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.

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.

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.

Semantic Categorization of Naming Responses Based on Prearticulatory Electrical Brain Activity.

PURPOSE: Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals. METHODS: We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity. RESULTS: Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas. CONCLUSIONS: Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.

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.

Effect of Healthy Aging and Gender on Syntactic Input Processing: A P600 Event-Related Potential Study.

PURPOSE: This study aimed to investigate the effect of healthy aging and gender, as well as the interaction, thereof, on syntactic input processing during sentence comprehension. This was achieved through the recording of the P600 event-related potential. METHOD: Sixty Flemish (native speakers of Dutch) participants (30 men and 30 women), equally distributed into three age groups (young, middle-aged, and older adults), were subjected to a visually presented word order violation task under simultaneous electro-encephalography recording. The task contained 60 sentences, of which half were grammatical and half contained a word order violation. P600 responses were analyzed for amplitude, latency, topographical distribution, and source localization. RESULTS: Regarding the effect of healthy aging, no age-related differences were found for the amplitude, onset latency, and topographical distribution of the P600 effect (difference wave). Although aging effects on the P600 effect amplitude were absent, a reduced P600 amplitude in response to both the grammatical and ungrammatical sentences was found, next to a reduced overall degree of source activation in linguistic regions of interest. Also, a reduced behavioral accuracy in response to the word order violation was observed in the older adults group. Regarding the effect of gender, females exhibited a larger P600 effect amplitude and a reduced behavioral accuracy compared to males. No gender-related differences were found for P600 effect onset latency, topographical distribution, and source activation. CONCLUSIONS: While this study demonstrates no effect of aging on the P600 effect, the lower behavioral response and absence of any activation shift argues against functional compensation. Moreover, although increased neural activation in women combined with their reduced behavioral accuracy may indicate the use of different cognitive strategies in men and women, source localization analysis could not objectify this hypothesis.

Aging and sex effects on phoneme perception: An exploratory mismatch negativity and P300 investigation.

BACKGROUND & AIMS: The mismatch negativity (MMN) and P300 event-related potentials (ERPs) have been studied in relation to phoneme discrimination and categorization, respectively. Although the effects of aging and sex on pure-tone perception have been widely investigated using these ERPs, evidence relating to phoneme perception is scarce. The current study aimed to provide insight into the effects of aging and sex on phoneme discrimination and categorization, as measured through the MMN and P300. METHOD: An inattentive and attentive oddball paradigm containing a phonemic articulation place contrast were administered during EEG registration in sixty healthy individuals (thirty males and females), of which an equal number of young (20-39 years), middle-aged (40-59 years) and elderly (60+ years) subjects were included. The amplitude, onset latency and topographical distribution of the MMN and P300 effect, as well as the amplitude of the P1-N1-P2 complex, were analyzed for age group and sex differences. RESULTS: With respect to aging, elderly subjects demonstrated a reduced MMN and P300 amplitude compared to the young group, whereas the scalp distribution of both components was unaffected. No aging effects on the P1-N1-P2 complex were found. In elderly individuals, the P300 was found to be delayed compared to the young group, while no such effect on MMN latency could be observed. No differences in MMN and P300 measures could be identified between males and females. CONCLUSION: Differential effects of aging were found on the MMN and P300, specifically in terms of latency, in relation to phoneme perception. In contrast, sex was found to scarcely affect both processes.

Asymmetry of sleep electrophysiological markers in patients with focal epilepsy.

Sleep can modulate epileptic activities, but our knowledge of sleep perturbation by epilepsy remains sparse. Interestingly, epilepsy and sleep both present with defining electrophysiological features in the form of specific graphoelements on EEG. This raises the possibility to identify, within ongoing EEG activity, how epilepsy impacts and disrupts sleep. Here, we asked whether the presence of a lateralized epileptic focus interferes with the expression of the dominant electrophysiological hallmarks of sleep: slow oscillations, slow waves and spindles. To this aim, we conducted a cross-sectional study and analysed sleep recordings with surface EEG from 69 patients with focal epilepsy (age range at EEG: 17-61 years, 29 females, 34 left focal epilepsy). Comparing patients with left and right focal epilepsy, we assessed inter-hemispheric asymmetry of sleep slow oscillations power (delta range, 0.5-4 Hz); sleep slow wave density; amplitude, duration and slope; and spindle density, amplitude, duration as well as locking to slow oscillations. We found significantly different asymmetries in slow oscillation power (P < 0.01); slow wave amplitude (P < 0.05) and slope (P < 0.01); and spindle density (P < 0.0001) and amplitude (P < 0.05). To confirm that these population-based differences reflect actual patient-by-patient differences, we then tested whether asymmetry of sleep features can classify laterality of the epileptic focus using a decision tree and a 5-fold cross-validation. We show that classification accuracy is above chance level (accuracy of 65%, standard deviation: 5%) and significantly outperforms a classification based on a randomization of epileptic lateralization (randomization data accuracy: 50%, standard deviation 7%, unpaired t-test, P < 0.0001). Importantly, we show that classification of epileptic lateralization by the canonical epileptic biomarker, i.e. interictal epileptiform discharges, improves slightly but significantly when combined with electrophysiological hallmarks of physiological sleep (from 75% to 77%, P < 0.0001, one-way ANOVA + Sidak's multiple comparisons test). Together, we establish that epilepsy is associated with inter-hemispheric perturbation of sleep-related activities and provide an in-depth multi-dimensional profile of the main sleep electrophysiological signatures in a large cohort of patients with focal epilepsy. We provide converging evidence that the underlying epileptic process interacts with the expression of sleep markers, in addition to triggering well-known pathological activities, such as interictal epileptiform discharges.

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.

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.

Effects of Healthy Aging and Gender on the Electrophysiological Correlates of Semantic Sentence Comprehension: The Development of Dutch Normative Data.

PURPOSE: The clinical use of event-related potentials in patients with language disorders is increasingly acknowledged. For this purpose, normative data should be available. Within this context, healthy aging and gender effects on the electrophysiological correlates of semantic sentence comprehension were investigated. METHOD: One hundred and ten healthy subjects (55 men and 55 women), divided among three age groups (young, middle aged, and elderly), performed a semantic sentence congruity task in the visual modality during electroencephalographic recording. RESULTS: The early visual complex was affected by increasing age as shown by smaller P2 amplitudes in the elderly compared to the young. Moreover, the N400 effect in the elderly was smaller than in the young and was delayed compared to latency measures in both middle-aged and young subjects. The topography of age-related amplitude changes of the N400 effect appeared to be gender specific. The late positive complex effect was increased at frontal electrode sites from middle age on, but this was not statistically significant. No gender effects were detected regarding the early P1, N1, and P2, or the late positive complex effect. CONCLUSION: Especially aging effects were found during semantic sentence comprehension, and this from the level of perceptual processing on. Normative data are now available for clinical use.

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.

Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure.

Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient's cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.

Changes of interictal epileptiform discharges during medication withdrawal and seizures: A scalp EEG marker of epileptogenicity.

Objective: To determine the influence of antiseizure medication (ASM) withdrawal on interictal epileptogenic discharges (IEDs) in scalp-EEG and seizure propensity. Methods: We included 35 adult unifocal epilepsy patients admitted for presurgical evaluation in the EEG and Epilepsy Unit of Geneva between 2016 and 2020, monitored for at least 5 days. ASM was individually tapered down, and automated IED detection was performed using Epilog PreOp (Epilog NV, Belgium, Ghent). We compared spike rate per hour (SR) at day 1 when patients were on full medication (baseline) with SR at the day with the lowest dose of medication. To determine possible peri-ictal changes of SR, we compared SR 8 h before and after a seizure with the SR at the same time of the baseline day. Results: Our results showed a significant increase in spiking activity in the day of lowest drug load if compared to spike rate at day on full medication (p < 0.001). The total amount of spikes during 24 h correlated significantly with seizure occurrence (p < 0.0001). We also revealed significant increase in peri-ictal SR, in particular 2-4 h preceding a seizure (p = 0.05) extending up to 3 h after the seizure (p = 0.03) with a short decrease just before seizure occurrence. Conclusions: Our results suggest that SR increases with medication withdrawal and particularly before and after seizures. There is a complex pattern of increase and decrease around seizure onset which explains divergent results in previous studies. Significance: Precise spike counting at similar circadian periods for a patient could help to determine the risk of seizure occurrence in a personalized fashion.

The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies.

Whereas the link between psychosocial stress and health complications has long been established, the influence of psychosocial stress on brain activity is not yet completely understood. Electroencephalography (EEG) has been regularly employed to investigate the neural aspects of the psychosocial stress response, but these results have not yet been unified. Therefore, in this article, we systematically review the current EEG literature in which spectral analyses were employed to investigate the neural psychosocial stress response and interpret the results with regard to the three stress phases (anticipatory, reactive, and recovery) in which the response can be divided. Our results show that three EEG measures, alpha power, beta power and frontal alpha asymmetry (FAA), are commonly utilized and that alpha power consistently decreases, beta power shows a tendency to increase, and FAA varies inconsistently. We furthermore found that whereas changes in alpha power are independent of the stress phase, and changes in beta power show a relative stress phase independent trend, other EEG measures such as delta power, theta power, relative gamma and theta-alpha power ratio show less stress phase independent changes. Meta-analyses conducted on alpha power, beta power and FAA further revealed a significant effect size (hedge's g = 0.6; p = 0.001) for alpha power, but an insignificant effect size for beta power (hedge's g = -0.31; p = 0.29) and FAA (hedge's g = 0.01, p = 0.93). From our results, it can be concluded that psychosocial stress results in significant changes in some spectral EEG indices, but that more research is needed to further uncover the precise (temporal) mechanisms underlying these neural responses.

Therapy-Induced Electrophysiological Changes in Primary Progressive Aphasia: A Preliminary Study.

Aims: This preliminary study aimed to investigate therapy-induced electrophysiological changes in persons with primary progressive aphasia (PPA). The investigated event-related potential (ERP) components associated with language processing were the mismatch negativity, P300, N400, and P600. Methods: A linguistic ERP test battery and standardized language assessment were administered in four patients with PPA of which two received speech-language therapy (SLT) and two did not receive therapy. The battery was administered twice with approximately 6 months in between in each patient. The results of the follow-up assessments were compared to the results of the initial assessments. Results: Although the results of the behavioral language assessment remained relatively stable between the initial and follow-up assessments, changes in the mean amplitudes, onset latencies, and duration of the ERP components were found in the four patients. In the two patients that did not receive SLT, an increased delay in 50% and a decreased mean amplitude in 25% of the measured ERP components were found. The electrophysiological changes found in the patients that received SLT were variable. Interestingly, the mismatch negativity and the N400 effect elicited by the categorical priming paradigm were less delayed and had an increased mean amplitude at the follow-up assessment in the patient with the non-fluent variant who received SLT. In this patient, the P600 component was absent at the initial assessment but present at the follow-up assessment. Conclusion: Although no clear patterns in electrophysiological changes between patients who received SLT and patients who did not receive SLT were found by our preliminary study, it seems like the SLT induced improvements or compensation mechanisms in some specific language comprehension processes in the patient with the NFV. The results of this study are still preliminary because only four heterogeneous patients were included. Future studies should include larger patient groups of the three clinical variants because the therapy-induced electrophysiological changes might differ depending on the clinical variant and the underlying pathology.

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.

Evaluation of Directed Causality Measures and Lag Estimations in Multivariate Time-Series.

The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their "comfort zone." PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine.