After-effects of 10 Hz tACS over the prefrontal cortex on phonological word decisions

Introduction Previous work in the language domain has shown that 10 Hz rTMS of the left or right posterior inferior frontal gyrus (pIFG) in the prefrontal cortex impaired phonological decision-making, arguing for a causal contribution of the bilateral pIFG to phonological processing. However, the neurophysiological correlates of these effects are unclear. The present study addressed the question whether neural activity in the prefrontal cortex could be modulated by 10 Hz tACS and how this would affect phonological decisions. Methods In three sessions, 24 healthy participants received tACS at 10 Hz or 16.18 Hz (control frequency) or sham stimulation over the bilateral prefrontal cortex before task processing. Resting state EEG was recorded before and after tACS. We also recorded EEG during task processing. Results Relative to sham stimulation, 10 Hz tACS significantly facilitated phonological response speed. This effect was task-specific as tACS did not affect a simple control task. Moreover, 10 Hz tACS significantly increased theta power during phonological decisions. The individual increase in theta power was positively correlated with the behavioral facilitation after 10 Hz tACS. Conclusion Our results show a facilitation of phonological decisions after 10 Hz tACS over the bilateral prefrontal cortex. This might indicate that 10 Hz tACS increased task-related activity in the stimulated area to a level that was optimal for phonological performance. The significant correlation with the individual increase in theta power suggests that the behavioral facilitation might be related to increased theta power during language processing

Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis

Neuronal networks in children with continuous spikes and waves during slow sleep

Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least >85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and waves during slow sleep and neuropsychological deficits associated with this condition are still poorly understood. Here, we investigated the haemodynamic changes associated with epileptic activity using simultaneous acquisitions of electroencephalography and functional magnetic resonance imaging in 12 children with symptomatic and cryptogenic continuous spikes and waves during slow sleep. We compared the results of magnetic resonance to electric source analysis carried out using a distributed linear inverse solution at two time points of the averaged epileptic spike. All patients demonstrated highly significant spike-related positive (activations) and negative (deactivations) blood oxygenation-level-dependent changes (P < 0.05, family-wise error corrected). The activations involved bilateral perisylvian region and cingulate gyrus in all cases, bilateral frontal cortex in five, bilateral parietal cortex in one and thalamus in five cases. Electrical source analysis demonstrated a similar involvement of the perisylvian brain regions in all patients, independent of the area of spike generation. The spike-related deactivations were found in structures of the default mode network (precuneus, parietal cortex and medial frontal cortex) in all patients and in caudate nucleus in four. Group analyses emphasized the described individual differences. Despite aetiological heterogeneity, patients with continuous spikes and waves during slow sleep were characterized by activation of the similar neuronal network: perisylvian region, insula and cingulate gyrus. Comparison with the electrical source analysis results suggests that the activations correspond to both initiation and propagation pathways. The deactivations in structures of the default mode network are consistent with the concept of epileptiform activity impacting on normal brain function by inducing repetitive interruptions of neurophysiological functio

A Comparison between Schizophrenia and Autism

Autism spectrum disorder and schizophrenia share a substantial number of etiologic and phenotypic characteristics. Still, no direct comparison of both disorders has been performed to identify differences and commonalities in brain structure. In this voxel based morphometry study, 34 patients with autism spectrum disorder, 21 patients with schizophrenia and 26 typically developed control subjects were included to identify global and regional brain volume alterations. No global gray matter or white matter differences were found between groups. In regional data, patients with autism spectrum disorder compared to typically developed control subjects showed smaller gray matter volume in the amygdala, insula, and anterior medial prefrontal cortex. Compared to patients with schizophrenia, patients with autism spectrum disorder displayed smaller gray matter volume in the left insula. Disorder specific positive correlations were found between mentalizing ability and left amygdala volume in autism spectrum disorder, and hallucinatory behavior and insula volume in schizophrenia. Results suggest the involvement of social brain areas in both disorders. Further studies are needed to replicate these findings and to quantify the amount of distinct and overlapping neural correlates in autism spectrum disorder and schizophrenia

With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging

In patients with medically refractory focal epilepsy who are candidates for epilepsy surgery, concordant non-invasive neuroimaging data are useful to guide invasive electroencephalographic recordings or surgical resection. Simultaneous electroencephalography and functional magnetic resonance imaging recordings can reveal regions of haemodynamic fluctuations related to epileptic activity and help localize its generators. However, many of these studies (40-70%) remain inconclusive, principally due to the absence of interictal epileptiform discharges during simultaneous recordings, or lack of haemodynamic changes correlated to interictal epileptiform discharges. We investigated whether the presence of epilepsy-specific voltage maps on scalp electroencephalography correlated with haemodynamic changes and could help localize the epileptic focus. In 23 patients with focal epilepsy, we built epilepsy-specific electroencephalographic voltage maps using averaged interictal epileptiform discharges recorded during long-term clinical monitoring outside the scanner and computed the correlation of this map with the electroencephalographic recordings in the scanner for each time frame. The time course of this correlation coefficient was used as a regressor for functional magnetic resonance imaging analysis to map haemodynamic changes related to these epilepsy-specific maps (topography-related haemodynamic changes). The method was first validated in five patients with significant haemodynamic changes correlated to interictal epileptiform discharges on conventional analysis. We then applied the method to 18 patients who had inconclusive simultaneous electroencephalography and functional magnetic resonance imaging studies due to the absence of interictal epileptiform discharges or absence of significant correlated haemodynamic changes. The concordance of the results with subsequent intracranial electroencephalography and/or resection area in patients who were seizure free after surgery was assessed. In the validation group, haemodynamic changes correlated to voltage maps were similar to those obtained with conventional analysis in 5/5 patients. In 14/18 patients (78%) with previously inconclusive studies, scalp maps related to epileptic activity had haemodynamic correlates even when no interictal epileptiform discharges were detected during simultaneous recordings. Haemodynamic changes correlated to voltage maps were spatially concordant with intracranial electroencephalography or with the resection area. We found better concordance in patients with lateral temporal and extratemporal neocortical epilepsy compared to medial/polar temporal lobe epilepsy, probably due to the fact that electroencephalographic voltage maps specific to lateral temporal and extratemporal epileptic activity are more dissimilar to maps of physiological activity. Our approach significantly increases the yield of simultaneous electroencephalography and functional magnetic resonance imaging to localize the epileptic focus non-invasively, allowing better targeting for surgical resection or implantation of intracranial electrode array

Constrained expectation maximisation algorithm for estimating ARMA models in state space representation

This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise partly from the assumption that the models have a block-diagonal structure, with each block corresponding to an ARMA process, that allows the reconstruction of independent source components from linear mixtures, and partly from the need to keep models identifiable. The first stage of parameter fitting is performed by the expectation maximisation (EM) algorithm. Due to the identifiability constraint, a subset of the diagonal elements of the dynamical noise covariance matrix needs to be constrained to fixed values (usually unity). For this kind of constraints, so far, no closed-form update rules were available. We present new update rules for this situation, both for updating the dynamical noise covariance matrix directly and for updating a matrix square-root of this matrix. The practical applicability of the proposed algorithm is demonstrated by a low-dimensional simulation example. The behaviour of the EM algorithm, as observed in this example, illustrates the well-known fact that in practical applications, the EM algorithm should be combined with a different algorithm for numerical optimisation, such as a quasi-Newton algorithm

Neuronal networks in epileptic encephalopathies with CSWC [Abstract]

Aim: Continues spikes and waves during slow sleep (CSWS) is an agerelated epileptic encephalopathy characterized by occurrence of diffuse,continues spike and wave discharges during NREM sleep, seizures andpsychomotor impairment. The aim of our study was to investigate theneuronal networks underling background oscillations of CSWC using thesource analysis method Dynamic Imaging of Coherent Sources (DICS)and renormalized partial directed coherence (RPDC). Methods: In order to investigate underlying network and effective con-nectivity within the detected network, a DICS analyses and renormalizedpartial directed coherence (RPDC) methods were applied. The baselinesleep EEG recordings and follow up sleep EEG recordings from 12Patients with CSWS were used for the analyses. Results and Conclusions: The results revealed that independent of aeti-ology and severely of the disease CSWS EEG pattern is associated withthe complex network of coherent sources in medial prefrontal cortex,somatosensory association cortex/posterior cingulate cortex, medial pre-frontal cortex, middle temporal gyrus/parahippocampal gyrus/insularcortex, Thalamus and cerebellum. The described network underlyingCSWS was found on both group and individual levels and was no longerobserved in follow up EEGs of the patients with normalized sleep EEGs,suggesting the specificity of the network for the CSWS pattern. Furtheron, for the first time, using RPDC analyses we investigated the hierarchywithin the described network, which showed that Thalamus, togetherwith mesial temporal and parietal regions may be seen as a central hub ofthe underlying network. The involvement of this thalamocortical net-work, which was no longer observed in normalized EEGs, and a furtherpropagation towards the frontal region may interfere with restructuringof cognitive networks in the sensitive phase of development

The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process

Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this "functional connectome" based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance.Fil: Tagliazucchi, Enzo. Christian Albrechts Universitat Zu Kiel.; Alemania. University Frankfurt am Main; AlemaniaFil: Siniatchkin, Michael. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: Laufs, Helmut. University Frankfurt am Main; Alemania. University Hospital Schleswig Holstein; AlemaniaFil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentin

Identifying seizure onset zone and epileptic networks with the dynamic imaging of coherent sources [Abstract]

Purpose: In the presurgical evaluation of children with refractory focalepilepsy the main difficulty is to locate the exact point of seizure onset.The aim of this study was to characterize the areas of seizure onset as wellas the epileptic network involved in seizure propagation usingDynamicimaging of coherent sources (DICS)of ictal EEGs. Method: DICS is an inverse solution in the frequency domain whichdescribes neuronal networks and coherence of oscillatory brain activityby applying a spatial filter (Gross et al. PNAS 2001; 98:694–699). In 15children with refractory focal epilepsy, typical seizures were selectedfrom the EEGs recorded during the presurgical evaluation. For every sei-zure, two data sets of 10 s duration were extracted: one EEG segmentcontained the seizure onset and the other segment included the middlepart of the seizure. For both segments, the frequency range was definedand analyzed with DICS. The brain area with the strongest power in thecorresponding frequency range was defined as a reference region and itscoherence with the entire brain was computed using DICS. The result ofthe reference region was compared with the electroclinical localizationof seizure onset as well as with the postoperative resection site to deter-mine concordance. Results: For the beginning of the seizure, a good concordance betweenresults of the DICS localization and postoperative outcome was achievedin all 15 patients. The analysis of seizure propagation revealed an epilep-tic network which resembled reverberation of epileptic activity betweendifferent brain areas. Conclusion: DICS may be a useful tool to define the seizure onset zoneand study epileptic networks