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Parent and child have been shown to synchronize their behaviors and physiology during social interactions. This synchrony is an important marker of their relationship quality and subsequently the child’s social and emotional development. Therefore, understanding the factors that influence parent–child synchrony is an important undertaking. Using EEG hyperscanning, this study investigated brain-to-brain synchrony in mother-child dyads when they took turns performing a visual search task and received positive or negative feedback. In addition to the effect of feedback valence, we studied how their assigned role, i.e., observing or performing the task, influenced synchrony. Results revealed that mother-child synchrony was higher during positive feedback relative to negative feedback in delta and gamma frequency bands. Furthermore, a main effect was found for role in the alpha band with higher synchrony when a child observed their mother performing the task compared to when the mother observed their child. These findings reveal that a positive social context could lead a mother and child to synchronize more on a neural level, which could subsequently improve the quality of their relationship. This study provides insight into mechanisms that underlie mother-child brain-to-brain synchrony, and establishes a framework by which the impact of emotion and task demand on a dyad’s synchrony can be investigated.

Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

The possibility of epileptiform activity generation by the thalamocortical neuronal network after focal brain injuries, including traumatic brain injury (TBI), is actively debated. Presumably, posttraumatic spike–wave discharges (SWDs) involve a cortico-thalamocortical neuronal network. Differentiation of posttraumatic and idiopathic (i.e., spontaneously generated) SWDs is imperative for understanding posttraumatic epileptogenic mechanisms. Experiments were performed on male Sprague-Dawley rats with electrodes implanted into the somatosensory cortex and the thalamic ventral posterolateral nucleus. Local field potentials were recorded for 7 days before and 7 days after TBI (lateral fluid percussion injury, 2.5 atm). The morphology of 365 SWDs (89 idiopathic before craniotomy, and 262 posttraumatic that appeared only after TBI) and their appearance in the thalamus were analyzed. The occurrence of SWDs in the thalamus determined their spike–wave form and bilateral lateralization in the neocortex. Posttraumatic discharges were characterized by more “mature” characteristics as compared to spontaneously generated discharges: higher proportions of bilateral spreading, well-defined spike–wave form, and thalamus involvement. Based on SWD parameters, the etiology could be established with an accuracy of 75% (AUC 0.79). Our results support the hypothesis that the formation of posttraumatic SWDs involves a cortico-thalamocortical neuronal network. The results form a basis for further research of mechanisms associated with posttraumatic epileptiform activity and epileptogenesis.

The integration of word meaning into an unfolding utterance representation is a core operation of incremental language comprehension. There is considerable debate, however, as to which component of the ERP signal— the N400 or the P600— directly reflects integrative processes, with far reaching consequences for the temporal organization and architecture of the comprehension system. Multi- stream models maintaining the N400 as integration crucially rely on the pres-ence of a semantically attractive plausible alternative interpretation to account for the absence of an N400 effect in response to certain semantic anomalies, as reported in previous studies. The single- stream Retrieval– Integration account posits the P600 as an index of integration, further predicting that its amplitude varies continuously with integrative effort. Here, we directly test these competing hypotheses using a context manipulation design in which a semantically attrac-tive alternative is either available or not, and target word plausibility is varied across three levels. An initial self- paced reading study revealed graded reading times for plausibility, suggesting differential integration effort. A subsequent ERP study showed no N400 differences across conditions, and that P600 amplitude is graded for plausibility. These findings are inconsistent with the interpretation of the N400 as an index of integration, as no N400 effect emerged even in the absence of a semantically attractive alternative. By contrast, the link between plausibility, reading times, and P600 amplitude supports the view that the P600 is a continuous index of integration effort. More generally, our results support a single- stream architecture and eschew the need for multi- stream accounts.

Schizophrenia has been associated with dysfunction in information integration/ segregation dynamics. One of the neural networks whose role has been most investigated in schizophrenia is the default mode network (DMN). In this study, we have explored the possible alteration of integration and segregation dynamics in individuals diagnosed with schizophrenia with respect to healthy controls, based on the study of the topological properties of the graphs derived from the functional connectivity between the nodes of the DMN in the resting state. Our results indicate that the patients show a diminution of the modularity of the DMN and a higher global efficiency, in sparse graphs. Our data emphasise the interest in studying temporal changes in network measures and are compatible with the hypothesis of randomization of functional networks in schizophrenia., Agencia Estatal de Investigacion, AEI PID2019-105145RB-I00

デジタル化が急速に進展する今日，身体感覚を育むモノづくり教育の重要性を忘れてはならない。モノづくりにおける手作業や機械加工は，単なる工学的な知識や技能の習得だけでなく，微妙な力加減や，精度を見極める感覚，危険を伴う処置など，直感力・想像力・集中力・判断力・閃き・感性といった様々な「身体知」を必要とする。しかし，個人固有の感覚に基づく「身体知」を抽出し，評価することは極めて難しく，教育現場においてこれらを客観的に判断する指標はない。本研究は，モノづくり活動中の学生の脳波波形に着目し，ヘッドバンド型簡易脳波計「Muse」を用いて検証を試みた。その結果，PC操作時の活動と身体を使うモノづくり活動が異なる特徴を持つことが脳波の波形から見出された。これらの結果から，教育現場で脳波測定の活用の可能性についても報告する。In the age of digital technology, the significance of manufacturing education should not be overlooked. Engineering knowledge and skills are the basis. However, embodied expertise is more important when operating the manual processing machine, which requires delicate adjustment. Cultivating physical sense or "embodied wisdom", such as intuition, imagination, concentration, judgement, inspiration, and sensibility, is an essential pillar of engineering education. This work-in-progress research focuses on student brain activity during manufacturing activities. Using the portable, headband-type devices called "MUSE," this study collected data on student brain activity during different types of manufacturing activities. The current study includes a relatively small sample size (N=5); however, it does identify some preliminary trends. Different reactions were observed in the computer-based study and manufacturing activities. This exploratory study provides a foundation for future studies and the potential for such a portable device to provide insight into educational effects that manufacturing activities may have.

In this paper, a multi-layer reservoir neural network is designed using an asynchronous cellular automaton neuron model. Furthermore, a learning method of the network based on the simulated annealing is proposed. It is shown that the network with reservoir layers can classify a set of several EEG. In addition, the classification performance of networks with various configurations were compared, and it is shown the best performing network is a two-layer reservoir neural network.

Review, 作業活動に没頭することによるリラックス効果は、これまで臨床的知見として報告されてきたが、神経科学的側面からは検討されてこなかった。我々はこれまで手工芸活動中の脳波と自律神経活動を測定することによって、手工芸活動への集中が脳内ネットワークにどのような影響を与えるか、そのメカニズムについて検討した。その結果、Frontal midline theta rhythm が出現した手工芸活動中には、実行系の脳内ネットワークが増加し、自律神経活動の変化からリラックス効果が確認された。The relaxing effect of a state of absorption during occupational activities has been reported as a clinical finding, but has not been investigated from a neuroscientific aspect. We aimed to elucidate the mechanism by measurement of EEG and autonomic nervous activity during manual arts and crafts activities. We investigated the mechanisms of how concentration on craft activities affects brain networks. The results showed that the brain network of the executive system increased during craft activities in which the frontal midline theta rhythm emerged, and changes in autonomic nervous activity confirmed a relaxation effect.

Background and purpose: Among the most common post-COVID symptoms, many patients experienced subjective cognitive deficit, commonly named "brain fog," that might be present also in those individuals without severe acute COVID-19 respiratory involvement. Some studies have investigated some of the mechanisms that might be associated with the brain fog with objective techniques including transcranial magnetic stimulation and neuroimaging. Methods: The aim of this study was to investigate the presence of electroencephalographic (EEG) alterations in people with post-COVID self-reported cognitive deficit. Results: Out of the 90 patients attending the post-COVID neurology ambulatory service, twenty patients presenting brain fog at least 4 weeks after acute non-severe COVID-19 infection, and without previous history of epilepsy, were investigated with 19-channel EEG, Montreal Cognitive Assessment (MoCA), and magnetic resonance imaging (MRI). EEG was found altered in 65% of the sample, among which 69% presented a slowing activity and 31% were characterized by epileptic discharges principally in the frontal areas. None of the patients showed DWI MRI lesions. Conclusions: These findings highlight the usefulness of EEG analysis to objectively describe possible neurophysiological abnormalities in post-COVID patients presenting subjective cognitive deficit.

Published: 01 February 2023 The intention to name an object modulates neural responses during object recognition tasks. However, the nature of this modulation is still unclear. We established whether a core operation in language, i.e. lexical access, can be observed even when the task does not require language (size-judgment task), and whether response selection in verbal versus non-verbal semantic tasks relies on similar neuronal processes. We measured and compared neuronal oscillatory activities and behavioral responses to the same set of pictures of meaningful objects, while the type of task participants had to perform (picture-naming versus size-judgment) and the type of stimuli to measure lexical access (cognate versus non-cognate) were manipulated. Despite activation of words was facilitated when the task required explicit word-retrieval (picture-naming task), lexical access occurred even without the intention to name the object (non-verbal size-judgment task). Activation of words and response selection were accompanied by beta (25–35 Hz) desynchronization and theta (3–7 Hz) synchronization, respectively. These effects were observed in both picture-naming and size-judgment tasks, suggesting that words became activated via similar mechanisms, irrespective of whether the task involves language explicitly. This finding has important implications to understand the link between core linguistic operations and performance in verbal and non-verbal semantic tasks. This work was supported by a Sir Henry Wellcome Postdoctoral Fellowship awarded to E.B. (Grant reference number: 210924/Z/18/Z). C.D.M received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No: 819093) and the Spanish Ministry of Economy and Competitiveness (PID2020-113926GB-I00). The authors acknowledge the Basque Government through the BERC 2022-2025 program and the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S.

The functioning of language as a communication system is described by a variety of linguistic theories which are not always consistent with each other and often do not rely on cognitive and/or neurobiological data. One of the most striking examples is the “null morpheme” proposed by the theory of universal grammar, which is a concept that has only an abstract meaning as it has no phonological implementation (for example, the null ending of nouns in some cases: cf. stol-∅ (tableNOM) vs. stol-a (tableGEN). With the aim of testing the processing of null morphemes by the brain, we conducted an EEG experiment in which subjects were presented with heard phrases with null (e.g., on kupil-∅) (he bought-∅) or marked (ona kupil-a) (she boughtFEM) verb endings, with correct and incorrect gender agreement between the pronoun and the verb (e.g., on kupil-∅ (he bought-∅) vs. *ona kupil-∅ (she bought-∅, where the correct ending is –a)) and the verb presented alone (kupil-∅) (bought-∅) as a control condition. Event-related potentials analysis results demonstrated an increase in cerebral responses where the null ending disagreed. The latency of this response was ~200 msec, corresponding to the ELAN component, which reflects the early automatic processing of (morpho)syntactic information. These results can be explained by the theory of morphosyntactic priming and indicate that a representation of the null morpheme (ending) exists and is activated in the process of speech perception.

Although humans are considered to be face experts, there is a well-established reliable variation in the degree to which neurotypical individuals are able to learn and recognise faces. While many behavioural studies have characterised these differences, studies that seek to relate the neuronal response to standardised behavioural measures of ability remain relatively scarce, particularly so for the time-resolved approaches and the early response to face stimuli. In the present study we make use of a relatively recent methodological advance, multi-variate pattern analysis (MVPA), to decode the time course of the neural response to faces compared to other object categories (inverted faces, objects). Importantly, for the first time, we directly relate metrics of this decoding assessed at the individual level to gold-standard measures of behavioural face processing ability assessed in an independent task. Thirty-nine participants completed the behavioural Cambridge Face Memory Test (CFMT), then viewed images of faces and houses (presented upright and inverted) while their neural activity was measured via electroencephalography. Significant decoding of both face orientation and face category were observed in all individual participants. Decoding of face orientation, a marker of more advanced face processing, was earlier and stronger in participants with higher levels of face expertise, while decoding of face category information was earlier but not stronger for individuals with greater face expertise. Taken together these results provide a marker of significant differences in the early neuronal response to faces from around 100 ms post stimulus as a function of behavioural expertise with faces. Fundação para a Ciência e Tecnologia - FCT info:eu-repo/semantics/publishedVersion

Mental imagery (MI) is gaining attention as a strategy towards endogenous brain stimulation for improving motor skill. Neurofeedback (NF) is commonly used to guide MI in order to activate the relevant brain networks. The current study investigates an individualized EEG-based method for NF through broad consideration of interactions between different brain networks. We selected the change in brain functional connectivity (FC) as an objective neurophysiological measure of change in motor skill during a longitudinal physical training (PT) program. Digital tracing tasks were developed for skill training and the spatial error in tracing was used to gauge the change in skill. We used partial least squares algorithms to find the most robust contributing networks towards correlation between the resting state FC and the acquired motor skill. We used the network with the largest margin for increasing FC as the candidate for NF training while experimenting with MI during a neurofeedback training program. The participant was informed of the changes in instantaneous FC through real-time audio feedback to help guide the MI. We showed over 20% reduction in tracing error through neurofeedback training alone, without any additional PT. We also showed retention of improvement in skill for several days after the completion of neurofeedback training. Our proposed methodology shows promise for a highly individualized approach towards improvement in motor skill. Given that EEG is an accessible health and wellness technology, such a method could provide a practical complementary option towards personalized therapeutic strategies to improve motor function., IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, ISSN:1534-4320, ISSN:1558-0210

Background: Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction. Methods: With a systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions. Results: 15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies resulted in an area under the curve of 0.91, a sensitivity of 83 % (95 % CI 74–89 %), and a specificity of 86 % (95 % CI 81–90 %). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable. Limitations: Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy. Conclusions: Electroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD. PROSPERO registration number: CRD42021268169.

The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the combination with conventional machine learning methods. Thus, channel selection combined with deep learning methods can be further analyzed in the field of seizure prediction. Given this, in this work, a novel iEEG-based deep learning method of One-Dimensional Convolutional Neural Networks (1D-CNN) combined with channel increment strategy was proposed for the effective seizure prediction. First, we used 4-sec sliding windows without overlap to segment iEEG signals. Then, 4-sec iEEG segments with an increasing number of channels (channel increment strategy, from one channel to all channels) were sequentially fed into the constructed 1D-CNN model. Next, the patient-specific model was trained for classification. Finally, according to the classification results in different channel cases, the channel case with the best classification rate was selected for each patient. Our method was tested on the Freiburg iEEG database, and the system performances were evaluated at two levels (segment- and event-based levels). Two model training strategies (Strategy-1 and Strategy-2) based on the K-fold cross validation (K-CV) were discussed in our work. (1) For the Strategy-1, a basic K-CV, a sensitivity of 90.18%, specificity of 94.81%, and accuracy of 94.42% were achieved at the segment-based level. At the event-based level, an event-based sensitivity of 100%, and false prediction rate (FPR) of 0.12/h were attained. (2) For the Strategy-2, the difference from the Strategy-1 is that a trained model selection step is added during model training. We obtained a sensitivity, specificity, and accuracy of 86.23%, 96.00% and 95.13% respectively at the segment-based level. At the event-based level, we achieved an event-based sensitivity of 98.65% with 0.08/h FPR. Our method also showed a better performance in seizure prediction compared to many previous studies and the random predictor using the same database. This may have reference value for the future clinical application of seizure prediction. peerReviewed

Objective: The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel. Methods: First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters’ tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters. Main results: The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection. Significance: Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios.

Contains fulltext : 247442.pdf (Publisher’s version ) (Open Access) Background: Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates. Methods: The biomarker in this study was developed in a heterogeneous clinical sample (N=4249), and first applied to two large transfer datasets, a priori stratifying young males (

Background: Previous studies suggest that different metabotropic glutamate (mGlu) receptor subtypes are potential drug targets for the treatment of absence epilepsy. However, no information is available on mGlu3 receptors. Objective: To examine whether (i) abnormalities changes of mGlu3 receptor expression/signaling are found in the somatosensory cortex and thalamus of WAG/Rij rats developing spontaneous absence seizures; (ii) selective activation of mGlu3 receptors with LY2794193 affects the number and duration of spike-wave discharges (SWDs) in WAG/Rij rats; and (iii) a genetic variant of GRM3 (encoding the mGlu3 receptor) is associated with absence epilepsy. Methods: Animals: immunoblot analysis of mGlu3 receptors, GAT-1, GLAST, and GLT-1; real-time PCR analysis of mGlu3 mRNA levels; assessment of mGlu3 receptor signaling; EEG analysis of SWDs; assessment of depressive-like behavior. Humans: search for GRM3 and GRM5 missense variants in 196 patients with absence epilepsy or other IGE/GGE Idiopathic Generalized Epilepsy (IGE)/ Genetic Generalized Epilepsy (GGE) and 125,748 controls. Results: mGlu3 protein levels and mGlu3-mediated inhibition of cAMP formation were reduced in the thalamus and somatosensory cortex of pre-symptomatic (25-27 days old) and symptomatic (6-7 months old) WAG/Rij rats compared to age-matched controls. Treatment with LY2794193 (1 or 10 mg/kg, i.p.) reduced absence seizures and depressive-like behavior in WAG/Rij rats. LY2794193 also enhanced GAT1, GLAST, and GLT-1 protein levels in the thalamus and somatosensory cortex. GRM3 and GRM5 gene variants did not differ between epileptic patients and controls. Conclusions: We suggest that mGlu3 receptors modulate the activity of the cortico-thalamo-cortical circuit underlying SWDs and that selective mGlu3 receptor agonists are promising candidate drugs for absence epilepsy treatment.

Background and purpose: Haemato­poietic stem cell transplantation (HSCT) is one of the most effective treatment methods for many malignant and non-malignant diseases. In this study, we aimed to detect electroencephalographic (EEG) anomalies at an early stage in patients who underwent allogeneic and autologous HSCT and required the management of potentially life-threatening non-convulsive seizures.. Methods: The study was conducted with 53 patients. The age, gender, HSCT type (allogeneic or autologous), and treatment regimens applied before and after HSCT were recorded. All patients underwent EEG monitoring twice, once on the first day of hospitalization and again one week after conditioning regimens began and HSCT was performed.. Results: When the pre-transplant EEG findings were examined, 34 (64.2%) patients had normal EEGs and 19 (35.8%) had abnormal EEGs. After transplantation, 27 (50.9%) had normal EEG findings, 16 (30.2%) had a basic activity disorder, 6 (11.3%) had a focal anomaly, and 4 (7.5%) had a ge­ne­ra­lised anomaly. In the allogeneic group, the anomaly rate in post-transplant EEGs was significantly higher than that in the auto­lo­gous group (p. Conclusion: It is important to consider the likelihood of epileptic seizures in the clinical follow-up of HSCT patients. EEG monitoring is crucial for the early diagnosis and treat­ment of such non-convulsive clinical ma­ni­festations..

Bu çalışmada, Harmoni Arama algoritmasının (Harmony Search algorithm, HSA) mevcut veriden faydalanarak başlangıç çözümlerini üretme yaklaşımı ile güçlendirilmiş varyantı olan Kaynak-Bağlantılı Harmoni Arama algoritmasının (Source-Linked HSA, slinkHSA) performansı elektroensefalografi (EEG) sinyallerinde gürültü minimizasyonu gerektiren büyük veri optimizasyonu üzerinden incelenmiştir. slinkHSA ile elde edilen sonuçlar diğer meta-sezgisel teknikler tarafından bulunan sonuçlar üzerinden kıyaslanmıştır. Karşılaştırmalar, başlangıç harmonilerini EEG sinyalleri kullanılarak üretmenin çözümlerinin kalitesini önemli ölçüde katkıda bulunduğunu ve algoritmanın yakınsama hızını artırdığını göstermiştir., In this study, the performance of Source-Linked Harmony Search algorithm (slinkHSA) that is a new variant of the Harmony Search algorithm (HSA) powered with the usage of existing data for generating initial solutions was investigated over a big data optimization problem requiring minimization of measurement noise for electroencephalography (EEG) signals. The results obtained by the mentioned HSA variant were also compared to the results of other meta-heuristic techniques. Comparative studies showed that generating initial harmonies by guiding the existing EEG signals significantly contributes to the qualities of the solutions and increases the convergence speed of the algorithm.

Şizofreninin (SZ) erken tanısı hastaların daha etkili tedavi görmelerine olanak sağlayarak, yaşam kalitelerini artırır. Ancak, hastalığın karmaşık ve heterojen bulguları erken klinik tanıları sekteye uğratmaktadır. Bu bağlamda elektroansefalogram (EEG), olası SZ için alternatif bir tanı aracı olarak kullanılmaktadır. EEG tekniğinin yüksek temporal çözünürlük sunmasından dolayı, bilişsel ve davranışsal süreçlerin kortikal aktivitelere yansımaları başarılı bir şekilde irdelenebilir. Bu çalışmada, EEG işaretlerinin permütasyon entropi (PE) tabanlı karmaşıklık analizi ile SZ anomalilerin sınıflandırılması ve değerlendirilmesi amaçlanmıştır. PE analizleri, SZ semptomları sergileyen 45 adölesan birey ile 39 sağlıklı katılımcıdan alınan EEG kayıtları üzerinde uygulanmıştır. Özellik çıkarımı için delta, teta, alfa, beta ve gama dalgaları olmak üzere tüm alt bantların PE değerleri hesaplanmıştır. Sınıflandırıcı model olarak ise çok katmanlı perseptron sinir ağları (MLPNN) kullanılmıştır. Sınıflandırma işlemi her bir elektrot için ayrı bir şekilde yürütülmüştür. Böylelikle, SZ tespitinde etkin kanallar belirlenmiş ve bu kanallara ilişkin kapsamlı istatistiksel analizler uygulanmıştır. Bulgular, SZ tespitinin P4 ve T6 elektrot konumlarından etkin bir şekilde yapılabileceğini göstermiştir. Sınıflandırma doğrulukları P4 ve T6 kanalları için sırasıyla %87.2 ve %86.8 olarak elde edilmiştir. Ayrıca, PE dağılımlarının istatistiksel sonuçları, gama aktiviteleri için SZ hastalarında karmaşık nörodavranışsal özelliklerin önemli ölçüde azaldığını göstermiştir., Early diagnosis of schizophrenia (SZ) can improve the quality of life by allowing patients to receive more effective treatment. The complexity and heterogeneity of symptoms related to the disease hinder early clinical diagnosis. In this context, electroencephalogram (EEG) is used as an alternative diagnostic tool for the probable SZ. Due to the high temporal resolution of the EEG technique, the reflections of cognitive and behavioral processes on cortical activities can be successfully investigated. In this study, it is aimed to classify and evaluate SZ anomalies by permutation entropy (PE) based complexity analysis of EEG signals. PE analyzes were performed on EEG recordings from 45 adolescents with symptoms of SZ and 39 healthy participants. For feature extraction, PE values are computed for all sub-bands in the EEG as delta, theta, alpha, beta, and gamma waves. A multilayer perceptron neural network (MLPNN) is used as a classifier model. The classification process was performed separately for each scalp electrode. Thus, comprehensive statistical analyzes of the PE distributions related to the efficient channels were performed. The results demonstrate that SZ can be detected efficiently from the P4 and T6 electrode locations. The classification accuracy of 87.2% and 86.8% is obtained for P4 and T6 channels, respectively. Moreover, statistical results of PE distributions showed that complex neurobehavioral features significantly decrease in the case of SZ patients for gamma activities.

Rationale Preclinical studies indicate that high-frequency oscillations, above 100 Hz (HFO:100–170 Hz), are a potential translatable biomarker for pharmacological studies, with the rapid acting antidepressant ketamine increasing both gamma (40–100 Hz) and HFO. Objectives To assess the effect of the uncompetitive NMDA antagonist ketamine, and of D-cycloserine (DCS), which acts at the glycine site on NMDA receptors on HFO in humans. Methods We carried out a partially double-blind, 4-way crossover study in 24 healthy male volunteers. Each participant received an oral tablet and an intravenous infusion on each of four study days. The oral treatment was either DCS (250 mg or 1000 mg) or placebo. The infusion contained 0.5 mg/kg ketamine or saline placebo. The four study conditions were therefore placebo-placebo, 250 mg DCS-placebo, 1000 mg DCS-placebo, or placebo-ketamine. Results Compared with placebo, frontal midline HFO magnitude was increased by ketamine (p = 0.00014) and 1000 mg DCS (p = 0.013). Frontal gamma magnitude was also increased by both these treatments. However, at a midline parietal location, only HFO were increased by DCS, and not gamma, whilst ketamine increased both gamma and HFO at this location. Ketamine induced psychomimetic effects, as measured by the PSI scale, whereas DCS did not increase the total PSI score. The perceptual distortion subscale scores correlated with the posterior low gamma to frontal high beta ratio. Conclusions Our results suggest that, at high doses, a partial NMDA agonist (DCS) has similar effects on fast neural oscillations as an NMDA antagonist (ketamine). As HFO were induced without psychomimetic effects, they may prove a useful drug development target.

In the human brain, physiological aging is characterized by progressive neuronal loss, leading to disruption of synapses and to a degree of failure in neurotransmission and information flow. However, there is increasing evidence to support the notion that the aged brain has a remarkable level of resilience (i.s. ability to reorganize itself), with the aim of preserving its physiological activity. It is therefore of paramount interest to develop objective markers able to characterize the biological processes underlying brain aging in the intact human, and to distinguish them from brain degeneration associated to age-related neurological progressive diseases like Alzheimer's disease. EEG, alone and combined with transcranial magnetic stimulation (TMS-EEG), is particularly suited to this aim, due to the functional nature of the information provided, and thanks to the ease with which it can be integrated in ecological scenarios including behavioral tasks. In this review, we aimed to provide the reader with updated information about the role of modern methods of EEG and TMS-EEG analysis in the investigation of physiological brain aging and Alzheimer's disease. In particular, we focused on data about cortical connectivity obtained by using readouts such graph theory network brain organization and architecture, and transcranial evoked potentials (TEPs) during TMS-EEG. Overall, findings in the literature support an important potential contribution of such neurophysiological techniques to the understanding of the mechanisms underlying normal brain aging and the early (prodromal/pre-symptomatic) stages of dementia.

Neuronal excitation/inhibition (E/I) imbalance is a potential cause of neuronal network malfunctioning in Alzheimer's disease (AD), contributing to cognitive dysfunction. Here, we used a novel approach combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to probe cortical excitability in different brain areas known to be directly involved in AD pathology.We performed TMS-EEG recordings targeting the left dorsolateral prefrontal cortex (l-DLPFC), the left posterior parietal cortex (l-PPC), and the precuneus (PC) in a large sample of patients with mild-to-moderate AD (n = 65) that were compared with a group of age-matched healthy controls (n = 21).We found that patients with AD are characterized by a regional cortical hyperexcitability in the PC and, to some extent, in the frontal lobe, as measured by TMS-evoked potentials. Notably, cortical excitability assessed over the l-PPC was comparable between the 2 groups. Furthermore, we found that the individual level of PC excitability was associated with the level of cognitive impairment, as measured with Mini-Mental State Examination, and with corticospinal fluid levels of AβOur data provide novel evidence that precuneus cortical hyperexcitability is a key feature of synaptic dysfunction in patients with AD. The current results point to the combined approach of TMS and EEG as a novel promising technique to measure hyperexcitability in patients with AD. This index could represent a useful biomarker to stage disease severity and evaluate response to novel therapies. ANN NEUROL 2022.

BACKGROUND/AIMS: As epilepsy is a complex disease group, it is difficult to diagnose and classify. Oxidative stress plays a vital role in the pathogenesis of epilepsy. This study investigated the total oxidant/antioxidant status levels in patients with focal onset and generalized onset seizures. The results we obtained may help find the etiological cause in patients with seizure complaints and may guide their treatment. In addition, knowing the seizure type of the patient can give an idea about the prognosis of their disease.MATERIALS AND METHODS: The total number of patients included in this prospective study was 58. There were also 57 people in a control group. The patients were classified according to their type of seizure: focal or generalized onset. The serum oxidative stress index (OSI) and total oxidant/antioxidant status values of all patients and control group members were measured. The patients (focal/generalized groups) and control group members were compared.RESULTS: This prospective study was completed with 58 eligible patients who met the inclusion criteria. There were 57 people in the control group. Total oxidant status (TOS) and OSI levels were higher in the seizure groups compared to the control group (p

The electroencephalography (EEG) signal is a crucial part of Brain-Computer Interface (BCI) technology. Simply put, the BCI is a non-muscular channel for information transfer between the brain and other devices. The primary goal of BCIs is to restore some level of social interaction for those who are unable to use their mouths or hands because of neurological impairments. Classification of EEG signals is essential for many uses, including imaging of motor imagery, diagnosis of pharmacological effects, identification of emotions, prediction of seizures, detection of eye states, and many others. As a result, the construction of an autonomous solution in the medical arena necessitates a powerful classification model capable of efficiently processing the EEG information. Accurate diagnosis of an eye disease using EEG data is a challenging but essential task in medicine and daily life. The fundamental goal of this study is to develop a hybrid model based on machine learning that improves the accuracy with which the ocular status of stroke patients may be detected from EEG data. It can aid in finding and eliminating anomalies, and it can help in developing the robotic or smart machine-based answer to societal problems. To determine its usefulness and accuracy, this hybrid categorization model was compared to state-of-the-art machine learning methods. The experimental analysis proves that the suggested hybrid classification model outperforms the competition. The suggested hybrid model outperforms the state-of-the-art on every test and validation metric.

The main topic of study is mapping human cognition into automated analysis since it has exciting applications in practically every aspect of creating artificially intelligent devices. Studying electroencephalogram (EEG) patterns is the greatest approach to comprehending how the brain functions; hence a lot of research has been focused on this topic. It is challenging to create a generic affect classification system that can effectively deliver robust affect labeling to the EEG patterns since EEG recordings are subject-dependent and show variances according to external influences or kinds of recording devices. A unique generic framework for affect-based cognitive analysis is presented in the proposed study as a solution to this problem. The proposed system includes the following steps: EEG pattern, dataset, pre-processing, feature selection, an Enhanced Squirrel search optimized Naive Bayes (ESSOp-NB) step to reduce inter-class and intra-class variance, and finally, the processed pattern is sent to a trained classifier for classification into the proper effect categories. The effectiveness of well-known classifiers is evaluated using EEG data from single and multiple people from two separate datasets: Database for Emotion Analysis using Physiological Signals (DEAP) and SJTU Emotion EEG Dataset (SEED). The findings of the experiment indicate that the ESSOp-NB classifier is the most effective at categorizing data from both single and mixed participants. &nbsp

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots’ real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system’s performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

Afektivni poremećaji, emocionalni su poremećaji koji predstavljaju mentalne bolesti kao što je depresija. Elektroencefalografija neinvazivna je tehnika kojom se snima električna aktivnost mozga. U ovom radu analizirane su EEG snimke osoba dijagnosticiranih s afektivnim poremećajem te snimke zdravih osoba. Provedena je klasifikacija korištenjem entropije kao značajke signala modelima strojnog učenja. Affective disorders are emotional disorders that represent mental illnesses such as depression. Electroencephalography is a non-invasive technique that records the electrical activity of the brain. In this paper, EEG recordings of persons diagnosed with affective disorder and recordings of healthy persons were analyzed. The classification using entropy as a feature of the signal was performed by machine learning models.

The aim of this study was to investigate the potential impact of guided imagery (GI) on attentional control and cognitive performance and to explore the relationship between guided imagery, stress reduction, alpha brainwave activity, and attentional control using common cognitive performance tests. Executive function was assessed through the use of attentional control tests, including the anti-saccade, Stroop, and Go/No-go tasks. Participants underwent a guided imagery session while their brainwave activity was measured, followed by attentional control tests. The study’s outcomes provide fresh insights into the influence of guided imagery on brain wave activity, particularly in terms of attentional control. The findings suggest that guided imagery has the potential to enhance attentional control by augmenting the alpha power and reducing stress levels. Given the limited existing research on the specific impact of guided imagery on attention control, the study’s findings carry notable significance.

This paper discusses the design and development of a low-cost virtual reality (VR) based flight simulator with cognitive load estimation feature using ocular and EEG signals. Focus is on exploring methods to evaluate pilot’s interactions with aircraft by means of quantifying pilot’s perceived cognitive load under different task scenarios. Realistic target tracking and context of the battlefield is designed in VR. Head mounted eye gaze tracker and EEG headset are used for acquiring pupil diameter, gaze fixation, gaze direction and EEG theta, alpha, and beta band power data in real time. We developed an AI agent model in VR and created scenarios of interactions with the piloted aircraft. To estimate the pilot’s cognitive load, we used low-frequency pupil diameter variations, fixation rate, gaze distribution pattern, EEG signal-based task load index and EEG task engagement index. We compared the physiological measures of workload with the standard user’s inceptor control-based workload metrics. Results of the piloted simulation study indicate that the metrics discussed in the paper have strong association with pilot’s perceived task difficulty.

This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland–Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72–52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (

Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30–35% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system.

A variety of subjective measures have traditionally been used to assess the perception of physical exertion at work and related body responses. However, the current understanding of physical comfort experienced at work is very limited. The main objective of this study was first to investigate the magnitude of isometric arm forces exerted by females at different levels of physical comfort measured on a new comfort scale and, second, to assess their corresponding neural signatures expressed in terms of power spectral density (PSD). The study assessed PSDs of four major electroencephalography (EEG) frequency bands, focusing on the brain regions controlling motor and perceptual processing. The results showed statistically significant differences in exerted arm forces and the rate of perceived exertion at the various levels of comfort. Significant differences in power spectrum density at different physical comfort levels were found for the beta EEG band. Such knowledge can be useful in incorporating female users’ force requirements in the design of consumer products, including tablets, laptops, and other hand-held information technology devices, as well as various industrial processes and work systems.

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. However, the relationship between the ongoing experience of mind wandering and the individual biological signal is still unclear. To quantify subjects' subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering several dimensions of mind wandering. The aim of this work was to estimate associations between subjective experiences during the resting state session and electrical brain’s signal, focusing on EEG power, phase synchronization and topographical aspects. As it was showed in this work, different aspects of the same signal are related to both - different and the same - domains of ARSQ. Power aspect of EEG signal as evaluated with frequency principal component analysis showed that activity in theta frequency range was related to scores for Sleepiness, but not the phase, as evaluated with global field synchronization. On the other hand, phase synchronization of of beta frequency range, but not the power aspect correlated with domain of Comfort. And both, power and phase parameters of alpha band activity correlated with ARSQ domain of Comfort. Three different microstates – C, E and G – displayed associations with domain of Comfort, while microstate B and D correlated with domain of Self and microstate F correlated with Somatic Awareness.

Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD). We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD.

Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning methods based on EEG signals for emotion recognition lack consideration of important aspects and comprehensive analysis of feature extraction interactions. In this paper, we propose a novel model named ECA-CRNN for emotion recognition using EEG signals. Our model integrates the efficient channel attention (ECA-Net) module into our modified combination of a customized convolutional neural network (CNN) and gated circulation unit (GRU), which enables more comprehensive feature extraction, enhances the internal relationship between frequency bands and improves recognition performance. Additionally, we utilize four-dimensional data as input to our model, comprising temporal, spatial and frequency information. The test on the DEAP dataset demonstrates that it enhances the recognition accuracy of EEG signals in both arousal and valence to 95.70% and 95.33%, respectively, while also reducing the standard deviation during five-fold cross-validation to 1.16 and 1.45 for arousal and valence, respectively, surpassing most methods.

In passive BCI studies, a common approach is to collect data from mental states of interest during relatively long trials and divide these trials into shorter “epochs” to serve as individual samples in classification. While it is known that using k-fold cross-validation (CV) in this scenario can result in unreliable estimates of mental state separability (due to autocorrelation in the samples derived from the same trial), k-fold CV is still commonly used and reported in passive BCI studies. What is not known is the extent to which k-fold CV misrepresents true mental state separability. This makes it difficult to interpret the results of studies that use it. Furthermore, if the seriousness of the problem were clearly known, perhaps more researchers would be aware that they should avoid it. In this work, a novel experiment explored how the degree of correlation among samples within a class affects EEG-based mental state classification accuracy estimated by k-fold CV. Results were compared to a ground-truth (GT) accuracy and to “block-wise” CV, an alternative to k-fold which is purported to alleviate the autocorrelation issues. Factors such as the degree of true class separability and the feature set and classifier used were also explored. The results show that, under some conditions, k-fold CV inflated the GT classification accuracy by up to 25%, but block-wise CV underestimated the GT accuracy by as much as 11%. It is our recommendation that the number of samples derived from the same trial should be reduced whenever possible in single-subject analysis, and that both the k-fold and block-wise CV results are reported.

Background: In patients with Alzheimer's disease (AD) without clinical seizures, up to half have epileptiform discharges on long-term in-patient electroencephalography (EEG) recordings. Long-term in-patient monitoring is obtrusive, and expensive as compared to outpatient monitoring. No studies have so far investigated if long-term outpatient EEG monitoring is able to identify epileptiform discharges in AD. Our aim is to investigate if epileptiform discharges as measured with ear-EEG are more common in patients with AD compared to healthy elderly controls (HC). Methods: In this longitudinal observational study, 24 patients with mild to moderate AD and 15 age-matched HC were included in the analysis. Patients with AD underwent up to three ear-EEG recordings, each lasting up to two days, within 6 months. Results: The first recording was defined as the baseline recording. At baseline, epileptiform discharges were detected in 75.0% of patients with AD and in 46.7% of HC (p-value = 0.073). The spike frequency (spikes or sharp waves/24 h) was significantly higher in patients with AD as compared to HC with a risk ratio of 2.90 (CI: 1.77–5.01, p < 0.001). Most patients with AD (91.7%) showed epileptiform discharges when combining all ear-EEG recordings. Conclusions: Long-term ear-EEG monitoring detects epileptiform discharges in most patients with AD with a three-fold increased spike frequency compared to HC, which most likely originates from the temporal lobes. Since most patients showed epileptiform discharges with multiple recordings, elevated spike frequency should be considered a marker of hyperexcitability in AD.

Human linguistic units are hierarchical, and our brain responds differently when processing linguistic units during sentence comprehension, especially when the modality of the received signal is different (auditory, visual, or audio-visual). However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in audio and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory or visual or combined audio- visual modalities, while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e., characters/monosyllabic words) and higher-level linguistic structures (i.e., phrases and sentences) across the three modalities separately. We found that audio-visual integration occurs at all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation (cTBS) to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

Remembering unpleasant events can trigger negative feelings. Fortunately, research indicates that unwanted retrieval can be suppressed to prevent memories from intruding into awareness, improving our mental state. The current scientific understanding of retrieval suppression, however, is based mostly on simpler memories, such as associations between words or pictures, which may not reflect how people control unpleasant memory intrusions in everyday life. Here, we investigated the neural and behavioural dynamics of suppressing personal and emotional autobiographical memories using a modified version of the Think/No-Think task. We asked participants to suppress memories of their own past immoral actions, which were hypothesised to be both highly intrusive and motivating to suppress. We report novel evidence from behavioural, ERP, and EEG oscillation measures that autobiographical memory retrieval can be suppressed and suggest that autobiographical suppression recruits similar neurocognitive mechanisms as suppression of simple laboratory associations. Suppression did fail sometimes, and EEG oscillations indicated that such memory intrusions occurred from lapses in sustained control. Importantly, however, participants improved at limiting intrusions with repeated practice. Furthermore, both behavioural and EEG evidence indicated that intentional suppression may be more difficult for memories of our morally wrong actions than memories of our morally right actions. The findings elucidate the neurocognitive correlates of autobiographical retrieval suppression and have implications for theories of morally motivated memory control.

Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.

Publisher Copyright: © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. The primary aim of this study is to examine whether bursting interhemispheric synchrony (bIHS) in the first week of life of infants born extremely preterm, is associated with microstructural development of the corpus callosum (CC) on term equivalent age magnetic resonance imaging scans. The secondary aim is to address the effects of analgesics such as morphine, on bIHS in extremely preterm infants. A total of 25 extremely preterm infants (gestational age [GA] < 28 weeks) were monitored with the continuous two-channel EEG during the first 72 h and after 1 week from birth. bIHS was analyzed using the activation synchrony index (ASI) algorithm. Microstructural development of the CC was assessed at ~ 30 and ~ 40 weeks of postmenstrual age (PMA) using fractional anisotropy (FA) measurements. Multivariable regression analyses were used to assess the primary and secondary aim. Analyses were adjusted for important clinical confounders: morphine, birth weight z-score, and white matter injury score. Due to the reduced sample size, only the most relevant variables, according to literature, were included. ASI was not significantly associated with FA of the CC at 30 weeks PMA and at 40 weeks PMA (p >.5). ASI was positively associated with the administration of morphine (p

Recent evidence suggests that about 30%of patients with mild to moderate Alzheimer’s disease (AD) without a known diagnosis of epilepsy may display epileptiform spikes during electroencephalographic (EEG) recordings. These abnormal discharges occur predominantly during sleep and may be associated with accelerated disease progression. Subclinical spikes may represent a relevant target for clinical drug interventions, and there is a clear unmet need for preclinical testing of novel disease modifying agents in suitable animal models. Transgenic rodent models of AD pathology exhibit various forms of epileptiform EEG activity related to the abnormal levels of amyloid species in the brain. Among them, large-amplitude cortical and hippocampal EEG spikes in mouse and rat AD models may be reminiscent of the subclinical epileptiform EEG spikes recorded in some AD patients. This article reports the recommendations of a multidisciplinary panel of experts on optimal EEG markers and experimental designs to measure and report epileptiform activities and their response to symptomatic and disease-modifying drugs in transgenic AD model rodents. These recommendations may harmonize future preclinical EEG studies in the drug discovery research and may increase the comparability of experimental outcomes and their translational clinical value.

Excessive monitoring of one's performance is a characteristic of anxiety disorders that has been linked to alterations in implicit emotion regulation (ER), including elevations in neural measures of performance monitoring (i.e., error- and correct-related negativity; ERN and CRN). Elevations in ERN and CRN amplitudes have been reported consistently in anxiety disorders, suggesting that an overactive performance monitoring system is linked to ER difficulties in anxiety. Yet, the relevance of these lab-based neural measures for day-to-day emotional functioning remains poorly understood. This study examined the degree to which ERN and CRN amplitudes are associated with measures of daily ER difficulties in youth with anxiety disorders. Youth (N=100, Mage=11.14, SDage=1.46) completed a computerized flanker task assessing the ERN and CRN. They then completed a 5-day ecological momentary assessment (EMA) protocol assessing their daily ER (i.e., intensity of momentary and peak negative affect, intensity of worry, reliance on maladaptive ER strategies). Results showed that more negative mean CRN amplitudes were associated with higher levels of negative emotional reactivity and more intense worries. There were no significant associations between ERN amplitude and EMA measures. Furthermore, elevations in CRN were linked to more frequent use of maladaptive ER strategies (i.e., rumination, physiological reactivity, avoidance). Together, results indicate that among youth with anxiety, individual differences in CRN, but not ERN, amplitudes are related to daily ER difficulties. Findings highlight the clinical utility of a lab-based neural measure of ER, suggesting that the CRN, rather than the ERN, reflects individual ER differences in the context of daily life among youth with pediatric anxiety disorders. As such, the CRN might serve as an important dimensional index of a treatment target that can be tracked with a validated, multi-method measure.

Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constructed for the healthy group and the major depression group. We assume that the two groups share the same features for group similarity and retain individual characteristics for group differences. Considering that the constructed tensors are nonnegative and the components in spectral and adjacency modes are partially consistent among the two groups, we formulate a double-coupled nonnegative tensor decomposition model. To reduce computational complexity, we introduce the lowrank approximation. Then, the fast hierarchical alternative least squares algorithm is applied for model optimization. After clustering analysis, we summarize four oscillatory networks characterizing the healthy group and four oscillatory networks characterizing the major depression group, respectively. The proposed model may reveal novel mechanisms of pathoconnectomics in MDD during rest, and it can be easily extended to other psychiatric disorders. peerReviewed

Alzheimer's disease dementia (ADD) is the most diffuse neurodegenerative disorder belonging to mild cognitive impairment (MCI) and dementia in old persons. This disease is provoked by an abnormal accumulation of amyloid-beta and tauopathy proteins in the brain. Very recently, the first disease-modifying drug has been licensed with reserve (i.e., Aducanumab). Therefore, there is a need to identify and use biomarkers probing the neurophysiological underpinnings of human cognitive functions to test the clinical efficacy of that drug. In this regard, event-related electroencephalographic potentials (ERPs) and oscillations (EROs) are promising candidates. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association and Global Brain Consortium reviewed the field literature on the effects of the most used symptomatic drug against ADD (i.e., Acetylcholinesterase inhibitors) on ERPs and EROs in ADD patients with MCI and dementia at the group level. The most convincing results were found in ADD patients. In those patients, Acetylcholinesterase inhibitors partially normalized ERP P300 peak latency and amplitude in oddball paradigms using visual stimuli. In these same paradigms, those drugs partially normalize ERO phase-locking at the theta band (4–7 Hz) and spectral coherence between electrode pairs at the gamma (around 40 Hz) band. These results are of great interest and may motivate multicentric, double-blind, randomized, and placebo-controlled clinical trials in MCI and ADD patients for final cross-validation. United States Department of Health & Human Services National Institutes of Health (NIH) - USA

International audience; During speaking or listening, endogenous motor or exogenous visual processes have been shown to fine-tune the auditory neural processing of incoming acoustic speech signal. To compare the impact of these cross-modal effects on auditory evoked responses, two sets of speech production and perception tasks were contrasted using EEG. In a first set, participants produced vowels in a self-paced manner while listening to their auditory feedback. Following the production task, they passively listened to the entire recorded speech sequence. In a second set, the procedure was identical except that participants also watched online their own articulatory movements. While both endogenous motor and exogenous visual processes fine-tuned auditory neural processing, these cross-modal effects were found to act differentially on the amplitude and latency of auditory evoked responses. A reduced amplitude was observed on auditory evoked responses during speaking compared to listening, irrespective of the auditory or audiovisual feedback. Adding orofacial visual movements to the acoustic speech signal also speeded up the latency of auditory evoked responses, irrespective of the perception or production task. Taken together, these results suggest distinct motor and visual influences on auditory neural processing, possibly through different neural gating and predictive mechanisms.