On behalf of the WPA “Psychiatric Electrophysiology” Section, we would like to submit the following symposium proposal to debate about the usefulness of different electrophysiological tools in psychiatric daily clinical practice. In this view, Pr. Pogarell (Chair of the Section, Germany) will present a general overview about the role of the standard electroencephalogram and the use of event-related potentials (ERPs) in the diagnosis and the management of psychiatric disorders. Then, three more specific communications will follow, illustrating the impact that different neurophysiological tools may have in facing with specific mental diseases. First, Pr Salisbury (Editor-in-Chief, Clinical Electroencephaolgrapy & Neurosience, USA) will debate about the potential role of neurophysiological biomarkers for detection of incipient schizophrenia. Then, Dr Campanella (ECNS Roy John Award 2015, Belgium) will focus on cognitive ERPs biomarkers of relapse in addictive disorders. Finally, Dr Baeken (member of the Multidisciplinary Research Partnership on the Integrative Neuroscience of Behavioral Control, with a focus on Psychiatry Imaging) will illustrate the impact of neuromodulation (tDCS) and/or theta burst stimulation in major depressive disorders.

The neurobiological characterization of patients is a major issue in psychiatry and can help to facilitate diagnostic and prognostic decisions. Furthermore, pathophysiological hypotheses with respect to brain functional activity provide a scientific basis for therapeutic approaches. The assessment of brain activity at rest (EEG) or upon stimulation (ERP) and the investigation of underlying neurochemical properties allow a brain functional characterization of psychiatric disorders and thus the monitoring of treatment effects. Diagnostic, therapeutic and predictive implications of these techniques will be discussed.

Psychotic disorders are neurodevelopmental disorders with active pathology prior to psychotic break. There is no test to detect disease presence prior to psychosis. Among clinical high risk individuals with attenuated symptoms (CHR) only 20-30% will suffer full psychosis. Thus, identification of true prodromal individuals is crucial for early intervention to prevent psychosis. We have developed several putative electrophysiological biomarkers of disease presence, all impaired at first psychosis. These include complex mismatch negativity (cMMN), N1 modulation with attention (Nd), emitted P3b, and the auditory segmentation potential (ASP). Current work in CHR will determine their utility as biomarkers predictive for psychotic disorders.

Dr. Campanella (University of Brussels, Belgium) will highlight an event related potential (ERP) study testing whether the inhibitory No-Go P300 and/or the oddball P300 components can help in recently detoxified alcoholic patients to predict which ones are at higher risk of relapse within the 3 months following detoxification. The impact of anti-craving medication (naltrexone, acamprosate, baclofen) vs. placebo will be discussed.

Pr. Baeken will talk on the impact of accelerated high frequency rTMS on the GABA system in treatment resistant depressed patients. The observed GABA concentration increases after real stimulation suggests that the immediate therapeutic effects of aHF-rTMS are mediated through a locally increased GABAergic inhibitory neurotransmission.

This symposium will present recent findings on multimodal imaging, EEG and MRI, in high-risk groups for psychosis and schizophrenia. In the high risk groups, we will have the presentation by Christoph Mulert as well as two presentations on biomarkers for psychosis in 22q11.2 Deletion Syndrome by Maria Carmela Padula and Tonia Rihs. Armida Mucci will present recent findings on biomarkers of negative symptoms in schizophrenia. The aim is to address how EEG and MRI neuroimaging can contribute to the search for biomarkers of psychosis and negative symptoms in participants at high risk for schizophrenia or living with schizophrenia.

Negative symptoms are core aspect of schizophrenia, which might be present in all phases of the disorder and predict poor outcome.

The assessment and definition of negative symptoms underwent important changes during the last decade challenging previous pathophysiological models.

A large consensus accumulated on the subdivision of negative symptoms in two clusters: avolition and expressive deficit. Alterations in several circuits related to the processing of reward, its valuation and translation in goal-directed behavior are hypothesized for the avolition domain, while alterations of cortico-cortical connections might underlie the expressive deficit.

The presentation will review electrophysiological and brain imaging correlates of negative symptoms, critically addressing the role of confounding factors on heterogeneity of findings.

With the aim to find EEG biomarkers for schizophrenia, we investigate EEG microstates during sensory processing and resting state in children and adolescents with 22q11 deletion syndrome, who carry a high genetic risk for schizophrenia in adulthood. In an auditory oddball paradigm we find that the characteristic mismatch response is found in children but not adolescents with 22q11DS. In a paradigm investigating visual illusory contour perception with Kanizsa shapes we observe reduced activity over visual processing areas as well as marked increases of anterior cingulate and medio-dorsal frontal cortex activations. We will discuss how the reduced activity of sensory processing areas and the aberrant activity over anterior cingulate cortex relate to positive and negative symptoms in 22q11DS.

Patients with 22q11.2 deletion syndrome (22q11DS) present an ultra-high risk of developing schizophrenia (30-40%), thus representing a unique model for investigating biomarkers associated to psychosis.

When comparing patients with high and low positive symptoms severity, we found alterations in structural connectivity (measured with diffusion tensor imaging and structural covariance of cortical thickness) and in functional connectivity (measured with resting-state fMRI), which converged in indicating that disconnectivity of the anterior cingulate cortex (ACC) is associated with higher levels of symptoms.

Therefore, we concluded that disconnectivity of the ACC is a valuable biomarker of psychosis in patients with 22q11DS

Christoph Mulert (UKE, Hamburg) will focus on EEG and fMRI findings in subjects in the clinical high-risk state for psychosis (HRP). Recent studies include findings of alterations in EEG microstates, theta- and gamma oscillations. In addition he will present results of simultaneous EEG-fMRI studies in HRP subjects. These findings are discussed as potential markers for the prediction of HRP to frank psychosis.

Source reconstruction is used to improve the interpretation of surface-level electroencephalography (EEG) and magnetoencephalography (MEG) measurements. It has been shown that combined EEG/MEG provides source reconstructions that outperform the ones provided by single modalities. To compute the EEG/MEG source reconstruction, which is an inverse problem, the forward problem has to be solved. When computing the EEG/MEG forward problem in realistically shaped head models, numerical methods have to be adopted. In this work, we deal with finite element methods (FEMs), focusing on fulfilling the conservation of charge law. Specific consequences of the conservation law have been observed in an EEG study, where the unwanted phenomenon of “skull leakages” was overcome by using a discontinuous Galerkin FEM (DG-­FEM) instead of a classical, continuous Galerkin FEM (CG­-FEM). As a consequence of the conservation law fulfillment, in “leaky scenarios” the accuracy of EEG results is increased by the DG­-FEM scheme. In the same scenarios, it can be desirable to proceed with MEG investigations without changing the discretization underneath. When implementing the standard formulation of the MEG solution, the accuracy of the results is compromised by a non-­conservative current reconstruction. Here, we present an improved approach that exploits the conservation law, thus providing a conservative current reconstruction and leading to results in the same range of accuracy of the ones of a CG-­FEM implementation for MEG. Finally, DG­-FEM makes it possible to perform combined EEG and MEG forward computations using the same discretization in those scenarios where DG-­FEM leads to advantages.

Beamformers are often applied in estimating locations and strengths of neuronal sources underlying the measured MEG/EEG signals. Several MEG analysis toolboxes have implemented linearly constrained minimum variance (LCMV) beamformers, but there are still remaining issues such as the effects of novel interference suppression methods such as signal-space separation (SSS) and its variants. Differences in implementations and processing pipelines in the packages complicates the application of beamformers and may hinder their wider adoption in research and clinical uses.

In this study, we compared event-related beamformer results obtained with four software packages (Fieldtrip, SPM12, Elekta Beamformer and MNE Python) with different noise covariance matrices applied to raw and SSS-preprocessed data from a 306-channel Elekta MEG system.

First, we applied the packages on phantom data where location and strength of sources are known. There were substantial source localization differences (up to 10 mm) between results obtained from the different packages and SSS effected the results.

Next, we utilized somatosensory evoked responses acquired with the Elekta MEG device from a healthy subject. Electrical stimulation was delivered separately to the tip of four fingers of right hand, resulting in relatively weak SEF responses (~10 nAm). We computed the event-related beamformer power normalized by projected noise (Z2) images. The obtained sources from the packages were mostly localized in hand somatosensory area but there were 5-15 mm differences across the packages.

These implementation-dependent differences in results should be understood thoroughly, and guidelines for comparable use of different packages should be established to obtain reliable clinical results.

Combined magnetoencephalography and electroencephalography recordings were collected to investigate the differences in source reconstruction of the primary somatosensory cortex following electrical wrist stimulation of the median nerve (EW) and hapto-tactile (HT) and pneumato-tactile (PT) stimulation of the index finger. The functional data (275 gradiometers and 80 electrodes) were preprocessed for artifact elimination. Magnetic resonance images (T1w- and T2w-MRI scans) were collected and segmented into six compartments (skin, compacta, spongiosa, cerebrospinal fluid, grey and white matter). Furthermore, diffusion weighted MRI was measured allowing to model white matter conductivity anisotropy to investigate its influence on especially source orientation. A six-compartment anisotropic finite element head model was constructed with individually calibrated skull conductivity. With regard to the reconstruction of the P20 component, only small differences in source locations (7mm) among the three stimulations, while significant source orientation changes between EW and PT (53o degrees) and between EW and HT (40o degrees) and also significantly higher EW source amplitude (37.6 μAmm) than for HT (6.37 μAmm) and PT (7.83 μAmm) were found. Our results might be interpreted in the way that EW causes a higher number of pyramidal cells in somatosensory area 3b to synchronize leading then to better SNR. EW might thus be called the most robust type of somatosensory stimulation. However, compared to PT and HT stimulation, EW might be less acceptable because of slightly painly kind of stimulation, especially for long – lasting stimulations or application in children. Alternatively, HT might be used instead of EW avoiding any kind of discomfort.

Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to the conductivity of the different head tissues. Conductivity values are time and subject dependent, so non-invasive methods of conductivity estimation are necessary to fine tune the EEG models. In this work, we aim at estimating conductivity while solving the EEG source localization problem. To do this, we need to compute a forward EEG problem solution (so-called lead field matrix) for a large number of conductivity configurations.

Computing one lead field requires a matrix inversion which is computationally intensive for realistic head models. Thus, the required time for computing of a large number of solutions quickly becomes impractical. In this work, we propose a method which allows us to approximate the lead field matrix for a set of conductivity configurations, using only the exact solution for a small set of basis points from the conductivity space. Our approach accelerates the computing time, while the approximation error remains controlled.

Our method is tested for brain and skull conductivity estimation, with simulated and real EEG data. In the case of real data, we process EEG evoked potentials of median nerve stimulation. We used a single-dipole model to estimate both source location and conductivities of brain and skull. Our approximation method offers a performance similar to using exact lead field matrices, but with a remarkable gain of time.

High density scalp EEG data is routinely collected with a sampling rate of 1 KHz. However, higher sampling rates of up to 16 KHz/channel are available. We examined what additional information can be derived from EEG data sampled at a higher rate. We compared power spectral densities (PSD) and phase clustering behavior of EEG data sampled at 16.384 KHz and at 1.024 KHz. The PSD plots were similar at both sampling frequencies. However, there were significant differences in the spatiotemporal analysis of EEG phase cone formations. The data of an adult subject was collected with an ANT Neuro 256-channel system (eego mylab) with 16.384 KHz/channel sampling frequency. This data set was down sampled at 1.024 KHz/channel for a comparative analysis. The PSD was calculated by use of Fourier transform in alpha, beta and gamma bands. The phase was calculated and unwrapped after taking Hilbert transform of the data in the gamma band. The spatiotemporal plots of the phase were made by using a montage layout of 256 equidistant electrode positions and stable phase cone structures and their clusters were extracted. The spatiotemporal plots of phase and instantaneous power both had detailed additional spatial features at higher sampling frequency which were missing or smoothed out at lower sampling frequency. The phase cluster rate was higher at higher sampling frequency. These results indicate that additional information about the formation of phase clusters and related cortical phase transitions can be derived from EEG data collected at a higher sampling rate.

Functional connectivity can be measured with electroencephalography (EEG) data using a variety of metrics that emphasize different aspects of brain dynamics. Coherence, which measures the consistency of relative phase between channels, is a widely used measure of synchronization in different frequency bands and describes marginal dependence between channels. The interpretation of coherence as reflecting a functional connection in the brain is confounded by volume conduction of current and by common inputs to both channels. Spatial filtering (e.g., surface Laplacians) is often used to minimize volume conduction effects, but removes variance from the data providing only a partial view of the underlying network. Other approaches such as imaginary coherence introduce new distortions to coherence estimates. In this paper we assume that EEG data in a frequency band are generated by a complex multivariate normal (CMVN) in order to define a complex-Gaussian Graphical Model of the data. Conditional dependence between channels is reflected in the precision values of the model. Compared to coherence, precision estimates suppress volume conduction and common input effects, while providing, by way of the graphical lasso, a sparse estimate of the underlying network. We show through simulation that this model outperforms coherence as an estimate of connectivity and captures the most important features of the network. Examples provided demonstrate that Complex-Gaussian graphical models can be applied to either EEG time series (channel space) or reconstructed source time series (source space), to suppress the effects of volume conduction and common inputs, thereby obtaining genuine estimates of brain networks.

Several Authors proposed a dimensional approach to the reduction of schizophrenia heterogeneity. Initially, delusions, hallucination and disorganization were included in the positive dimension of psychosis. However, factor-analytic studies supported the existence of two separate clusters for the positive dimension: reality distortion, including hallucinations and delusions, and disorganization, including disorganized language and behavior, inappropriate affect and some features of cognitive impairment. These clusters emerged as separate domains of psychopathology, with distinct impacts on the outcome of schizophrenia and separate neuropsychological and brain imaging correlates.

This symposium will present recent electrophysiological and brain imaging findings supporting the neurobiological heterogeneity of reality distortion and disorganization in psychosis. Associations of these dimensions (and their constituent features) with indices of functional brain alterations in subjects at high risk for psychosis, as well as in those with first-episode or chronic schizophrenia will be reviewed.

A reduced amplitude of the auditory mismatch negativity (MMN), an ERP component thought to reflect updating of the stimulus context, is associated with positive symptoms (including auditory hallucinations) in chronic schizophrenia patients. It is unclear, however, whether the association between positive symptoms and MMN reductions can be observed in the early phase of the illness. We report that positive symptoms are associated with reductions of mismatch negativity elicited by both stimulus-change and pattern paradigms in early phase psychosis patients. This suggests that a link between positive symptom severity and brain functional alterations is present already in the earliest stages of the illness.

In subjects with schizophrenia (SCZ), the disorganization factor was found to be a strong predictor of real-life functioning. “Conceptual disorganization” (P2), “Difficulty in abstract thinking” (N5) and “Poor attention” (G11) are considered core aspects of the disorganization factor, as assessed by PANSS. The overlap of these items with neurocognitive functions is debated, and should be further investigated.

Within the Italian Network for Research on Psychoses study, electrophysiological and neurocognitive correlates of the disorganization factor and its component items were investigated.

In 145 chronic SCZ and 69 healthy controls, spectral amplitude (SAmp) differences and correlations with psychopathology and MATRICS Consensus Cognitive Battery (MCCB) scores were explored by RAGU.

A negative correlation between Alpha1 and the disorganization factor was observed in SCZ. At the item level, only ‘Difficulty in abstract thinking’ showed this correlation. MCCB neurocognitive composite score was associated with ‘Conceptual disorganization’ and ‘Difficulty in abstract thinking’ but not with Alpha1 SAmp.

Our findings suggest an heterogeneity of the disorganization dimension and a partial overlap with neurocognitive domains. The N5, “difficulties in abstract thinking”, had a unique association with alpha1 SAmp, which is thought to be involved in the formation of conceptual maps.

Formal thought disorders (FTD) are a core symptom in schizophrenia. We focus on distinguishable state cerebral blood flow (rCBF) patterns and white matter (WM) microstructure associated with FTD dimensions. We assessed FTD dimensions and imaging on a 3T MRI scanner. Positive FTD were associated with perfusion within brain regions relevant for language production, while negative FTD were associated with perfusion of semantic processing regions and fractional anisotropy in left hemispheric language system. Perfusion within the left supramarginal gyrus was associated with social functioning after 6 months. Distinguishable associations with FTD dimensions point to distinct underlying pathophysiology.

Abnormal salience processing has been found in people at ultra-high risk (UHR) for psychosis. In our study, using fMRI, we assessed the relationship between changes in the clinical features of 23 UHR and longitudinal changes in Ventral Striatum (VS) activation elicited during the Salience Attribution task. In UHR, we observed that the amelioration of abnormal beliefs over the follow-up period is linked to a longitudinal increase in VS activation during adaptive reward prediction.

Our results indicate a relationship between clinical outcome and longitudinal changes in ventral striatum during salience processing in UHR.

It has been long suggested that the different symptom domains of schizophrenia are best explained in terms of aberrant connectivity within distributed brain networks.

Electrophysiological and brain imaging studies might contribute to disentangle the neurobiological basis of different psychopathological dimensions which might be related to disconnection within different networks.

The aim of our symposium will be to summarize recent electrophysiological and brain imaging findings of altered connectivity in psychotic disorders and their association with psychopathological domains.

We will report brain electrical microstate correlates of positive symptoms and discuss the involved pathophysiological mechanisms. This report will in particular be based on a recent meta-analysis conducted across the available literature, novel data we have obtained from at-risk patients, and existing literature on the fMRI BOLD correlates of particular microstate classes. The overall picture from this analysis points at a dysbalance between processes that integrate and evaluate representations of information from the external world with representations of internal states.

Negative symptoms represent a key aspect of schizophrenia, with a worse outcome and a poor response to pharmacological treatment. They cluster into two domains: “avolition”, which includes apathy, anhedonia and asociality, and “expressive deficit”, which includes blunted affect and alogia.

The aim of this study was to investigate the different neurobiological correlates of negative symptoms domains using brain electrical microstates (MS), which reflect global, subsecond patterns of functional connectivity.

Resting EEGs were recorded in 142 schizophrenia patients (SCZ) and in 64 healthy controls (HC), recruited within the add-on EEG study of the Italian Network for Research on Psychoses. Four microstates classes (MS-A to MS-D) were quantified in terms of relative time contribution, duration and occurrence. We tested group differences on MS parameters and the relationships with negative symptom domains, assessed using the Brief Negative Symptoms Scale (BNSS).

SCZ, in comparison to HC, showed increased contribution (p=0.009) and duration (p=0.016) of MS-C.

As regard to negative symptoms, the total score of the BNSS was positively correlated with the contribution of MS-A (r= 0.19, p<0.03). Only avolition (r=0.22, p<0.01) and not expressive deficit (r=0.12, p=0.15) was correlated with contribution of MS-A. Within the avolition domain, anticipatory anhedonia (r=0.20, p=0.02), apathy (r=0.20, p=0.02) and asociality (r=0.25, p=0.003), but not consummatory anhedonia (r=0.13, p=0.13), were positively correlated with MS-A.

Our results support different neurobiological underpinnings of negative symptom domains and suggest the idea that only anticipatory anhedonia and not consummatory anhedonia shares common pathophysiological mechanisms with avolition.

<p>We investigated relations between resting-state EEG Source Functional Connectivity (EEG-SFC) and Expressive Deficit (ED) and Avolition (AV) measured with BNSS, in subjects with Schizophrenia (SCZ).</p>

<p>Out of 97 chronic, stabilized SCZ recorded, we selected 25 in upper (HIGH-ED) and 24 in the lower (LOW-ED) quartile of BNSS-ED and 27 in upper (HIGH-AV) and 24 in the lower (LOW-AV) quartile of BNSS-AV.</p>

<p>Respect to LOW-ED, HIGH-ED showed significant increased alpha connectivity in fronto-cingulate, para-hippocampal and insular inter-hemispheric regions. No significant difference emerged between HIGH-AV and LOW-AV in the source connectivity.</p>

<p>Subgrouping SCZ according to negative symptom severity reveals heterogeneous patterns of resting-state EEG-SFC.</p>

Auditory cortices show pathological activation during auditory verbal hallucinations (AVH) in schizophrenia, and reduced cortical gray matter volumes at first hospitalization that worsen with disease course. Here we explored the role of interhemispheric transcallosal connectivity in AVH in 31 first episode schizophrenia-spectrum psychosis (FESz). Spearman’s rank-order correlation revealed a relationship between reduced generalized functional anisotropy and increased AVH (rho = -.43, p =.016). This finding suggests that impaired structural connectivity between left and right hemisphere auditory cortices may play a role in AVH and the emergence of psychosis.

Introduction: EEG signals recorded during simultaneous fMRI are contaminated by strong artifacts, among which the ballistocardiographic (BCG), induced by subject’s cardiac activity, is the most challenging to be removed due to its complex non-stationary nature.

The presence of BCG residuals in EEG data may hide true, or generate spurious, correlations between EEG and fMRI time-courses. In this study, we propose an adaptive optimal basis set (AOBS) method for BCG removal, which uses artifact spatio-temporal features to firmly reduce BCG residuals.

Methods: Each EEG signal was epoched based on BCG rather than ECG events, to ensure more effective artifact characterization by Principal Component Analysis (PCA). Furthermore, the artifactual components to be removed were automatically identified from the data, based on signal features.

AOBS method performance was evaluated in terms of BCG removal and brain signal preservation, with respect to Average Artifact Subtraction (AAS), Independent Component Analysis (ICA) and Optimal Basis Set (OBS), using high-density EEG data acquired during simultaneous fMRI in 6 subjects.

Results: As compared to alternative methods, the application of AOBS led to a remarkable BCG artifact attenuation. Specifically, it yielded a percentage of BCG residuals equal to 7.51%, versus 19.16%, 13.81% and 13.21%, for AAS, ICA and OBS, respectively.

Conclusions: AOBS method enables reliable and effective reduction of BCG residuals. It is easy to use and does not require parameter tuning. Thus, it may find wide application in the field of simultaneous EEG-fMRI, especially for applications in which no data epoching and averaging is possible, e.g. resting-state studies.

NeuroPycon is an open-source multi-modal brain data analysis kit which provides Python-based pipelines for advanced multi-thread processing of fMRI, MEG and EEG data, with a focus on connectivity and graph analyses [1].

NeuroPycon is based on NiPype framework [2] which facilitates data analyses by wrapping many commonly-used neuroimaging software into a common python framework. Therefore, a major strength of NeuroPycon is that it relies on (and interfaces with) several freely available Python packages developed for efficient and fast parallel processing and that it seamlessly connects with existing open-science neuroimaging and signal processing toolboxes.

The flexible design allows users to configure analysis pipelines defined by connecting different nodes, where each node may be a user-defined function or a well-established tool or python-wrapped module (e.g. MNE-python for MEG analysis [3], etc.).

The current implementation of NeuroPycon contains three different packages:

- ephypype includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses

- graphpype allows to study functional connectivity exploiting graph-theoretical metrics including also modular partitions

- clipype is a command line interface for ephypype package.

NeuroPycon will shortly be available for download via github (installation via Docker) and is currently being documented. Future developments include fusion of multi-modal data (ex. MEG and fMRI or iEEG and fMRI).

References

1. Bullmore, Sporns (2009), Nat Rev Neurosci

2. Gorgolewski et al. (2011) Front. Neuroinform

3. Gramfort et al. (2013), Front. Neurosci

Background: Epilepsy is a widespread brain networks disorder with a high risk of recurrent unprovoked epileptic seizures associated with impaired awareness or convulsions. The main feature in pathological epileptic EEG is the presence of spikes. Brain connectivity is an important tool to explore this pathological network aspects, but there are many open issues on the methods consistency. The choice of the parameters in the connectivity estimation, e.g., the selection of the p-order of the multivariate autoregressive model, is fundamental to avoid meaningless results such as spurious connections. In epileptic EEG during spikes, both Akaike Information Criterion (AIC) and Bayesian Information Criterion are often not effective in the determination of the optimal p order, i.e., AIC increased monotonically with increasing model order.

Methods: In the pre-operative EEG of 9 patients we applied the same analysis to the sources time-courses and their time-reversal versions in order to detect false-positive connections. In particular, we computed Partial Directed Coherence (PDC) in the source space varying p in the range [2-20] for both dipoles and reverse-dipoles. In the ideal case with infinitive signal-to-noise-ratio, the connectivity matrix computed from the reverse-dipoles should be the transpose of the original connectivity one. Therefore, we defined as optimal p-order the one which minimizes the absolute difference between the two connectivity matrices.

Results: The optimal p-order varied in the range [8-19] for each different type of spike-time series.

Conclusion: Future development will be to validate our methodology using concordance of connectivity with surgical resection in post-operatively seizure free patients.

After stroke, functional recovery may be promoted through rehabilitation. In such cases, a remapping of affected limbs to other regions of the cortex is often observed. High spatial resolution neuroimaging techniques, like magnetic resonance imaging (MRI), can be used to investigate the anatomical changes in the brain, but their low temporal resolution provides less insight of dynamic changes of brain function. In contrast, electroencephalography (EEG) has an excellent temporal resolution to measure such transient events, hindered in turn by its low spatial resolution. This study introduces a multimodal brain imaging technique to improve the spatial resolution of EEG to study stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data was acquired from patients (N = 3) and healthy controls (N = 2) while electrical stimuli were delivered sequentially at index finger in left and right hand, respectively. A reasonably accurate estimation for active sources and inter-source connectivity was achieved in this study. Results indicate the changes of information flow in the brain after stroke, although the interpretation of these results in terms of neuroplasticity relearning is yet to be performed. This study provides evidence of this method being useful to track the information flow in the brain and may lead to a precise prognostic model of stroke.

Paralysis has a severe impact on a patient’s quality of life and entails a high emotional burden and life-long social and financial costs. Restoring movement and independence for the paralyzed remains a challenging clinical problem, currently with no viable solution. Recent demonstrations of intracortical brain-computer interfaces, neuroprosthetic devices that create a link between a person and a computer based on a person’s brain activity, have brought hope for their potential to restore movement and communication. While the intracortical brain-computer interfaces have steadily improved over the last decade, our recent success in linking brain activity with the newly developed techniques for spinal cord stimulation look to revolutionize locomotor rehabilitation. Specifically, our brain-spine interface restored weight-bearing locomotion of the paralyzed leg as early as six days post-injury in macaques1. New approaches in identifying neural features and designing decoding algorithms, which transform neural signals into computer commands, aim to deliver both stable and accurate control over clinically relevant periods of several months. To this end, we developed signal processing and decoder calibration approaches that enabled a person with long-stable tetraplegia to control a communication brain-computer interface for 138 days with an unchanged decoder2. Preliminary clinical studies suggest that these concepts and technologies are directly translatable to therapeutic strategies for people with paralysis.

1. M. Capogrosso*, T. Milekovic*, D. Borton*, et al., A brain–spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539, 284-288 (2016).

2. T. Milekovic, et al., Stable asynchronous BCIs based on field potentials for communication, BCCN Conference

Introduction: Neurexan®, a medicinal product sold over the counter (OTC), contains passionflower, oat, coffee and zinc valerianate. Neurexan® has been investigated in patients with symptoms related to acute stress, nervousness and insomnia. Amygdala is involved in the development of fear and emotional behavior. Acute stress sensitizes the amygdala, thereby increasing vigilance/anxiety, which in turn promotes the stress response. Amygdala reactivity to negative stimuli is a reliable phenotype that closely associates with stress regulation and can be assessed with Hariri paradigm. Furthermore, a linkage between an increased level of stress hormones and increased emotional response to angry faces has been shown in patients with social phobia. Previous investigation suggested an attenuated neuroendocrine stress response in healthy volunteers induced by Neurexan®. Thus, our aim was to further explore the effect of Neurexan® on the emotional brain response in the amygdala.

Methods: In a randomized, placebo-controlled, double-blind, two-period crossover trial brain response to the Hariri task, an emotional paradigm, of 39 healthy, moderate stressed males was measured after intake of a single dose of Neurexan® and placebo control via 3 Tesla fMRI. Data were preprocessed and analyzed in SPM12.

Results&Conclusion: Hariri task was firstly validated for negative emotional faces response. Significant (peak level FWE corrected) bilateral activations of fusiform gyri, amygdalae and prefrontal cortex as well as unilateral activation in right thalamus were confirmed as previously reported. Paired t-test showed a significant reduction of BOLD response to negative faces in the left amygdala (p<0.05) during the Neurexan® session as compared to placebo.

The pathophysiological features of Alzheimer’s Disease are broadly consistent (e.g. extracellular deposition of amyloid beta 1-42 and intracellular accumulation of phospho-tau), but the clinical phenotype is heterogeneous with different manifestations of the symptoms over time. Several structural, molecular, and functional neuroimaging markers capture several important underlying structural and functional cortical and subcortical abnormalities. However, they cannot explore a potentially critical angle of the Alzheimer’s disease as a pathology of distributed cognitive systems, namely the neurophysiological mechanisms of neural synchronization and coupling in the complex linear and nonlinear interactions of neurons and distributed neural populations at millisecond time scale. This Session will highlight new findings obtained from neurophysiological methods to study those interactions in neuronal circuitry and signal transmission at spatial macro-, meso-, and micro-scale, conferred by Alzheimer’s disease specific pathologies in living systems. Furthermore, the Session will focus on the issue of translation (from preclinical to clinical) vs. back-translation (from clinical to preclinical) of electrophysiology biomarkers in research and drug discovery and development. Finally, findings on the cross-modal validity and specificity of the electrophysiological markers of Alzheimer’s disease will be presented and discussed.

A tau seeding model was used wherein pre-formed synthetic tau fibrils (K18) are unilaterally injected into either the hippocampus or the locus coeruleus (LC) of a transgenic mouse model of Alzheimer’s Disease (AD), which expresses mutant human P301L tau.After hippocampal K18-injections, network alterations and clear theta–gamma EEG uncoupling in a hippocampal network were detected before Tau pathology and neuronal loss. Specifically, there was a decreased intra- and inter-hemispheric hippocampal theta directionality and functional phase-amplitude theta-gamma coupling strength. These findings support the view that disruptions in synaptic plasticity and progressive loss of functional connectivity in the hippocampus are hypothesized to underlie cognitive deficits.

<p>In the European IMI PharmaCog project (Grant Agreement n&deg;115009, www.pharmacog.org), we evaluated the hypothesis that resting state electroencephalographic (rsEEG) and auditory oddball ERP/markers may be sensitive to transient experimental challenges of cognitive performance in young volunteers (Healthy) for future applications to very early drug discovery phases. Cortical sources of delta and alpha rsEEG rhythms and auditory oddball P3b of ERPs were abnormal in mild cognitive impairment patients positive to cerebrospinal markers of Alzheimer&rsquo;s disease (MCI+) compared with MCI- subjects. Furthermore, one-night sleep deprivation (SD) altered the cognitive performance as well as cortical sources of delta and alpha rsEEG rhythms and auditory oddball P3b peak in Healthy subjects, while Modafinil partially recovered them. These rsEEG and auditory ERP markers can be back-translated from prodromal Alzheimer&rsquo;s disease patients to healthy young volunteers under a cognitive challenge (e.g. SD).</p>

The differential diagnosis of Alzheimer's disease (AD) and Dementia with Lewy bodies (DLB) is challenging. Enriching the biomarkers for this diagnosis, we underwent 72 participants (21 Controls, 30 AD, 21 DLB) to rsEEG and 3 T MR imaging. MRI index was derived from medial temporal atrophy (MTA) ratings. A mixed rsEEG-MRI model showed the best classification accuracy of 93% for AD and 86% for DLB. It was concluded that multimodal rsEEG-MRI approaches might be advantageous, especially in settings where neuroimaging of dopaminergic system is not available.