embedded in multi-dimensional space of coherences and can be extracted by appropriate dimensional reduction technique.

Methods: EEG recordings were acquired with respect to pharmacokinetics of psilocin (in rats) and psilocybin (in human). Weighted phase lag indices (WPLI) were calculated at four time epochs after administration. WPLI time differences between epochs were calculated and t-Distributed Stochastic Neighbor embedding (t-SNE) reduced data dimension. A number of clusters was determined by silhouette clustering index and similarities were labeled by k-means algorithm. Surrogate series preserving power spectrum of instantaneous angular frequency in EEG data were generated to statistically address the clustering properties.

Results: Preliminary results of psilocin in rats clearly showed four clusters of connections. Three clusters were localized intra-hemispherically and only one connected both hemispheres together. In human, the psilocybin resulted in two symmetric clusters distributed mostly intra-hemispherically.

Conclusion: Developed method is capable to extract individual (rats or human) and common (rats and human) long term connectivity dynamics induced by drug administration.

This work was supported by the grants GACR 17-23718S, AZV 15-29370A, Progres Q35, and project LO1611 under the NPU I program.

[Introduction] Transcranial Direct Current Stimulation (tDCS) is getting interests in treatment of psychiatric disorders. As the electrode montage of tDCS defines flow of stimulation current, optimization of an electrode montage is crucial for treatment applications.

The aim of this study is to investigate tDCS effects with different montages in patients with major depression.

[Methods] Eighteen patients and 20 healthy controls (HC) participated. 1 mA tDCS was administered for 20 min either on the DLPFC (F5 EEGelectrode) or medial prefrontal cortex (AFz). Patients and HC received tDCS on one of the site in a randomized order. The Positive and negative affect schedule (PANAS) and State-Trait Anxiety Inventory (STAI) tests were measured before and after tDCS for each session.

[Results] We found that PANAS negative affect scores in the patient group decreased after tDCS only in the F5 stimulation, while no effect in the AFz stimulation condition. No significant tDCS effects were observed in the STAI scores of patients. In controls, there was no significant changes neither in PANAS scores nor STAI scores.

[Conclusion] A previous tDCS study on location of F3 EEG electrode showed no significant changes in PANAS scores in patients with major depression (Wolkenstein and Plewnia, 2013). We consider this discrepancy can be explained by the difference in electrode montages. In conclusion, we argue that the anterior part of the DLPFC could have stronger potency for the treatment of major depression, consistent with subregional difference in treatment efficacy of rTMS on major depression.

Background

Disorganized speech has been shown to be related to functional and grey matter abnormalities in schizophrenia. However, the relationship between white matter and disorganized speech is poorly understood. We investigated the association between formal thought disorders (FTDs) and white matter microstructure in 61 patients with schizophrenia spectrum disorders. We hypothesized that FTDs are related to important fibers of the language system such as the uncinate fascicle and superior and inferior longitudinal fascicle.

Methods

The Bern Psychopathology Scale (BPS) organizes schizophrenia symptoms in three neurobiological dimensions, i.e. language, limbic and motor. The BPS was used to rate the dimension of language abnormalities ranging from negative FTDs to positive FTDs. Tract-based spatial statistics (TBSS) was used to study whole brain white matter abnormalities. Fractional anisotropy (FA) values were correlated with the BPS language dimension including age, gender, antipsychotic medication and the motor and affective dimensions of the BPS as covariates.

Results

The TBSS analysis indicated increased FA in left-hemispheric pathways of the language system in patients with negative FTDs; and lower FA values for patients with positive FTDs at p<0.05 (FWE corrected). The fiber tracts associated with FTDs included the left uncinate fascicle, superior and inferior longitudinal fascicle and the inferior fronto-occipital fascicle.

Discussion

We found an association of FTDs in schizophrenia and disturbed WM in language related pathways. Our findings are in line with the literature, linking FTDs in schizophrenia to structural and functional abnormalities in the language system. Thus, altered white matter properties in relevant fiber tracts may represent distinct pathobiology of specific formal thought disorders.

Objective : The aim of the study was to identify electrophysiological biomarkers of major depressive disorder (MDD) through high-density EEG technique.

Methods: Seven patients suffering from MDD and eight healthy controls underwent EEG recording using 128 or 256 scalp electrodes during eyes closed resting-state conditions. Microstate analysis was performed at individual and group levels. Microstate variants were identified and their parameters such as mean correlation, mean duration, time coverage, and segment count density were evaluated.

Results: Cross-validation criterion used to determine the most dominant topographies revealed six (A-F) microstates. The six microstates across subjects in each group explained more than 80% of global variance. Results of two-way repeated measures ANOVA revealed significant group x microstate interaction for segment count density. Post-hoc test revealed significant group difference for class A microstate, which showed decreased value in MDD patients.

Conclusion: Parameters revealed by microstate analysis are suggested to be possible electrophysiological biomarkers of functional brain abnormities in MDD patients.

The study has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 739939

Functional magnetic resonance imaging (fMRI) is useful to explore the brain’s functional organization and to examine whether functional organization is altered in patients with mental disorders. Functional connectivity can be defined as the synchrony of neural activity among regions. Areas of the brain which exhibit signal fluctuations correlated in time are assumed to be functionally connected. Independent component analysis allows us to decompose fMRI data into independent and non-Gaussian spatiotemporal components without any use of a reference function or predefined seed voxel. Independent component analysis is extensively applied as an exploratory analysis for the investigation of resting state functional connectivity. To examine the resting-state functional connectivity of mental disorders we evaluated resting-state networks in 20 patients with schizophrenia and 20 healthy participants using 3T MRI scanner. This study was approved by the local ethics committee, and written informed consent was obtained from all participants recruited. Independent component analysis was performed for all data using MELODIC of FSL (FMRIB, Oxford University, UK). Preliminary analysis showed that under each diagnostic grope similar pattern of functional connectivity was observed, however, there was anatomically aberrant activity in patients compared to control. We are planning to conduct deeper analysis of dual regression with increase the number of subject.

Background: Preterm birth has been associated with an increased risk for ADHD-like behavioural symptoms and cognitive impairments. However, direct comparisons across ADHD and preterm-born samples on neurophysiological measures are limited. The aim of this analysis was to test whether quantitative EEG (QEEG) measures identify differences or similarities in preterm-born adolescents, compared to term-born adolescents with and without ADHD, during resting-state and cognitive task conditions. Methods: We directly compared QEEG activity between 186 preterm-born adolescents, 69 term-born adolescents with ADHD and 135 term-born control adolescents during an eyes-open resting-state condition (EO), which previously discriminated between the adolescents with ADHD and controls, and during a cued continuous performance task (CPT-OX). Results: Absolute delta power was the only frequency range to demonstrate a significant group-by-condition interaction. The preterm group, like the ADHD group, displayed significantly higher delta power during EO, compared to the control group. In line with these findings, parent-rated ADHD symptoms in the preterm group were significantly correlated with delta power during rest. While the preterm and control groups did not differ with regard to absolute delta power during CPT-OX, the ADHD group showed significantly higher absolute delta power compared to both groups. Conclusion: Our results provide evidence for overlapping excess in the absolute delta range in preterm-born adolescents and term-born adolescents with ADHD during rest. During CPT-OX, preterm-born adolescents resembled controls. Increased delta power during rest may be a potential general marker of brain trauma, pathology or neurotransmitter disturbances.

Auditory Sensory Gating in Schizophrenia: relation with cognitive functioning, social cognition, socio-relational functioning

The neurophysiological effect of EMDR and TF-CBT in PTSD: an EEG study

Psychoses are aetiologically complex disorders that affect about 1 − 2% of the population during their lifetime. Psychotic symptoms are thought to represent disturbances in higher-order brain functions that can be grouped according to their dysfunction in one or more of the following three neural brain circuitries: language, affect, motor function. Dysfunction of the neural language brain circuitry has already been linked to disturbances in expressive speech and formal thought disorders. However, it remains currently unknown if the language brain circuitry is only disturbed in psychosis, or if already individuals at familial or clinical high-risk show some extend of aberrancy.

To examine the whole spectrum form health to psychosis, four different subject groups are being examined: healthy controls (HC), first-degree relatives of psychosis patients (REL), a clinical high-risk group (CHR) and psychosis patients (PAT). In total, 120 subjects (30 per group) will complete a lexical priming task during electroencephalography and functional magnetic resonance imaging.

On a behavioural level, we expect to find subtle language dysfunction in the REL and CHR group. Furthermore, we hypothesize that aberrant neural activation patterns are present during the language task in PAT, CHR and REL groups in comparison to HC individuals. Finally, we aim to depict that aberrant neural activation in language - related brain areas is most pronounced in the PAT group and to a lesser extend present in the REL group. With this study, we hope to improve diagnostic strategies, treatments and outcome predictions.

Visual backward masking (VBM) is a very sensitive endophenotype of schizophrenia. Masking deficits are highly correlated with reduced EEG amplitudes. In VBM, a target stimulus is followed by a mask, which decreases performance on the target. Here, we investigated the neural correlates of VBM in unaffected siblings of schizophrenia patients. We had three conditions: target only and two VBM conditions, with long and short inter-stimulus intervals (ISI). Patients’ performance was impaired, while the siblings performed at the same level as the controls. Interestingly, EEG peak amplitudes were higher in siblings compared to controls, while they were lower in patients relative to controls as previously reported. For siblings, EEG amplitudes were at the same level in all conditions. For controls and patients, EEG peak amplitudes increased with task difficult, e.g., amplitudes in the long ISI condition were lower than in short ISI condition. Our results suggest that unaffected siblings of schizophrenia patients use a compensation mechanism tuning the brain to maximum performance in all conditions. Since siblings are already at the peak of their activations, increasing the task difficulty does not change brain processing.

Despite identical diagnosis, patients with psychosis show a variety of clinical symptoms. Various psychosis symptoms relate to a disturbed perception, experience, regulation or expression of emotions. Previous research indicates that emotional dysregulation may form a distinct psychosis symptom dimension that is linked to aberrant function and structure of the limbic system and its cortico-basal ganglia and cortico-cortical connections. However, the nature of emotional dysregulation in psychosis has not been studied extensively yet. We expect that disturbed affect in psychosis may be best conceptualized as a dimension, that varies with psychosis vulnerability.

Neural activation patterns are investigated using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) from four different subject groups: patients with psychosis, subjects at clinical high-risk for psychosis, first-degree relatives of patients with psychosis and healthy controls. During fMRI and EEG examination, a specifically developed face perception task is being used. The presented stimuli are short animations of faces that vary in certain characteristics: gender (male, female), aesthetic (high, low), head movement (up, down) and gaze direction (direct, averted). Subsequently, all face stimuli are rated with regard to gender, dominance, trustworthiness, aggressiveness, health and attractiveness.

We hypothesize that the four study groups differ in terms of event-related potentials and brain activation patterns in affect-related brain regions. Furthermore, we expect that these differences are being linked to measures of emotionality and emotional processes such as emotion regulation and perception. The expected results will give further insights in the psychopathology of psychosis and might improve future diagnostic and treatment options.

We have previously shown that EEG microstates during resting-state have a deviant pattern of temporal presence in a group of adolescents with 22q11.2 deletion syndrome (Tomescu et al., 2014), as well as in a different group of adults diagnosed with schizophrenia (Tomescu et al., 2015). To further explore the relationship between this deviant pattern and the development of schizophrenia, we computed partial least squares correlation (PLSC) between the temporal presence (characterized by the global explained variance (GEV) and duration) of the microstates and the symptoms assessed using the 19 item symptom scale from the structured interview for prodromal syndromes (SIPS, Miller et al., 2003) . The PLSC analysis allowed us to identify patterns of GEV and duration that covary with prodromal symptoms by computing latent variables (LVs), which represent the optimal link between these two modalities (Krishnan et al., 2010). Using permutation testing, we obtained a significant latent variable (5000 iterations, p= 0.03) explaining 62% of the covariance. This LV is characterized by significantly positive loadings of the GEV of microstates A and B and significantly negative loadings of the duration of microstates C and D. These temporal patterns (weighted by the loadings of the latent variable) were negatively correlated with several mostly negative prodromal symptoms, including social anhedonia (r=-0.441, p=0.014), expression of emotion (r=-0.45, p=0.01), experience of emotions and self (r=-0.37 (p=0.04), and disorganized communication (r=-0.36, p=0.050). Thus, temporal patterns of EEG microstates might be related to the severity of prodromal, negative symptoms in the 22q11DS.

Flexible adaptation to conflicting task demands plays an important role in everyday life and is impaired in many psychiatric disorders. As a prerequisite, cognitive control comes into play when task demands conflict, which may be reflected in brainelectrical activity as enhanced N2 amplitude under conflict (monitoring) in various tasks. Cognitive control may also play a role in Attention Deficit/Hyperactivity Disorder (ADHD), but studies on N2-Enhancement revealed mixed results.

The current study contrasts three task demands tapping conflict monitoring and cognitive control and their relation to ADHD. This was done in a sample of 94 children with ADHD in comparison to 43 Controls, aged 8 to 15 years using a Flanker-Task paradigm by contrasting processing of congruent and incongruent stimuli, and in Continuous Performance Tests by contrasting Go-Nogo demands and processing of additionally presented incongruent stimuli.

All three demands for cognitive control revealed significant N2-Enhancement, but differential ADHD effects thereon: N2-Enhancements in the CPT regarding Go-Nogo and processing of additional incongruent Flankers was similar in ADHD and Controls, while Flanker-Task Congruency revealed medium-sized ADHD effects.

The current results indicate that children with ADHD have medium-sized difficulties with cognitive control during some particular demand (e.g. with Flanker-Task Congruency that requires frequent responding) but not on others (e.g. during response-control in CPTs that require responding in only 10% of all trials). This highlights the importance of clinical studies for understanding cognitive control in different demands, and it may also indicate that moderators like arousal may play an important role for ADHD deficits.

To generate new treatments for ASD it is critical to understand the deviations in brain function from typically developing controls (TD) and to derive robust biomarkers to quantify those. Resting state EEG is a unique tool to explore non-invasively – and across a broad age range – the dynamics of spontaneous neuronal activity. Here we explored the developmental changes in ASD from childhood to adulthood in resting state EEG powerspectrum in the LEAP dataset of EU-AIMS (www.eu-aims.eu), using n=294 high functioning ASD and TD individuals from 6 to 30 years. More specifically, we investigated the alpha rhythm – the hallmark of resting state EEG, linked to sensory-motor and cognitive processes – and the powerspectrum over frequencies from 1 to 32 Hz at the sensor level and in source space. We quantified developmental trajectories with linear mixed effect models, accounting for confounding factors such as gender, IQ and site. Known typical developmental trajectories were recovered: with increasing age the alpha peak frequency increased, its amplitude decreased, relative power in 2-6Hz decreased and relative power 10-32Hz increased. The developmental changes in the ASD group followed closely that of the TD subjects, and accordingly no significant group effects were found. Additionally, no evidence for increased heterogeneity in ASD was found, since the modeled variances in both groups did not differ significantly. Overall, this indicates that the baseline 1 to 32 Hz power matures in high functioning ASD following a typical developmental trajectory.

Deficit schizophrenia (DS) is characterized by the presence of primary, enduring negative symptoms, and has different course, risk factors and clinical features with respect to non-deficit schizophrenia (ND).

Our aim was to investigate differences in white matter connectivity patterns in subjects with DS compared to ND and healthy controls (HC), using probabilistic analysis of diffusion tensor imaging (DTI) data.

Forty-six subjects with chronic schizophrenia (SCZ) and 35 HC, matched for age and gender, were examined using DTI. SCZ were classified as DS (n=9) or ND (n=37) using the Schedule for the Deficit Syndrome (SDS). Further assessments included the Positive and Negative Syndrome Scale (PANSS) and the MATRICS Consensus Cognitive Battery. Connectivity index (CI, % of the probabilistic streamlines originating from a region that reach a second one) and Fractional Anisotropy (FA) of the connections between bilateral dorso-lateral prefrontal cortex (DLPFC), nucleus accumbens (NAcc), amygdala (AMY) and insular cortex (IC) were examined.

We found a reduced CI between right AMY and DLPFC in SCZ compared to HC (p<0,0044), while there were no differences between DS and ND. DS showed an increased CI from right AMY to dorsal-anterior IC compared to ND (p<0,0036). Finally, in SCZ the FA of right NAcc-DLPFC connections directly correlated with PANSS disorganization dimension (p<0.0031).

These findings confirm previous evidences of distinct neurobiological substrates for different symptom dimensions and clinical subtypes of SCZ. Primary and persistent negative symptoms seem to be related to abnormal connectivity of brain regions involved in guiding goal-directed behavior based on experienced value.

Dysfunction of the reward system is probably related to the avolition/apathy domain of negative symptoms in schizophrenia. In particular, structural and functional abnormalities were reported in key regions within the reward system, including the ventral-tegmental area (VTA), the nucleus accumbens (NAcc), the orbito-frontal cortex (OFC) as well as the amygdala (AMY) and the insular cortex (IC).

Our aim was to investigate the white matter connectivity patterns within these regions in male subjects with schizophrenia, using probabilistic analysis of diffusion tensor imaging (DTI) data.

Thirty male subjects with schizophrenia (SCZ) and 17 male healthy controls (HC) matched for age, underwent DTI. SCZ were evaluated clinically with the Schedule for Deficit Syndrome (SDS), Positive and Negative Syndrome Scale (PANSS) and the MATRICS consensus cognitive battery (MCCB). Pathways connecting the AMY and the NAcc with the OFC and IC were evaluated.

Reduced fractional anisotropy (FA) was observed in left AMY-ventral anterior IC connection (p<0.0048), in SCZ compared to controls. This abnormality was negatively correlated with the avolition/apathy (p<0.0023) but not with the expressive deficit scores. SCZ showed also reduced connectivity indices (% of the probabilistic streamlines originating from a region that reach a second one) between right NAcc and medial OFC as compared to controls (p<0.0001). Finally the left NAcc-dorsal anterior IC connectivity index was negatively correlated with working memory (WM) scores (p<0.0013).

Our results confirm that the avolition/apathy but not the expressive deficit domain is related to reward system abnormalities. Distinct alterations seem to underlie cognitive impairment and avolition/apathy.

Introduction. Abnormalities in cognitive functions and motivation are core aspects of schizophrenia. One of the crucial aspects of cognitive impairment is the disturbance of cognitive control, or the ability to flexibly adjust behavior in accordance with one’s intentions and goals. A large literature has emerged focusing on the anterior N2 as a correlate of cognitive control based on motivational value.

Aims. Given the clinical importance of goal-directed behavior impairments in schizophrenia as a strong predictor of functional outcome, we aimed to study the impact of reward- and avoidance-based motivation on cognitive control using event-related potentials (ERPs).

Method. ERPs were recorded during the execution of the "Monetary Incentive Delay (MID)” task in 34 patients with schizophrenia (SCZ) stabilized on second generation antipsychotics and 22 healthy controls (HC). Negative symptom domains (avolition/apathy and expressive deficit), positive and disorganization dimensions were also assessed in SCZ.

Results. We did not observe any group difference in N2 amplitude or latency. In the HC group, N2 amplitude was significantly larger for anticipation of large punishment than reward and for all incentive conditions than neutral one. Unlike HC, N2 amplitude in SCZ did not discriminate motivational relevance. N2 amplitude was not correlated with psychopathological dimensions in SCZ.

Conclusion. Our results suggest that the discrimination of motivational value appears to be impaired in SCZ, independently of psychopathology. Future studies should be aimed to assess whether distinct subgroups within SCZ, in particular those with deficit features, might be characterized by this abnormality.

Long-term schizophrenia (Sz) and first episode schizophrenia spectrum (FE) patients have deficits in processing auditory and visual stimuli evidenced by reduced event-related potentials. Multisensory integration (MSI) is the process by which information from multiple modalities are integrated into a coherent percept. Findings from studies of non-linguistic MSI in Sz are equivocal. No studies have assessed MSI in FE. This study examined MSI by presenting 18 first episode matched healthy controls (FEHC), 26 chronic schizophrenia matched healthy controls (SzHC), 21 FE, and 29 Sz with auditory (A), visual (V), and simultaneous audiovisual (AV) stimuli. Auditory stimuli were presented as groups of 4 tones (1 kHz, 50ms, 5ms rise/fall, 330ms SOA, 750ms ITI). Visual stimuli (small blue circle, 50ms) were also presented in groups of 4. AV stimuli were the simultaneous presentation of the A and V stimuli. Participants were asked to sit silently and attend to the stimuli presented. MSI was calculated by subtracting the sum of the unisensory stimuli from the simultaneous audiovisual stimulus [AV – (A+V)]. MSI amplitude was calculated as the average voltage between 95-115ms and 180-190ms at PO9 and PO10, where previous studies have looked for MSI interactions. MSI at 95-115ms was more negative in SzHC (p=.011) compared to Sz, but no group differences were found in FE and FEHC (p=.968). MSI at 180-190ms was not significantly different from controls for Sz or FE. These results suggest that early MSI is impaired in Sz but not FE, and may serve as an indicator of disease progression.

Primary auditory cortex pathophysiology is linked to auditory deficits and auditory verbal hallucinations in schizophrenia. The aim of this study was to replicate associations between reductions in the magnitude of the mismatch negativity (MMN) response during a passive auditory task and reductions in gray matter volume in Heschl’s gyrus in subjects with first-episode schizophrenia (FESz). Participants included 28 FESz and 28 healthy control subjects matched for age, parental socioeconomic status, IQ, sex, and handedness. Freesurfer was used to segment white matter, gray matter, and pial surfaces from T1-weighted structural 3T MRI data (1mm x 1mm x 1mm). Gray matter was measured in left Heschl’s gyrus (containing primary auditory cortex). Pitch-deviant and duration-deviant MMN responses were measured as the averaged amplitude within a 100-ms window at Fz. In FESz, total gray matter volume in Heschl’s gyrus correlated with the magnitude of pitch MMN (rho = -.38, p <.05) and duration MMN (r = -.44, p <.05). There were no significant correlations between MMNs and Heschl’s gray matter volumes in the healthy control group, and the pathological correlations in FESz were significantly different from healthy controls (Fisher Z p <.05). Smaller Heschl’s gyrus in first episode schizophrenia patients is related to a smaller magnitude MMN for both pitch and duration deviants. This pathological relationship between MMN amplitude and Heschl’s gyrus volume in FESz suggests the presence of pre-psychosis gray matter loss in a subset of patients, and may be useful for tracking disease progression and as an outcome measure of successful interventions

The N1 auditory evoked potential is reduced in long-term schizophrenia (Sz) and in the first episode schizophrenia spectrum (FE). N1 is increased by attention, and this modulation is impaired in Sz. It is not known whether FE can modulate N1 by attention. This study examined N1 modulation by attention (Negative Difference; Nd) in FE, early in disease course. Thirteen FE and 11 matched healthy control (HC) participants heard sounds while watching a silent video. Participants heard repetitious tones in a typical AEP task (1k Hz, 50 ms duration, 5 ms rise/fall, 80 dB), spaced 1050 ms to 1550 ms apart. In one condition, participants were told to ignore tones, while in the other condition participants pressed a button to every 7th tone. Continuous EEG was recorded between DC and 104 Hz. After high pass filtering at 0.5 Hz and ICA artifact correction, data were low pass filtered at 20 Hz, epoched, and artifact rejected, and averages constructed. N1 amplitude was calculated as average voltage between 100 ms and 110 ms at Cz. Of primary importance, N1 amplitude was differentially affected by attention between groups (p =0.002). HC showed larger N1 with attention (p =0.01), but FE did not (p =0.27). This may reflect a long-range functional disconnection between cognitive control cortical areas and auditory sensory cortex early in disease course. Clinically, the lack of attention-related Nd in FE suggests it may serve as a sensitive biomarker for the detection of the schizophrenia prodrome among clinical high-risk individuals.

Auditory verbal hallucinations (AVH) are common in schizophrenia and may relate to abnormal connectivity between auditory cortices. We examined transcallosal auditory cortex tracts in 14 AVH+ first-episode schizophrenia patients (FESz), 15 AVH- FESz, and 23 healthy controls with diffusion spectrum imaging (DSI). AVH+ scored at least a 2 on auditory hallucinations, voices commenting, or voices conversing measured with the Scale for the Assessment of Positive Symptoms (SAPS). Groups were matched for age, parental socioeconomic status, education, IQ, gender, and handedness. A deterministic fiber tracking algorithm identified transcallosal auditory fibers, defined as 1000 fibers traversing the posterior third of the corpus callosum and ending bilaterally in Brodmann’s area 22, Heschl’s gyrus, or planum temporale. MANOVA revealed transcallosal auditory cortex connectivity differences between groups (F(6, 94) = 2.34, p = .038) driven by tract volume (F(2, 49) = 3.46, p =.039) and generalized functional anisotropy (gFA, F(2, 49) = 4.77, p = .013). Pairwise t-tests indicated lower gFA and greater tract volume for AVH+ vs AVH- (p’s < .05). Healthy controls trended towards greater gFA (p = .068) vs AVH+ and tract volume (p = .063) vs AVH-. All other comparisons were nonsignificant (p >.1). Reduced fiber tract directionality indicates less efficient transcallosal auditory connectivity in AVH+ FESz; smaller tract volume indicates potential reduced structural auditory cortex connectivity in AVH-. Interhemispheric auditory cortex functional connectivity abnormalities may underlie AVH in schizophrenia even early in disease course while overall structural connectivity differences may affect AVH- individuals. Interhemispheric connectivity differences may underlie symptom-level phenotypes.

Individuals with long-term schizophrenia (SZ) show reductions in simple mismatch negativity (MMN) to infrequent stimulus parameter deviance, and in complex MMN to infrequent pattern deviance. First episode schizophrenia-spectrum individuals (FE) show less reduction of simple MMN. Complex pattern deviance may be more suitable for elucidating subtle deficits in auditory perception at first-episode, and may be more useful for distinguishing between those with and those without schizophrenia. We measured complex MMN to an extra fourth tone amongst standard groups of three tones (1 kHz, 50 ms duration, 5 ms rise/fall, 80 dB, 330 ms SOA, 800 ms ITI) in 23 SZ), and 20 matched healthy controls (HCSZ), and in 21 FE (within 6 months of first-episode), and 20 matched healthy controls (HCFE). Both HCSZ and HCFE produced two complex MMNs: one early ~150 ms after deviant-onset and one late ~400 ms after deviant-onset. SZ showed significant reductions in the late complex MMN (p=.03, d=0.67), as did FE (p=.01, d=0.85). Both individuals with long-term schizophrenia and individuals at their first-break showed impaired complex MMN, specifically later in deviance detection processing. In contrast with simple MMN, complex MMN may be a more sensitive biomarker of the presence of schizophrenia early in disease course. To assess whether complex MMN has greater sensitivity to detect incipient psychosis, both simple MMN and complex MMN will be measured in prodromal individuals.

Psychotic disorders are highly complex with regard to their symptomatic characteristics and aetiology. About 1-2% of the population are affected by psychosis during their lifetime. Impairments in motor function that occur in psychosis have been associated with aberrant neural activity and structure. It is still unclear whether differences in motor ability in healthy individuals are similarly related to distinct brain activation patterns in motor-related brain areas, suggesting that impairments in psychosis patients are extreme values on a trait continuum. In the present study, we examine the neural underpinnings of motor function on the spectrum from health to psychosis.

We investigate the neural correlates by conducting electroencephalography (EEG) as well as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) in 120 subjects from four different groups: psychosis patients, subjects with a clinical high-risk for psychosis, first-degree relatives of psychosis patients and healthy controls. During MRI and EEG measurement, subjects perform an ankle movement task as well as a biological motion recognition task, using the point light walker paradigm. In addition, physiological markers such as heart rate variability and force variability are recorded.

We hypothesize functional and structural neural abnormalities in the motor areas in relation to the strength of the behavioural disturbances. Our results will provide new insights into the neural basis, as well as the aetiology of motor function in psychosis.

We used a logistic discriminant analysis (LDA) to determine a QEEG predictive model of response to four-week trial of low-frequency transcranial magnetic stimulation (LF-rTMS) on right prefrontal cortex in patients with major depression who failed to previous antidepressant treatment (N=25). Out of the set of 836 variables generated by the Neuroguide software, we selected 12 significant (p˂0.001) variables using a t-test. This number has been then reduced within LDA by means of a stepwise method on 5 predictors (p˂0.05):alpha asymmetry (ASAL) C3-Cz, ASAL C4-Cz , theta asymmetry (TAS) C4-T4, TAS T3 – T4 and theta peak (TP) C4. The LDA model containing these five variables demonstrated correct classification on 88 %. Furthermore, it shows a good overall model fit based on -2 Log Likelihood (p˂0.001). Sequential and eliminating employment of bootstrapping (N=10000) on the original 12 variables confirmed that the QEEG parameters of TAS T3-T4 (p=0.0001), ASAL2 C4-F8 (p=0.01), ASAL1 C3-Cz (p=0.03), ASAL C3-Cz (p=0.04) demonstrate robust resistancy against bias while keeping a satisfactory level of overall classification. Our findings confirm the ability of QEEG data analysis to create a suitable prediction model. An important part of our model is the T3-T4 predictor, which demonstrates both predictive sensitivity and bias resistance. From our model, it can be concluded that higher temporal theta asymmetry (T3 > T4) indicates a significantly higher chance of therapeutic response to rTMS therapy for pharmaco-resistant depression.

Supported by Czech Science Foundation, grant nr. 17-07070S and Ministry of Health of the Czech Republic, grant nr. 16-31380A.

Avolition is a key negative symptom of schizophrenia for which there is no effective treatment. It is correlated to poor functional outcome and real life motivation. Dysfunctions of different motivation processes were documented in patients with avolition. Altered connectivity within dopaminergic (DA) cortico-striatal circuits, involved in motivation processes, might underlie most of these dysfunctions. The highest number of DA neurons involved in motivation circuits are located in the ventro-tegmental area (VTA). In light of these observations, our study investigated relationships between the resting-state functional connectivity (RS-FC) of the VTA and avolition.

We used resting state functional magnetic resonance imaging to study RS-FC in 22 healthy controls (HC) and in 26 patients with schizophrenia, treated with second generation antipsychotics only, divided in high (HA=13) and low avolition (LA=13) subgroups. We also assessed the relationships of VTA RS-FC with avolition, assessed using the Schedule for the Deficit Syndrome.

HA patients, in comparison to LA patients and HC, showed significantly reduced VTA RS-FC with the right (R) ventrolateral prefrontal cortex (VLPFC), left (L) VLPFC, R insula (INS), L INS and R lateral occipital complex (LOC). A reduced RS-FC was found for LA patients, with respect to HC, only in L INS and L VLPFC. Significant negative correlations were found between avolition and RS-FC of VTA with R INS, R VLPFC and L INS.

Conclusion. Our findings demonstrate that avolition in schizophrenia is linked to dysconnection of VTA from key cortical regions involved in retrieval of outcome values of actions to motivate behavior.

Introduction: Cannabis is the most widely consumed illicit substance with an estimated annual prevalence of 182, 5 million people worldwide in 2014. While the effect of cannabis on cognition has already been investigated, these studies mostly employed intravenously delivered, high doses of Δ-9-THC where participants were unable to influence neither the amount nor the speed of intake. Present study adopted a naturalistic design (subjects using their regular dose of their own cannabis the way they would normally do) focusing on selected cognitive domains and their underlying neurophysiology.

Method: Attention, mental flexibility, psychomotor speed (PEBL battery tasks – TMT, CPT) and P300 (auditory oddball paradigm) were measured 30 minutes after cannabis intoxication in 34 occasional, 31 chronic cannabis users and 30 non-using adults. Phenomenology was objectified by Altered States of Consciousness Scale and Brief Psychiatric Rating Scale. THC and THC-COOH were determined from blood analysis 30 and 60 minutes after cannabis intake.

Results: Although cannabis users had significantly higher APZ and BPRS scores, the groups did not differ in terms of their cognitive performance. Likewise, no between-group differences were observed in terms of P300 amplitude, latency or area under curve. Further analysis only revealed smaller P200 amplitude and shorter N200 latencies in occasional users when compared to non-using controls.

Conclusion: Unimpaired profile of selected cognitive functions both at behavioral and neurophysiological level was an unexpected finding suggesting a possible tolerance to the cognitive-impairing effects of cannabis provided the subjects are able to control the rate of intoxication.

Altered resting-state EEG source functional connectivity in Autism Spectrum Disorder

Introduction: Neurexan®, a medicinal product sold over the counter (OTC), contains passionflower, oats, coffee and zinc valerianate. Neurexan® has been previously investigated in patients with symptoms related to acute stress, nervousness/restlessness, and insomnia. The underlying neuronal mechanisms that lead to the reduction of those symptoms are less clear. The anterior cingulate cortex (ACC) and the Amygdala are the two areas important in stress reaction. Previous studies showed that especially the dorsal ACC (dACC) influences the generation of autonomic arousal. Additionally, it was found that the dACC is activated under cognitive stress. Thus, the dACC seems to be an important area controlling stress reactivity. We hypothesize Neurexan® to induce changes in the activation of dACC and associated areas during a stress task.

Methods: The treatment effect of a single dose was investigated using a randomized, placebo-controlled, double-blind, two-period-crossover design on thirty-nine healthy males. The stress response was induced using the ScanSTRESS which uses arithmetic tasks as well as mental rotation tasks.

Results&Conclusion: We found higher activation during psychosocial stress (stress > control; rotation and arithmetics together) in the anterior insula, premotor area bilaterally, angular gyrus, occipital lobe, and cerebellum. Paired-t-test analysis showed a significant cluster in the region of interest right dACC for the contrast placebo > drug in rotation stress > rotation control after correcting for multiple testing in the ROI. A single dose of Neurexan® significantly reduces right dACC activation during psychosocial stress compared to placebo.

Relation between Cognition and Resting-state EEG source functional connectivity in Schizophrenia

The serotonergic hallucinogen psilocybin and its active metabolite psilocin nowadays receive a lot of attention as a research tool for modeling psychosis. The aim of the study was to assess psilocin-induced changes in quantitative EEG (QEEG) in rats in order to explore the role of different serotonergic receptors in psilocin action.

EEG was recorded in freely moving rats after implantation of 12 active electrodes onto the surface of the cortex. EEG power spectra (local synchronization) and coherence (long projections) were analyzed comparing the drugs’ effect in time to the baseline record. Only EEG traces corresponding to behavioral inactivity were included in the analysis. We used psilocin, selective 5HT receptor antagonists and antipsychotics.

Psilocin generally decreased both EEG absolute spectral power and EEG coherences. The changes in spectral power induced by psilocin were normalized partially by all substances used, mainly in the lower frequency bands. However, only 5HT1A and 5HT2A antagonists partially normalized the psilocin-induced decrease of EEG coherences. The specific QEEG pattern of each substance and the temporal dynamics of QEEG changes will be presented.

Psilocin-induced changes in QEEG in rats are very similar to our human data with psilocybin and are in accordance with the concept of psychosis as a disconnection syndrome. All the specific 5HT antagonists and both antipsychotic drugs specifically affected the EEG spectral power induced by psilocin. Surprisingly, only 5HT1A and 5HT2A antagonists were able to partially reverse psilocin-induced disconnection.

This study was supported by LO1611/NPUI, MICR VI20172020056; Progres Q35; and European Regional Development Fund.

Alzheimer’s disease (AD) is a neurodegenerative disorder associated with the progressive dysfunction of cognitive ability. Previous research has indicated that the default mode network (DMN) is closely related to cognition and is impaired in Alzheimer’s disease. Because recent studies have shown that different frequency bands represent specific physiological functions, DMN functional connectivity studies of the different frequency bands based on resting state fMRI (RS-fMRI) data may provide new insight into AD pathophysiology. In this study, we explored the functional connectivity based on well-defined DMN regions of interest (ROIs) from the five frequency bands: slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), slow-2 (0.198-0.25 Hz) and low-frequency oscillations (LFO) (0.01-0.08Hz). We found that the altered functional connectivity patterns are mainly in the frequency band of slow-5 and slow-4 and that the decreased connections are long distance, but some relatively short connections are increased. In addition, the functional connectivity fingerprint of the DMN in AD is frequency dependent and differs between the slow-5 and slow-4 bands. Mini-Mental State Examination (MMSE) scores were significantly correlated with the altered functional connectivity patterns in the slow-5 and slow-4 bands. These results indicate that frequency-dependent functional connectivity changes might provide potential biomarkers for AD pathophysiology.

One character of autism spectrum disorder (ASD) is restricted and repetitive patterns of behavior, interests, or activities. The characters are related to the abilities of selection of task-relevant information and inhibition of task-irrelevant information. The abilities are proposed to be related to phase synchronization between the distant motor and sensory areas in human electroencephalography (EEG) studies. It is however, not clear about the relationship of the synchronization with ASD symptoms. To address the issue, we measured EEG data during two types of simple motor-response tasks (an auditory-motor response task (AM) and a single visual-motor response task (VM)) and two types of select-motor-response tasks (an auditory-select-motor response task (ASM) and a single visual-select-motor response task (VSM)). Behavioral results showed that the response time for ASM and VSM tasks was longer than that for the AM and VM tasks. Interestingly, the prolong times were positively correlated with the ASD symptoms. The time-frequency analyses for the EEG data showed that the alpha phases between the motor and visual areas during only the VM and VSM tasks and between the motor and auditory areas in only the AM and ASM tasks. These task-relevant alpha phase synchronizations were decreased in the participants with high scores of the ASD symptoms. These results suggested that the ASD deficits would be associated with the decrements of the alpha synchronizations between the motor areas and the task-relevant sensory areas.

Quality of the raw data crucially impacts the validity of analyses and interpretation of scientific results obtained from electroencephalography (EEG). Therefore, regular assessments of EEG data quality are essential to ensure that established standards are met, particularly for multicenter studies.

EEG data are collected from N=68 patients within an ongoing multicenter study ESCAlife (involving Bochum, Homburg, Köln, Mainz, Mannheim, Marburg, Oldenburg, Rostock, Tübingen, Würzburg) of children, adolescents and adults (6 - 45 years) with ADHD in the ESCAbrain subproject. Resting state eyes open and eyes closed (4 minutes) EEG data are collected to identify potential predictors of treatment response. The EEG is recorded using a 22-channel EEG cap (Brain Products) and a sampling rate of 256 Hz (DC-70 Hz). As ADHD patients are prone to EEG artifacts, a regular data quality assessment focused on typical artefacts was conducted.

For data processing, the software BrainVision Analyzer (Brain Products) is used. The percentage of artifact-free epochs is calculated for each dataset as an index of data quality. Furthermore, the percentage of blink-artifact-related ICA-components is determined and compared across study centers and participants. Age of the participants, ADHD symptom severity, as well as the different study sites (comprising potentially relevant variables such as the electromagnetic environment, the training, and the experience of the staff with EEG recordings) are explored as relevant factors that might influence data quality.

Finally, corrective actions are discussed that were adopted to improve data quality.

This work was supported by the research consortium on ADHD, ESCA-Life, funded by the German Federal Ministry of Education and Research (FKZ 01EE1408E).

Introduction

The neurophysiological effects of neurofeedback (NF) as a treatment for children with ADHD are still unclear. This randomized controlled trial analysed EEG power spectra before and after 25 sessions of slow-cortical potentials neurofeedback compared to electromyogram (EMG) biofeedback as a semi-active control group.

Methods

Children with ADHD (n=150, age 7-9 y) were randomly assigned to 25 training sessions of SCP- neurofeedback or EMG feedback. The neurofeedback group had to regulate slow EEG activity at Cz, while the EMG-feedback control group had to regulate relative electromyographic activity of the musculus supraspinati. Each training session consisted of three runs with visual feedback and one run without feedback which aims to transfer the learned skills into daily life. EEG power spectra topographies (21 channels) during resting with eyes closed from pre and post-intervention was analysed.

Results

Both interventions showed comparable reductions of theta and alpha activity during the eyes closed condition which was more prominent at central and posterior scalp localisations. A time by group interaction was found for beta frequencies over the central cortex. The EMG control group showed reduced beta power after the training sessions.

Conclusion:

This study provides evidence for nonspecific neurophysiological changes after neurofeedback and EMG training. The general reduction in both groups of theta and alpha power could reflect increased attention although effects of maturation of the brain cannot be excluded. The specific reduction in beta power after EMG biofeedback could be related to a better motor control and inhibition.

Here, we present the first online BCI based on deep learning (DL). Application of DL has so far focused on computer-vision tasks. Recently, the BCI community is also developing an increasing interest for DL. We have previously shown DL using convolutional networks (ConvNets) to be competitive with the current state-of-the-art algorithms for motor-execution offline decoding. The present work extends the application of ConvNets to online decoding. We acquired EEG in five healthy subjects (S1-5) each performing five mental tasks: right hand finger tapping, both feet toe movement, rotation of an L shape, generation of words starting with the same letter, and rest. We performed training and feedback with a menu-based graphical user interface (GUI). During offline training, actions with the GUI were controlled by the paradigm. We then used the raw offline EEG data to train hybrid networks combining a deep ConvNet with a shallower ConvNet. Test evaluation on the last two runs (each 10min) of the EEG data yielded class averaged offline decoding accuracies of 70.7%, 49.2%, 73.1%, 58.8% and 32.6% for each subject, respectively. There were no significant differences between offline deep ConvNets and Filter Bank Common Spatial Patterns (FBCSP) results, confirming that deep ConvNets are competitive with FBCSP. Using self-developed visualization techniques, we show that the ConvNets extracted neuronal features in the delta, alpha and beta bands. During online feedback, all subjects successfully navigated the menu structure with 5 actions (select, down, back, up, wait) using the trained ConvNets, demonstrating the feasibility of DL-based online BCI control.

Based on recent methodological and technical progress, as well as on an increasing knowledge about the neural correlates of behavior and cognition, brain-computer interfaces (BCIs) are attracting growing interest in both the scientific and medical communities. According to the survey from several articles, a portable BCIs is needed for practical application. Now, most of the BCI is based on Personal Computer (PC), which is bulky and not portable. So in this paper, a BCIs, which is based on embedded processor, is designed.

The main content of this paper include embedded system building, the design of USB driver and BCI application program. And the embedded system building includes the cutting of embedded Linux kernel, file system’s making and the installation of the QT library. The function of the USB driver is to transfer EEG data from EEG amplifier to embedded system. The BCI application program includes a waveform display program and a BCI program. And the BCI program implements the function of visual stimulation, EEG data acquisition, feature extraction and pattern classification, etc.

Finally, in order to verify the performance of the embedded BCI system, we tested the whole system and its core module. The average accuracy of the BCI system is 82.8%, and the average information transfer rate is 19.9 bits per minute. The test results show that the BCI system can meet the demand of basic medical application.

Generating and monitoring motor actions are key functions of the human nervous system. Brain disorders can lead to motor impairments that limit affected individuals in their ability to communicate and interact with their environment. Brain-computer interfaces (BCIs) can be used to replace lost or improve impaired functions. Despite of the usefulness of current non-invasive BCIs, there is still room for improvement in respect to their performance and specificity. To provide more advanced features for BCIs, we are using electroencephalographic (EEG) source imaging for investigating cortical dynamics linked to motor functions.

We have studied repetitive movements [i.e. gait (N=10) and finger movements (N=18) ], distinguishing movement state-related from movement phase-related activities. Movement state-related activities are reflected by sustained suppression of mid beta (18–24 Hz) and enhancement of high gamma (60-80 Hz) oscillations during movement. These activities are suggested to represent upregulation of cortical excitability in regions representing the limb that is moved. Movement phase-related activities appear as dynamic amplitude modulations, most pronounced at high beta (24-40 Hz) frequencies in prefrontal and bilateral sensorimotor areas. These patterns are significantly related to the time course of repetitive movements. Because we identified the frequency spectra and spatial sources of movement state- and movement phase-related activities to be different, we suggest that they represent different functional large-scale networks providing independent information. In our latest work, we utilize these two network specific EEG source features to improve BCIs in decoding brisk movements.

Functional brain connectivity (FC) is a marker of brain state. In last decade there is a growing interest in examination of its dynamics which is connected to the synchronization and desynchronization of neuronal populations activity. To better understand these processes we analysed resting state simultaneous fMRI/EEG data. The aim was to answer whether dynamics of FC (as seen by fMRI) is reflected in EEG power fluctuations.

We measured 50 healthy controls with 1.5T MRI and 30 channel MR compatible EEG during resting state. Preprocessing was done in the standard way in SPM8 and BrainVision Analyzer software. BOLD data were decomposed by Group ICA into 20 stable components (tested for inter/intra class stability). Dynamic functional connectivity (DFC) was computed on component's time series using sliding rectangular window and Pearson correlation coefficients. DFC state vector was then extracted for each component pair based on values of correlation coefficients (3 states - synchronous, asynchronous and no communication).

Simultaneously, EEG data were analysed by Parallel Factor Analysis (PARAFAC), where 3D spectrogram (3-way array with modes of electrodes, time and frequency) was decomposed. Resulting components contain 3 signatures - spatial, temporal and spectral.

Finally, ANOVA tests were performed to assess relations between DFC state vectors and fluctuations of EEG spectral patterns.

Our previous findings revealed relation between fluctuations of EEG spectral patterns and hemodynamics of large scale brain networks (LSBN). We present results, which show that the relation exists also at the level of DFC among LSBN.

Long-term exposure of humans to unusual sensory spaces is effective to see the adaptive strategy for an environment. Because little had been examined about adaptation to left-right reversed audition, we constructed a left-right reversed stereophonic system using wearable devices and examined the adaptation effects on audiovisual spatial integration, as reported in BaCI 2015. However, the adaptation effects on auditory-motor spatial coordination still remains unknown. In a way similar to our previous study, we asked two participants to wear the system for 4 weeks, and tested the effects with MEG every week. The MEG responses were measured under the selective reaction time task, where they immediately distinguished between sounds delivered to either the left or the right ear with the index finger on the compatible or incompatible side. The N1m intensities for the response-compatible sounds tended to be larger than those for the response-incompatible sounds until the third week but decreased on the fourth week, which correlated with the initially shorter and longer reaction times for the compatible and incompatible conditions, respectively. Moreover, Granger causality analysis showed disruption of the auditory-motor connectivity in the second week, with the largest N1m intensities and the longest reaction times, irrespective of compatibility. We thus conclude that long-term exposure to the left-right reversed audition optimizes the auditory-motor coordination based on the new rule, where the transient unstable situation leads subsequent modulation of early auditory processing.

The vestibular system has a critical role in sensing body tilt, gravity direction, and acceleration input, and receives information from other sensory systems such as the visual system. Because of the technical difficulty in designing a neuroimaging experiment for testing the vestibular system in humans, however, little is known about the interaction between vestibular and visual information. Here, we tested the visual-vestibular interaction using EEG with two apparatuses: a virtual reality head-mounted display and an inversion table. Videos of falling scenes in either upward or downward direction were randomly displayed by the VR device, while a participant's body tilt was alternately manipulated for every eight video plays in either an upright or inverted manner by the inversion table. Therefore, four combinational conditions were established with regard to the orientation of retinal images and the direction of gravity. Event-related potential analysis revealed that continuous attenuation of visual activity was observed from 100-150 ms after the start timing of falling in the inverted body condition as compared with the upright condition, irrespective of the visual falling direction. Moreover, Granger causality analysis showed feedback connection from the temporoparietal vestibular area to the visual area.These findings indicate that visual activity is suppressed by unusual vestibular information and that visual-vestibular interaction begins at a relatively early stage of visual processing.

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique. There exists literature on tDCS effects that can modulate brain cognitive functions (e.g., mental arithmetic skills) in the posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC). In this study, in the hope to find other stimulation effects, we investigate tDCS-induced brain activity with magnetoencephalography (MEG). Fifteen healthy subjects participated in this experiment. All subjects conducted complex mental multiplication task on three different days under different stimulation conditions (anode, cathode, and sham). The tDCS (Starstim) was applied with current of 1.5mA for 25 minutes. Active and reference electrodes were placed on left PPC and right DLPFC, respectively. 152-channel whole-head MEG (KRISS) was recorded before and after tDCS. We observed significant differences in event-related (de)synchronization (ERD/ERS) of alpha power in the post-tDCS conditions. In the left temporo-parietal area, alpha ERD increased in anodal-tDCS than sham. Furthermore, mild increase of alpha ERS in the right centro-parietal area was observed in cathodal-tDCS compared to sham. The results show that anodal-tDCS over the left PPC causes alpha ERD in the left temporo-parietal area, which is the primary feature of complex problem solving. Alpha ERS (associated with cortical idle) appears in the right centro-parietal area by cathodal-tDCS. Based on such observations, it is believed that the alpha ERD on the left temporo-parietal area may be an evidence of tDCS-induced brain activity changes during the task.

*This work was supported by the NRF of Korea (2016R1A2B4010897) and GRI grant funded by the GIST in 2017.

Dry electrodes can improve the viability of electroencephalography (EEG) acquisitions, contributing to an increased use in established and emerging fields of application for EEG. We recently developed and successfully validated a novel pol-ymer-based flexible dry multipin electrode. Dry electrodes rely on a direct, stable contact to the scalp and therefore pose specific requirements for the used cap system. Our investigations emphasize the need for adaptive caps and electrode de-signs ensuring easy, comfortable application and reliable adduction. We propose a novel modular, adaptable cap system for rapid EEG, implementing the specific requirements of dry electrodes.

Our proposed cap system is based on two independent components: a headband and a butterfly-shaped central cap mod-ule. The headband enables adaption to the individual head circumference, integrating frontal, temporal and occipital elec-trodes. The central module enables adaptation to sagittal and coronal width as well as height of the head, carrying central and parietal electrodes. The modules are based on a semi-flexible, washable textile. 64 electrodes are arranged in a quasi-equidistant layout to allow application of state-of-the art methods for artifact removal and source localization.

We present our assessment of electrode and cap requirements, the cap design and preliminary results of the concept val-idation. For our proposed electrode shape, we’ve identified an optimal adduction force between 2-3 N. Adapted pin height at frontal and frontal-temporal areas contribute to improved wearing comfort. The cap system enables rapid application with preparation times below 10 min. The separate modules enable adaptation of the cap to different head shapes.

Social norms are crucial to any society’s functioning, but social norm compliance is characterized by heterogeneous types of behavior. Here, we examined neural traits to uncover the sources of behavioral types in compliance with the fairness norm. Participants played a distribution game in which they decided how much money to share with an anonymous partner in two conditions; i) no consequences for an unfair offer, ii) possible sanctions for an unfair offer. Cluster analyses revealed distinct types of people: voluntary compliers, who follow the fairness norm of an even split in both conditions, sanction-based compliers, who follow the fairness norm only in the possible-sanctions condition but share little in the no-consequences condition, and non-compliers, who never follow the fairness norm and share little in both conditions. Source-localization analyses of resting-state EEG revealed that voluntary compliers are characterized by lower baseline delta activity in the right TPJ, compared to the two other types. Sanction-based compliers are characterized by higher baseline beta3 activity in the DLPFC, compared to non-compliers. These findings are the first demonstration of the sources of three heterogeneous types in social norm compliance. Discussion focuses on the potential roles of the TPJ and DLPFC.

Fatigue may cause a decrease in mental and physical performance capacity, which is a serious safety risk for the drivers in the transportation system. How to visualize and detect the potential implicit risk already draws common concern. Here, we proposed a new spatio-temporal interaction approach for analyzing electroencephalpgram (EEG) signals during driver fatigue to maintain the reliability of fatigue-related information in different dimensions. The methodology began with construction of functional brain network to integrate functional interactions between different brain regions. With clustering on the brain network, the essential part of the spatial information was retained on fewer network’s nodes to reduce the fluctuations in spatial dimension. Subsequently a nonlinear parameter, wavelet entropy, was computed within a sliding window from the selected nodes to extract the temporal features. Finally, the drivers’ spatio-temporal matrices were created to find the fatigue patterns. The experimental results demonstrated that rhythmic alpha activity spread from the frontal node to parietal node and presented a gradient distribution feature (across frontal, central, and parietal regions) in space domain, though its energy fluctuation occurred with the accumulation of fatigue. In time domain, the features from the selected spatial loci of EEG had a certain synchronized decreasing trend, which revealed brain activity complexity reducing. In addition, most inflection points (81.25%) gained from the temporal features can match the points marked by the subjects, which showed the method’s potential value for the analysis of fatigue mechanism and the application of fatigue detection.

Artefactual latency jitter in ERP study: a simulation study

Gamma band oscillation during Sternberg Task

The Dual Mechanisms of Control theory offers to divide cognitive control in two main strategies: proactive and reactive control (Braver et al., 2007). Proactive control is defined as the ability to anticipate and maintain in working memory information necessary to succeed a task, while reactive control is representative of inhibitory mechanisms triggered by an external cue. The AX-CPT context processing task has become a popular paradigm to examine the use of proactive and reactive control strategies. However, the neurophysiological correlates, such as Event-Related Potentials (ERP’s), of cognitive control strategies in the AX-CPT task remain understudied.

This explorative study investigated the ERPs of 100 young and healthy participants confronted to an AX-CPT task. Each subject filled multiple questionnaires assessing personality trait (impulsivity), clinical traits (anxiety, depression, addiction) and eventual medical and psychiatric history along with a neuropsychological testing assessing working memory, sustained attention and inhibition.

Preliminary results on the respective influences of clinical and cognitive variables on both performance and ERPs parameters of proactive and reactive control will be presented.

Sleep spindles are hallmark of electrophysiological activity in the non-rapid eye movement (NREM) sleep stage 2. Numerous studies about spindles have been reported so far and several functional roles of spindles have been identified. However, sleep spindle identification has been commonly performed by expert’s visual inspection and it is quite time consuming. Thus, various automatic spindle detectors have been proposed, however, it is hard to expect stable performance of detectors because of subject-varying characteristics of electroencephalography (EEG). Here, we applied a popular automatic spindle detector to a sleep spindle database to verify a necessity of subject-specific optimization. The spindle detector is based on a constant threshold scheme, which selects the threshold from a distribution of the root mean square (RMS) values of the NREM sleep EEG epochs. We considered window size and step of epochs as optimization parameters as well as a percentile of RMS distribution for threshold selection. We selected four subjects (among eight) who have reasonable number of spindles in the database. For two subjects, significant differences in detecting performance between the detector with typical parameter and the subject-optimized detector in two subjects were found (for example, F1-score changed from 0.32±0.02 to 0.36±0.03 with p-value<0.000001). With these preliminary results, subject-specific optimization scheme for automatic spindle detectors may be compelling. Further investigation on real-time spindle detector for sleep modulation study is under way.

*This work was supported by IITP grant funded by Korea government (No. 2017-0-00451), GRI grant funded by the GIST in 2017, and NRF of Korea (2016R1A2B4010897).

A deqi is known as a phenomenon that occurs by acupuncture stimulation, and play an important role in the acupuncture treatments. However, there is no certain evidence on the mechanism and expression of deqi. In our previous studies, we find out that temperature and touch sensations are related to deqi sensation. Here, in this study, we focused on correlation between individual sensitivity of those sensations through quantitative biomarkers via analyzing electroencephalogram (EEG).

The number of subjects of clinical trials was 30, half male and half female. 64 channels of EEG are measured simultaneously while measuring the sensitivities of cold, hot, touch, and deqi. The analyses of the data are performed in three way: 1) correlation between sensitivity of sensations; 2) changes of ratio of sensorimotor rhythm wave (SMR; 13~15Hz frequency band), which is known as related feature of EEG for sensations; 3) coherence analysis among frontal, temporal, occipital, and parietal lobes.

As results, correlation between temperature sensitivity, especially the cold, and deqi sensitivities, is appeared in all three analyses. There were high correlation between temperature and deqi sensitivities. Same patterns in relative power of sensorimotor rhythm wave and connectivity trends of SMR coherence at part of channels in parietal lobe are appeared in groups of cold sensitive and deqi sensitive.

This study aims to check the impact of acupuncture manipulation caused by breathing on the peripheral nervous system, using a somatosensory evoked potential (SEP) measurement method in EEG study. The subjects were 7 healthy men and women aged 19-35. For the purposes of the experiment, which was conducted using deep respiration with a two-week washout period, the subjects were divided into three groups, i.e. those subject to Bre-acupuncture manipulation (BAM), those subject to acupuncture manipulation (AM) without regard to respiration, and those subject to only deep breathing (DB), and were subjected to acupuncture manipulation of acupuncture point LI4 ten times. SEP measurement was carried out for 5 minutes, applying 2 HZ-pulse electrical stimulation to the thumb and the little finger of the hand acupunctured with 64-channel EEG, alternately. The verbal rating scaling system (VRS) was used in each experiment before and after acupuncture manipulation. The measurement results show evoked potential amplitude in the range of 40-60ms in the order of BAM<AM<DB, and that the difference in VRS before and after acupuncture stimulation was found in the order of BAM>AM>DB. This shows that acupuncture manipulation increases the pain threshold in the nervous system and that it is more effective when performed simultaneously with deep respiration-based control of the autonomic nervous system. The results of this study indicate that acupuncture-based treatment can produce a higher effect by using a patient’s breathing.

FMRI studies have shown that large-scale functional networks are inherently active in the brain at rest. Several distinct resting-state networks (RSNs) have been attributed to different functional states, and shown to be non-stationary in time, but partitioned into stable epochs (Zalesky, 2014). Periods of stable activity have also been robustly described in EEG recordings at rest, albeit on a faster temporal scale (~100ms). They appear recurrently in a reduced number of quasi-stable electrical field topographies, called microstates (Lehmann, 1980). A recent EEG-fMRI study has identified a relation between EEG microstates and fMRI RSNs – namely auditory, visual, salience and attention networks (Britz, 2010). The next step towards a more direct demonstration that different microstates reflect different functional (mental) states would be to show that well-defined cognitive tasks specifically modulate certain states, both in EEG and fMRI. In this pilot study, we examine whether distinct cognitive tasks can differentially manipulate specific EEG microstates and fMRI RSNs. A group of healthy subjects underwent high-density EEG and 7T-fMRI in three distinct paradigms: eyes-closed rest (6min), autobiographical memory retrieval induced by images of personal past episodes (15min), and mental arithmetics (serial subtraction, 15min). Consistently across subjects, EEG microstate analysis revealed task-specific alterations of microstates C and D, previously associated with the salience and attention RSNs, respectively. These results support the possibility of modulating specific electric and hemodynamic RSNs by appropriate cognitive tasks, corroborating their correspondence to specific mental states. Resting-state analysis of the fMRI data will be further performed to confirm this association.

Auditory mismatch negativity (MMN) is believed to be an electrophysiological marker of pre-attentive cognitive processing and it´s deficit is suggested as schizophrenia endophenotype. MMN is usually registered in paradigms with tones or sounds but paradigms using more complex and natural auditory conditions i.e. modified speech or deviant phonemes, are less common.

In our study EEG data were collected from 17 healthy volunteers and 17 schizophrenia patients. 300 five-to-seven-word sentences in Czech language were presented to subjects by headphones. A phonetic "error" (replacement of a diphone in a word by a diphone from another word) appeared randomly in 150 sentences. Subject´s attention was distracted by watching a mute video.

We observed MMN approximately 200ms after "error stimulus" with it´s maximum in fronto-central electrodes in both groups. Schizophrenia patients showed reduced MMN compared to healthy volunteers.

There are two main results of this study. First, we validated that MMN can be reliably elicited in a paradigm based on natural conditions that the human speech is. Second, we showed that this method is sensitive to the pre-attentive cognitive processing deficit in schizophrenia.

This work is supported by Ministry of Health of the Czech Republic, grant nr. 15-29900A, ED2.1.00/03.0078, LO1611/NPU I, MICR VI20172020056, MH CZ - DRO (NIMH-CZ, 00023752) and PROGRES Q35.

An important process underlying meditation and its benefits involves the regulation of attention. Although the two main meditation categories – open-monitoring meditation (OMM) and focused-attention meditation (FAM) – are associated with different benefits and attentional processes, direct comparisons between the attentional neural mechanism of FAM and OMM are rare. This study uses magnetoencephalography (MEG) recordings in 12 expert meditators to compare FAM and OMM by assessing (i) source spectral power, (ii) seed-based functional connectivity of key regions in attention, (including anterior cingulate cortex, dorsolateral prefrontal cortex and the thalamus) and (iii) graph theory metrics that describe brain-wide efficiency of information processing. We reconstructed the source space using minimum norm estimate and computed spectral power and functional connectivity in multiple frequency bands (delta, theta, alpha, beta, gamma) using a custom-designed python-based MEG analysis pipeline (NeuroPycon). The results reveal unique patterns of neural processes specific to FAM or OMM. Among other things, compared to FAM, OMM appears to be characterized by enhanced small-world network properties. By contrast, FAM exhibits greater functional connectivity between the anterior cingulate cortex and frontal regions. These findings shed light onto the mechanisms that potentially mediate the different behavioral and attentional capacities associated with each of the two meditation techniques. Our results are discussed in the context of previous behavioral and fMRI studies on meditation and attention.

Here we present pilot data where we administered tACS (20Hz and 140Hz) and tRNS over the primary motor cortex of healthy children/adolescents and adults to observe possible influences on the corticospinal excitability. Based on previous studies (Minhas et al, 2012; Moliadze et al, 2015) we hypothesize that the effects of tACS will differ in pediatric population compared to adults.

Methods: 20Hz and 140Hz tACS, tRNS and sham stimulation with 1 mA were applied for 10 minutes on the left M1HAND (mean/SD age: children and adolescents 13,1 ± 2,56; adults 25,23 ± 3,47) in a randomized order. Electrical stimulation was delivered by a battery driven stimulator through conductive-rubber electrodes. MEP were measured by TMS before and after the stimulation (0, 30 and 60 minutes).

Results/Conclusion: In all subjects electrical stimulation was well tolerated.

In both groups 1mA tRNS as well as 140Hz tACS resulted in a significant increase of MEP amplitudes compared with baseline recordings and sham stimulation. However, the increment at tRNS starts with a delay of 30 minutes post stimulation in children.

Interestingly, 20Hz tACS leads to a slightly continuing increase of MEP in adults starting 30 minutes after stimulation which is contrary to the results of other studies (Cappon et al, 2016; Wach et al, 2013). In children 20Hz tACS seems to have an inhibitory tendency after one hour.

Based on our preliminary results the electrical stimulation protocols have to be optimized according to age by planning studies in pediatric population.

Anxiety is a common emotion, but when experienced beyond a certain threshold, it may symptomatically compromise cognitive and functional performance. The LIFECOURSE ERC project establishes a multilevel developmental framework utilizing EEG in this context to examine the influence of stress on diverse physiological, emotional, and behavioural outcomes among adolescents. In the reported pilot investigation, we examine the potential interplay between anxiety and cognitive neurodynamics defined by EEG.

University students between the ages of 22 and 29 years (n=32) underwent anxiety assessment defined by emotive and semantic feedback in a designed psychological questionnaire. EEG was acquired, using a 32-channel wet electrode cap, over the duration of several neurocognitive tasks: object naming, mental rotation, antonym generation, and spatial frequency assessment. A Matlab Graphical User Interface (GUI) was developed order to perform statistical assessment of event-related potential (ERP) and power spectral analyses. The GUI was designed to handle both EEG (*.cnt) and spectral files (*.asc). The program likewise allowed for comparative assessment within a single subject, between two different subjects, or against the entire iteratively-updated cohort. Differences in task-related regional hemispheric alpha asymmetry patterns were found between subjects with highest and lowest anxiety levels. This was most evident in left temporal and occipital electrodes. Likewise, changes in central midline delta, along with frontal, central, and occipital theta frequencies were identified. Finally, in the beta band, differences were primarily evidenced in frontal, right occipital, and central midline areas. Altogether, these changes suggest differences in language, motor, and somatosensory cortical activity.

Preclincial work suggests that sleep promotes global downscaling of overall synaptic strength (homeostatic plasticity) and the consolidation of long-term potentiation (LTP) of task-specific synapses (associative plasticity). Here we use electroencephalography (EEG) and transcranial magnetic stimulation (TMS) as non-invasive indices of homeostatic and associative synaptic plasticity in healthy humans before and after brief periods of daytime sleep and wakefulness (repeated measures sleep laboratory study, 14 healthy participants, 5 females, 9 males, 18–30 years). We demonstrate indices of decreased overall synaptic strength (wake EEG theta activity) and increased input-specific synaptic strength (paired associative stimulation (PAS) induced changes in motor-evoked potentials) after sleep compared to wakefulness. The increase in input-specific synaptic strength was positively correlated with EEG slow wave activity (1-4 Hz) over the respective motorcortical area (M1). Our study supports the notion that slow wave sleep orchestrates homeostatic and associative synaptic plasticity, believed to be the neural basis for adaptive behavior, in humans.