Cortical Generators and Connections Underlying Phoneme Perception: A Mismatch Negativity and P300 Investigation.

The cortical generators of the pure tone MMN and P300 have been thoroughly studied. Their nature and interaction with respect to phoneme perception, however, is poorly understood. Accordingly, the cortical sources and functional connections that underlie the MMN and P300 in relation to passive and active speech sound perception were identified. An inattentive and attentive phonemic oddball paradigm, eliciting a MMN and P300 respectively, were administered in 60 healthy adults during simultaneous high-density EEG recording. For both the MMN and P300, eLORETA source reconstruction was performed. The maximal cross-correlation was calculated between ROI-pairs to investigate inter-regional functional connectivity specific to passive and active deviant processing. MMN activation clusters were identified in the temporal (insula, superior temporal gyrus and temporal pole), frontal (rostral middle frontal and pars opercularis) and parietal (postcentral and supramarginal gyrus) cortex. Passive discrimination of deviant phonemes was aided by a network connecting right temporoparietal cortices to left frontal areas. For the P300, clusters with significantly higher activity were found in the frontal (caudal middle frontal and precentral), parietal (precuneus) and cingulate (posterior and isthmus) cortex. Significant intra- and interhemispheric connections between parietal, cingulate and occipital regions constituted the network governing active phonemic target detection. A predominantly bilateral network was found to underly both the MMN and P300. While passive phoneme discrimination is aided by a fronto-temporo-parietal network, active categorization calls on a network entailing fronto-parieto-cingulate cortices. Neural processing of phonemic contrasts, as reflected by the MMN and P300, does not appear to show pronounced lateralization to the language-dominant hemisphere.

Hippocampal seizures differentially modulate locus coeruleus activity and result in consistent time-locked release of noradrenaline in rat hippocampus.

The locus coeruleus (LC) is a small brainstem nucleus and is the sole source of noradrenaline in the neocortex, hippocampus and cerebellum. Noradrenaline is a powerful neuromodulator involved in the regulation of excitability and plasticity of large-scale brain networks. In this study, we performed a detailed assessment of the activity of locus coeruleus neurons and changes in noradrenergic transmission during acute hippocampal seizures evoked with perforant path stimulation, using state-of-the-art methodology. Action potentials of LC neurons, of which some were identified by means of optogenetics, were recorded in anesthetized rats using a multichannel high-density electrophysiology probe. The seizure-induced change in firing rate differed between LC neurons: 55% of neurons decreased in firing rate during seizures, while 28% increased their firing rate. Topographic analysis of multi-unit activity over the electrophysiology probe showed a topographic clustering of neurons that were inhibited or excited during seizures. Changes in hippocampal noradrenaline transmission during seizures were assessed using a fluorescent biosensor for noradrenaline, GRABNE2m, in combination with fiber photometry, in both anesthetized and awake rats. Although our neuronal recordings indicated both inhibition and excitation of LC neurons during seizures, a consistent release of noradrenaline was observed. Concentrations of noradrenaline increased at seizure onset and decreased during or shortly after the seizure. In conclusion, this study showed consistent but heterogeneous modulation of LC neurons and a consistent time-locked release of hippocampal noradrenaline during acute hippocampal seizures.

Test-retest reliability of electroencephalographic and magnetoencephalographic measures elicited during language tasks: A literature review.

Electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings during language processing can provide relevant insights on neuroplasticity in clinical populations (including patients with aphasia). To use EEG and MEG in a longitudinal way, the outcome measures should be consistent across time in healthy individuals. Therefore, the current study provides a review on the test-retest reliability of EEG and MEG measures elicited during language paradigms in healthy adults. PubMed, Web of Science and Embase were searched for relevant articles based on specific eligibility criteria. In total, 11 articles were included in this literature review. The test-retest reliability of the P1, N1 and P2 is systematically considered to be satisfactory, whereas findings are more variable for event-related potentials/fields occurring later in time. The within subject consistency of EEG and MEG measures during language processing can be influenced by multiple variables such as the stimulus presentation mode, the offline reference choice and the required amount of cognitive resources during the task. To conclude, most of the available results are favourable regarding the longitudinal use of EEG and MEG measures elicited during language paradigms in healthy young individuals. In view to the use of these techniques in patients with aphasia, future research should focus on whether the same findings apply to different age groups.

Semiautomatic interictal electric source localization based on long-term electroencephalographic monitoring: A prospective study.

OBJECTIVE: Electric source imaging (ESI) of interictal epileptiform discharges (IEDs) has shown significant yield in numerous studies; however, its implementation at most centers is labor- and cost-intensive. Semiautomatic ESI analysis (SAEA) has been proposed as an alternative and has previously shown benefit. Computer-assisted automatic spike cluster retrieval, averaging, and source localization are carried out for each cluster and are then reviewed by an expert neurophysiologist, to determine their relevance for the individual case. Here, we examine its yield in a prospective single center study. METHOD: Between 2017 and 2022, 122 patients underwent SAEA. Inclusion criteria for the current study were unifocal epilepsy disorder, epilepsy surgery with curative purpose, and postoperative follow-up of 2 years or more. All patients (N=40) had continuous video-electroencephalographic (EEG) monitoring with 37 scalp electrodes, which underwent SAEA. Forty patients matched our inclusion criteria. RESULTS: Twenty patients required intracranial monitoring; 13 were magnetic resonance imaging (MRI)-negative. Mean duration of analyzed EEG was 4.3 days (±3.1 days), containing a mean of 12 749 detected IEDs (±22 324). The sensitivity, specificity, and accuracy of SAEA for localizing the epileptogenic focus of the entire group were 74.3%, 80%, and 75%, respectively, leading to an odds ratio (OR) of 11.5 to become seizure-free if the source was included in the resection volume (p < .05). In patients with extratemporal lobe epilepsy, our results indicated an accuracy of 68% (OR=11.7). For MRI-negative patients (n = 13) and patients requiring intracranial EEG (n = 20), we found a similarly high accuracy of 84.6% (OR=19) and 75% (OR = 15.9), respectively. SIGNIFICANCE: In this prospective study of SAEA of long-term video-EEG, spanning several days, we found excellent localizing information and a high yield, even in difficult patient groups. This compares favorably to high-density ESI, most likely due to marked improved signal-to-noise ratio of the averaged IEDs. We propose including ESI, or SAEA, in the workup of all patients who are referred for epilepsy surgery.

Single-trial ERP Quantification Using Neural Networks.

Traditional approaches to quantify components in event-related potentials (ERPs) are based on averaging EEG responses. However, this method ignores the trial-to-trial variability in the component's latency, resulting in a smeared version of the component and underestimates of its amplitude. Different techniques to quantify ERP components in single trials have therefore been described in literature. In this study, two approaches based on neural networks are proposed and their performance was compared with other techniques using simulated data and two experimental datasets. On the simulated dataset, the neural networks outperformed other techniques for most signal-to-noise ratios and resulted in better estimates of the topography and shape of the ERP component. In the first experimental dataset, the highest correlation values between the estimated latencies of the P300 component and the reaction times were obtained using the neural networks. In the second dataset, the single-trial latency estimation techniques showed an amplitude reduction of the N400 effect with age and ascertained this effect could not be attributed to differences in latency variability. These results illustrate the applicability and the added value of neural networks for the quantification of ERP components in individual trials. A limitation, however, is that simulated data is needed to train the neural networks, which can be difficult when the ERP components to be found are not known a priori. Nevertheless, the neural networks-based approaches offer more information on the variability of the timing of the component and result in better estimates of the shape and topography of ERP components.

Clinical added value of interictal automated electrical source imaging in the presurgical evaluation of MRI-negative epilepsy: A real-life experience in 29 consecutive patients.

OBJECTIVE: During the presurgical evaluation, manual electrical source imaging (ESI) provides clinically useful information in one-third of the patients but it is time-consuming and requires specific expertise. This prospective study aims to assess the clinical added value of a fully automated ESI analysis in a cohort of patients with MRI-negative epilepsy and describe its diagnostic performance, by evaluating sublobar concordance with stereo-electroencephalography (SEEG) results and surgical resection and outcome. METHODS: All consecutive patients referred to the Center for Refractory Epilepsy (CRE) of St-Luc University Hospital (Brussels, Belgium) for presurgical evaluation between 15/01/2019 and 31/12/2020 meeting the inclusion criteria, were recruited to the study. Interictal ESI was realized on low-density long-term EEG monitoring (LD-ESI) and, whenever available, high-density EEG (HD-ESI), using a fully automated analysis (Epilog PreOp, Epilog NV, Ghent, Belgium). The multidisciplinary team (MDT) was asked to formulate hypotheses about the epileptogenic zone (EZ) location at sublobar level and make a decision on further management for each patient at two distinct moments: i) blinded to ESI and ii) after the presentation and clinical interpretation of ESI. Results leading to a change in clinical management were considered contributive. Patients were followed up to assess whether these changes lead to concordant results on stereo-EEG (SEEG) or successful epilepsy surgery. RESULTS: Data from all included 29 patients were analyzed. ESI led to a change in the management plan in 12/29 patients (41%). In 9/12 (75%), modifications were related to a change in the plan of the invasive recording. In 8/9 patients, invasive recording was performed. In 6/8 (75%), the intracranial EEG recording confirmed the localization of the ESI at a sublobar level. So far, 5/12 patients, for whom the management plan was changed after ESI, were operated on and have at least one-year postoperative follow-up. In all cases, the EZ identified by ESI was included in the resection zone. Among these patients, 4/5 (80%) are seizure-free (ILAE 1) and one patient experienced a seizure reduction of more than 50% (ILAE 4). CONCLUSIONS: In this single-center prospective study, we demonstrated the added value of automated ESI in the presurgical evaluation of MRI-negative cases, especially in helping to plan the implantation of depth electrodes for SEEG, provided that ESI results are integrated into the whole multimodal evaluation and clinically interpreted.

Detection of interictal epileptiform discharges in an extended scalp EEG array and high-density EEG-A prospective multicenter study.

OBJECTIVES: High counts of averaged interictal epileptiform discharges (IEDs) are key components of accurate interictal electric source imaging (ESI) in patients with focal epilepsy. Automated detections may be time-efficient, but they need to identify the correct IED types. Thus we compared semiautomated and automated detection of IED types in long-term video-EEG (electroencephalography) monitoring (LTM) using an extended scalp EEG array and short-term high-density EEG (hdEEG) with visual detection of IED types and the seizure-onset zone (SOZ). METHODS: We prospectively recruited consecutive patients from four epilepsy centers who underwent both LTM with 40-electrode scalp EEG and short-term hdEEG with 256 electrodes. Only patients with a single circumscribed SOZ in LTM were included. In LTM and hdEEG, IED types were identified visually, semiautomatically and automatically. Concordances of semiautomated and automated detections in LTM and hdEEG, as well as visual detections in hdEEG, were compared against visually detected IED types and the SOZ in LTM. RESULTS: Fifty-two of 62 patients with LTM and hdEEG were included. The most frequent IED types per patient, detected semiautomatically and automatically in LTM and visually in hdEEG, were significantly concordant with the most frequently visually identified IED type in LTM and the SOZ. Semiautomated and automated detections of IED types in hdEEG were significantly concordant with visually identified IED types in LTM, only when IED types with more than 50 detected single IEDs were selected. The threshold of 50 detected IED in hdEEG was reached in half of the patients. For all IED types per patient, agreement between visual and semiautomated detections in LTM was high. SIGNIFICANCE: Semiautomated and automated detections of IED types in LTM show significant agreement with visually detected IED types and the SOZ. In short-term hdEEG, semiautomated detections of IED types are concordant with visually detected IED types and the SOZ in LTM if high IED counts were detected.

A case series of verbal semantic processing in primary progressive aphasia: Evidence from the N400 effect.

BACKGROUND: The semantic variant of primary progressive aphasia (PPA) is typically associated with a loss of semantic knowledge. Research on the semantic processing in the other clinical variants of PPA is, however, rather sparse and limited to off-line behavioural studies. AIMS: This study aimed to investigate verbal semantic processing in patients with the three variants of PPA by the event-related potential technique. The presence, latency, amplitude and/or topographic distribution of the N400 effect may be helpful in the diagnosis of PPA and its clinical variants and it provides temporal information about semantic processing (disturbances) in the three variants of PPA. METHODS & PROCEDURES: The N400 effect was studied by a categorical word-priming paradigm and a semantic-anomaly paradigm at sentence level in eight persons with PPA(-plus) and 30 age-matched healthy controls. The mean amplitudes and onset latencies of the N400 effect were compared between each patient and the control group by two methods that are applicable in clinical practice, namely visual inspection and Z-scores. OUTCOMES & RESULTS: The N400 effect elicited by the categorical-priming paradigm was only present in the two patients with the non-fluent variant of PPA. This effect was absent in the two patients with the semantic variant(-plus), two patients with the logopenic variant(-plus), one patient with the non-fluent variant-plus, and the patient with PPA not otherwise specified. The results of the N400 effect elicited by the semantic-anomaly task at the sentence level were variable, but differences in the presence, mean amplitudes, onset latencies and/or topographic distributions of the effect were found in all patients with PPA(-plus) in comparison with the control group. CONCLUSIONS & IMPLICATIONS: The results of our study showed that the evaluation of the N400 effect might have an added value in the diagnostic process of PPA in general and in the differentiation of patients with the non-fluent variant from patients with the logopenic and semantic variants. Furthermore, our results indicate the presence of difficulties with retrieving stored semantic knowledge or semantic integration of a word in the preceding context in patients with the three variants of PPA. These findings might help the speech-language pathologist in determining individualized therapy goals and indicate that it might be helpful to focus on verbal semantic processing in language therapy in patients with the three variants of PPA and not only in patients with the semantic variant. WHAT THIS PAPER ADDS: What is already known on the subject The semantic variant of PPA is characterized by an impaired object knowledge and single-word comprehension and these functions are relatively spared in the non-fluent and logopenic variants following the guidelines of Gorno-Tempini et al. (2011). Research on the semantic processing in patients with the non-fluent and logopenic variant is, however, rather sparse and limited to off-line behavioural studies. Only four group studies investigated verbal semantic processing by the N400 effect, and these studies indicate disturbances in the three variants of PPA. What this paper adds to existing knowledge Our results indicate the presence of difficulties with retrieving stored semantic knowledge or semantic integration of a word in the preceding context during a semantic-priming paradigm in patients with the semantic and logopenic variants of PPA and during a semantic-anomaly task at the sentence level in patients with the three variants of PPA. What are the potential or actual clinical implications of this work? The results of our study showed that the evaluation of the N400 effect might have an added value in the diagnostic process of PPA in general and in the differentiation of patients with the non-fluent variant from patients with the logopenic and semantic variants. The evaluation of the N400 effect might also help the speech-language pathologist in determining individualized therapy goals and indicate that it might be helpful to focus on verbal semantic processing in language therapy in patients with the three variants of PPA and not only in patients with the semantic variant.

Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG.

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy.

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.

Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy.

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space - identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory-inhibitory balance.

Sex Differences in Neurophysiological Activation Patterns During Phonological Input Processing: An Influencing Factor for Normative Data.

In the context of neurophysiological normative data, it has been established that aging has a significant impact on neurophysiological correlates of auditory phonological input processes, such as phoneme discrimination (PD) and word recognition (WR). Besides age, sex is another demographic factor that influences several language processes. We aimed to disentangle whether sex has a similar effect on PD and WR. Event-related potentials (ERPs) were recorded in 20 men and 24 women. During PD, three phonemic contrasts (place and manner of articulation and voicing) were compared using the attentive P300 and pre-attentive Mismatch Negativity. To investigate WR, real words were contrasted with pseudowords in a pre-attentive oddball task. Women demonstrated a larger sensitivity to spectrotemporal differences, as evidenced by larger P300 responses to the place of articulation (PoA) contrast and larger P300 and MMN responses than men in PoA-based PD. Men did not display such sensitivity. Attention played an important role, considering that women needed more attentional resources to differentiate between PoA and the other phonemic contrasts. During WR, pseudowords evoked larger amplitudes already 100 ms post-stimulus independent of sex. However, women had decreased P200 latencies, but longer N400 latencies in response to pseudowords, whereas men showed increased N400 latencies compared to women in response to real words. The current results demonstrate significant sex-related influences on phonological input processes. Therefore, existing neurophysiological normative data for age should be complemented for the factor sex.

Multiple sparse volumetric priors for distributed EEG source reconstruction.

We revisit the multiple sparse priors (MSP) algorithm implemented in the statistical parametric mapping software (SPM) for distributed EEG source reconstruction (Friston et al., 2008). In the present implementation, multiple cortical patches are introduced as source priors based on a dipole source space restricted to a cortical surface mesh. In this note, we present a technique to construct volumetric cortical regions to introduce as source priors by restricting the dipole source space to a segmented gray matter layer and using a region growing approach. This extension allows to reconstruct brain structures besides the cortical surface and facilitates the use of more realistic volumetric head models including more layers, such as cerebrospinal fluid (CSF), compared to the standard 3-layered scalp-skull-brain head models. We illustrated the technique with ERP data and anatomical MR images in 12 subjects. Based on the segmented gray matter for each of the subjects, cortical regions were created and introduced as source priors for MSP-inversion assuming two types of head models. The standard 3-layered scalp-skull-brain head models and extended 4-layered head models including CSF. We compared these models with the current implementation by assessing the free energy corresponding with each of the reconstructions using Bayesian model selection for group studies. Strong evidence was found in favor of the volumetric MSP approach compared to the MSP approach based on cortical patches for both types of head models. Overall, the strongest evidence was found in favor of the volumetric MSP reconstructions based on the extended head models including CSF. These results were verified by comparing the reconstructed activity. The use of volumetric cortical regions as source priors is a useful complement to the present implementation as it allows to introduce more complex head models and volumetric source priors in future studies.

Bayesian model selection of template forward models for EEG source reconstruction.

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction.

Influence of skull modeling approaches on EEG source localization.

Electroencephalographic source localization (ESL) relies on an accurate model representing the human head for the computation of the forward solution. In this head model, the skull is of utmost importance due to its complex geometry and low conductivity compared to the other tissues inside the head. We investigated the influence of using different skull modeling approaches on ESL. These approaches, consisting in skull conductivity and geometry modeling simplifications, make use of X-ray computed tomography (CT) and magnetic resonance (MR) images to generate seven different head models. A head model with an accurately segmented skull from CT images, including spongy and compact bone compartments as well as some air-filled cavities, was used as the reference model. EEG simulations were performed for a configuration of 32 and 128 electrodes, and for both noiseless and noisy data. The results show that skull geometry simplifications have a larger effect on ESL than those of the conductivity modeling. This suggests that accurate skull modeling is important in order to achieve reliable results for ESL that are useful in a clinical environment. We recommend the following guidelines to be taken into account for skull modeling in the generation of subject-specific head models: (i) If CT images are available, i.e., if the geometry of the skull and its different tissue types can be accurately segmented, the conductivity should be modeled as isotropic heterogeneous. The spongy bone might be segmented as an erosion of the compact bone; (ii) when only MR images are available, the skull base should be represented as accurately as possible and the conductivity can be modeled as isotropic heterogeneous, segmenting the spongy bone directly from the MR image; (iii) a large number of EEG electrodes should be used to obtain high spatial sampling, which reduces the localization errors at realistic noise levels.

Investigating the effect of template head models on Event-Related Potential source localization: a simulation and real-data study.

Introduction: Event-Related Potentials (ERPs) are valuable for studying brain activity with millisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the head model used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks. Methods: Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset. Results: The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiform gyrus, a known face processing area, using the subject-specific models. Apart from the fusiform gyrus, the template models also reconstructed several other sources, illustrating the localization inaccuracies. Discussion: While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors.

Subclinical epileptiform activity in the Alzheimer continuum: association with disease, cognition and detection method.

BACKGROUND: Epileptic seizures are an established comorbidity of Alzheimer's disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. AIMS: 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. METHODS: Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. RESULTS: We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher's exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall-Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann-Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. CONCLUSION: We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. TRIAL REGISTRATION: Clinicaltrials.gov, NCT04131491. 12/02/2020.

Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy.

PURPOSE: Fifteen percent to 25% of patients with refractory epilepsy require invasive video-electroencephalography (EEG) monitoring (IVEM) to precisely delineate the ictal-onset zone. This delineation based on the recorded intracranial EEG (iEEG) signals occurs visually by the epileptologist and is therefore prone to human mistakes. The purpose of this study is to investigate whether effective connectivity analysis of intracranially recorded EEG during seizures provides an objective method to localize the ictal-onset zone. METHODS: In this study data were analyzed from eight patients who underwent IVEM at Ghent University Hospital in Belgium. All patients had a focal ictal onset and were seizure-free following resective surgery. The effective connectivity pattern was calculated during the first 20 s of ictal rhythmic iEEG activity. The out-degree, which is reflective of the number of outgoing connections, was calculated for each electrode contact for every single seizure during these 20 s. The seizure specific out-degrees were summed per patient to obtain the total out-degree. The electrode contact with the highest total out-degree was considered indicative of localization of the ictal-onset zone. This result was compared to the conclusion of the visual analysis of the epileptologist and the resected brain region segmented from postoperative magnetic resonance imaging (MRI). KEY FINDINGS: In all eight patients the electrode contact with the highest total out-degree was among the contacts identified by the epileptologist as the ictal onset. This contact, that we named "the driver," always laid within the resected brain region. Furthermore, the patient-specific connectivity patterns were consistent over the majority of seizures. SIGNIFICANCE: In this study we demonstrated the feasibility of correctly localizing the ictal-onset zone from iEEG recordings by using effective connectivity analysis during the first 20 s of ictal rhythmic iEEG activity.

Effects of acute psychosocial stress on source level EEG power and functional connectivity measures.

The usage of EEG to uncover the influence of psychosocial stressors (PSSs) on neural activity has gained significant attention throughout recent years, but the results are often troubled by confounding stressor types. To investigate the effect of PSSs alone on neural activity, we employed a paradigm where participants are exposed to negative peer comparison as PSS, while other possible stressors are kept constant, and compared this with a condition where participants received neutral feedback. We analyzed commonly used sensor level EEG indices (frontal theta, alpha, and beta power) and further investigated whether source level power and functional connectivity (i.e., the temporal dependence between spatially seperated brain regions) measures, which have to our knowledge not yet been used, are more sensitive to PSSs than sensor level-derived EEG measures. Our results show that on sensor level, no significant frontal power changes are present (all p's > 0.16), indicating that sensor level frontal power measures are not sensitive enough to be affected by only PSSs. On source level, we find increased alpha power (indicative of decreased cortical activity) in the left- and right precuneus and right posterior cingulate cortex (all p's < 0.03) and increased functional connectivity between the left- and right precuneus (p < 0.001), indicating that acute, trial based PSSs lead to decreased precuneus/PCC activity, and possibly indicates a temporary disruption in the self-referential neural processes of an individual.