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Although in-stream video advertising is common, its effects on advertisement (ad) information encoding remain unclear. We investigated the effects of in-stream video advertising by comparing two groups: those watching mid-roll (between the program) ads and those watching pre- and post-roll (before and after the program, respectively) ads. To elucidate how advertising content is encoded in the context of in-stream video advertising, we integrated two theoretical frameworks: the negative emotion–memory model (NEMM) and the limited capacity model of motivated–mediated message processing (LC4MP). We used electroencephalography (EEG) to assess negative emotions and bottom-up attention during advertisement viewing. The findings indicate that the first mid-roll ad induced negative emotions, but these feelings were attenuated during subsequent mid-rolls. In addition, negative emotions induced by mid-roll ads attenuated the role of bottom-up attention in the information encoding process. However, the pre- and post-roll ads were not accompanied by negative emotions; thus, bottom-up attention played a major role in the information encoding of these ads. The results also suggest that despite the negative emotions experienced during mid-rolls, such transient negative reactions did not affect purchase intention for the advertised products. [ABSTRACT FROM AUTHOR]

Contact with nature is restorative and leads to health benefits via nearby nature such as gardens, natural scenes, and interactions with companion animals. Although the recovery effect of natural environments is well known, few studies examine the restorative value of watching animals in urban park settings such as off-leash dog areas in urban parks. This study aims to investigate the restorative effect of observing dog play in urban dog parks by measuring brain activity, emotional state, and perceived restorativeness. Brain activity and psychological responses were measured by electroencephalography (EEG) and the perceived restorativeness scale (PRS)/profile of mood states, respectively. The EEG results demonstrated that the dog play video produced significantly lower alpha and higher beta power spectra, which suggested that the subjects were attentive and focused by accepting the scene as an interesting stimulus. Among the subscales of the PRS, fascination plays an important role in the restorative effects of watching dog play. The subjects felt more lively, cheerful, and interested than they did when watching the city and natural view videos. Thus, the perceived restorative values of watching dog play video in dog parks are comparable to those of natural park video environments. Moreover, dog parks exerted positive effects regardless of dog ownership status by improving mood and enhancing restorative experience. Opportunities for watching dog play in safe places around dog parks through considerable park planning would extend the health benefits of dog parks to non-dog owners and improve negative attitudes toward dog parks.

Research on action-based timing has shed light on the temporal dynamics of sensorimotor coordination. This study investigates the neural mechanisms underlying action-based timing, particularly during finger-tapping tasks involving synchronized and syncopated patterns. Twelve healthy participants completed a continuation task, alternating between tapping in time with an auditory metronome (pacing) and continuing without it (continuation). Electroencephalography data were collected to explore how neural activity changes across these coordination modes and phases. We applied deep learning methods to classify single-trial electroencephalography data and predict behavioral timing conditions. Results showed significant classification accuracy for distinguishing between pacing and continuation phases, particularly during the presence of auditory cues, emphasizing the role of auditory input in motor timing. However, when auditory components were removed from the electroencephalography data, the differentiation between phases became inconclusive. Mean accuracy asynchrony, a measure of timing error, emerged as a superior predictor of performance variability compared to inter-response interval. These findings highlight the importance of auditory cues in modulating motor timing behaviors and present the challenges of isolating motor activation in the absence of auditory stimuli. Our study offers new insights into the neural dynamics of motor timing and demonstrates the utility of deep learning in analyzing single-trial electroencephalography data.

Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.

Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data from n = 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulation directly impacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.

BACKGROUND: Fragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments. METHODS: Resting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5-20), 49 participants with ASD (aged between 6-17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations. RESULTS: Our results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated. CONCLUSIONS: These findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

Alterations in functional connectivity (FC) have been observed in individuals with Alzheimers disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

OBJECTIVE: ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD. METHOD: We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network. RESULTS: EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, seat (difficulty staying seated), and runs (running or climbing excessively). CONCLUSIONS: EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

The auditory steady state response (ASSR) arises when periodic sounds evoke stable responses in auditory networks that reflect the acoustic characteristics of the stimuli, such as the amplitude of the sound envelope. Larger for some stimulus rates than others, the ASSR in the human electroencephalogram (EEG) is notably maximal for sounds modulated in amplitude at 40 Hz. To investigate the local circuit underpinnings of the large ASSR to 40 Hz amplitude-modulated (AM) sounds, we acquired skull EEG and local field potential (LFP) recordings from primary auditory cortex (A1) in the rat during the presentation of 20, 30, 40, 50, and 80 Hz AM tones. 40 Hz AM tones elicited the largest ASSR from the EEG acquired above auditory cortex and the LFP acquired from each cortical layer in A1. The large ASSR in the EEG to 40 Hz AM tones was not due to larger instantaneous amplitude of the signals or to greater phase alignment of the LFP across the cortical layers. Instead, it resulted from decreased latency variability (or enhanced temporal consistency) of the 40 Hz response. Statistical models indicate the EEG signal was best predicted by LFPs in either the most superficial or deep cortical layers, suggesting deep layer coordinators of the ASSR. Overall, our results indicate that the recruitment of non-uniform but more temporally consistent responses across A1 layers underlie the larger ASSR to amplitude-modulated tones at 40 Hz.

BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies. METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (> 6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally. DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial. TRIAL REGISTRATION: The trial was registered as BCI-FES Therapy for Stroke Rehabilitation on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.

OBJECTIVE: Relapse of epileptic spasms after initial treatment of infantile epileptic spasms syndrome (IESS) is common. However, past studies of small cohorts have inconsistently linked relapse risk to etiology, treatment modality, and EEG features upon response. Using a large single-center IESS cohort, we set out to quantify the risk of epileptic spasms relapse and identify specific risk factors. METHODS: We identified all children with epileptic spasms at our center using a clinical EEG database. Using the electronic medical record, we confirmed IESS syndrome classification and ascertained treatment, response, time to relapse, etiology, EEG features, and other demographic factors. Relapse-free survival analysis was carried out using Cox proportional hazards regression. RESULTS: Among 599 children with IESS, 197 specifically responded to hormonal therapy and/or vigabatrin (as opposed to surgery or other second-line treatments). In this study, 41 (21%) subjects exhibited relapse of epileptic spasms within 12 months of response. Longer duration of IESS prior to response (>3 months) was strongly associated with shorter latency to relapse (hazard ratio = 3.11; 95% CI 1.59-6.10; p = 0.001). Relapse was not associated with etiology, developmental status, or any post-treatment EEG feature. SIGNIFICANCE: This study suggests that long duration of IESS before response is the single largest clinical predictor of relapse risk, and therefore underscores the importance of prompt and successful initial treatment. Further study is needed to evaluate candidate biomarkers of epileptic spasms relapse and identify treatments to mitigate this risk. PLAIN LANGUAGE SUMMARY: Relapse of infantile spasms is common after initially successful treatment. With study of a large group of children with infantile spasms, we determined that relapse is linked to long duration of infantile spasms. In contrast, relapse was not associated with the cause of infantile spasms, developmental measures, or EEG features at the time of initial response. Further study is needed to identify tools to predict impending relapse of infantile spasms.

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

Functional mapping during brain surgery is applied to define brain areas that control critical functions and cannot be removed. Currently, these procedures rely on verbal interactions between the neurosurgeon and electrophysiologist, which can be time-consuming. In addition, the electrode grids that are used to measure brain activity and to identify the boundaries of pathological versus functional brain regions have low resolution and limited conformity to the brain surface. Here, we present the development of an intracranial electroencephalogram (iEEG)-microdisplay that consists of freestanding arrays of 2048 GaN light-emitting diodes laminated on the back of micro-electrocorticography electrode grids. With a series of proof-of-concept experiments in rats and pigs, we demonstrate that these iEEG-microdisplays allowed us to perform real-time iEEG recordings and display cortical activities by spatially corresponding light patterns on the surface of the brain in the surgical field. Furthermore, iEEG-microdisplays allowed us to identify and display cortical landmarks and pathological activities from rat and pig models. Using a dual-color iEEG-microdisplay, we demonstrated coregistration of the functional cortical boundaries with one color and displayed the evolution of electrical potentials associated with epileptiform activity with another color. The iEEG-microdisplay holds promise to facilitate monitoring of pathological brain activity in clinical settings.

Spindle-shaped waves of oscillations emerge in EEG scalp recordings during human and rodent non-REM sleep. The association of these 10-16 Hz oscillations with events during prior wakefulness suggests a role in memory consolidation. Human and rodent depth electrodes in the brain record strong spindles throughout the cortex and hippocampus, with possible origins in the thalamus. However, the source and targets of the spindle oscillations from the hippocampus are unclear. Here, we employed an in vitro reconstruction of four subregions of the hippocampal formation with separate microfluidic tunnels for single axon communication between subregions assembled on top of a microelectrode array. We recorded spontaneous 400-1000 ms long spindle waves at 10-16 Hz in single axons passing between subregions as well as from individual neurons in those subregions. Spindles were nested within slow waves. The highest amplitudes and most frequent occurrence suggest origins in CA3 neurons that send feed-forward axons into CA1 and feedback axons into DG. Spindles had 50-70% slower conduction velocities than spikes and were not phase-locked to spikes suggesting that spindle mechanisms are independent of action potentials. Therefore, consolidation of declarative-cognitive memories in the hippocampus may be separate from the more easily accessible consolidation of memories related to thalamic motor function.

Purpose: The aim of this study was to examine whether cortical activity changes during exercise with increasing cognitive demands in preadolescent children. Method: Twenty healthy children (8.75 [0.91] y) performed one movement game, which was conducted with lower and higher cognitive demands. During a baseline measurement and both exercise conditions, cortical activity was recorded using a 64-channel electroencephalographic system, and heart rate was assessed. Ratings of perceived excertion and perceived cognitive engagement were examined after each condition. To analyze power spectral density in the theta, alpha-1, and alpha-2 frequency bands, an adaptive mixture independent component analysis was used to determine the spatiotemporal sources of cortical activity, and brain components were clustered to identify spatial clusters. Results: One-way repeated-measures analyses of variance revealed significant main effects for condition on theta in the prefrontal cluster, on alpha-1 in the prefrontal, central, bilateral motor, bilateral parieto-occipital, and occipital clusters, and on alpha-2 in the left motor, central, and left parieto-occipital clusters. Compared with the lower cognitive demand exercise, cortical activity was significantly higher in theta power in the prefrontal cluster and in alpha-1 power in the occipital cluster during the higher cognitive demand exercise. Conclusion: The present study shows that exercise complexity seems to influence cortical processing as it increased with increasing cognitive demands. [ABSTRACT FROM AUTHOR]

Purpose: To test the effects of mental fatigue (MF) on tackling technique on the dominant and nondominant shoulders in rugby union. Methods: Twenty male amateur rugby union players and a total of 953 tackles were analyzed. A randomized crossover counterbalanced design was used across a non-MF (control) and an MF condition. During each condition, each player performed 24 tackles, divided into 4 sets of 6 tackles (3 tackles on each shoulder). In the MF condition, players performed the Stroop Task between each set of tackles. A video recording of each tackle was used to evaluate each player's technical proficiency. A score of 1 point was awarded if a specific technique was performed correctly, and 0 point was given if not. The total score, measured in arbitrary units (AU) out of 11, represents the player's overall tackling proficiency. Results: Overall, players displayed a significantly lower technical proficiency score in the MF condition compared to control (set 2: control 7.30 [7.04–7.57] AU vs MF 6.91 [6.70–7.12] AU, P =.009, effect size (ES) = 0.30 small and set 3: control 7.34 [7.11–7.57] AU vs MF 6.88 [6.66–7.11] AU, P =.002, ES = 0.37 small). For the nondominant shoulder, players had a significantly lower technical proficiency score during the MF condition at set 2 (control 7.05 [6.68–7.41] AU vs MF 6.69 [6.42–6.96] AU, P =.047, ES = 0.29 small) and set 3 (control 7.14 [6.83–7.45] AU vs MF 6.61 [6.35–6.87] AU, P =.007, ES = 0.49 small). Conclusions: MF can diminish a player's overall tackling proficiency, especially when tackling on the nondominant shoulder. The physiological mechanism for this finding may be impaired executive function and suboptimal functioning of neural signals and pathways, which result in less skillful coordination of movement. To further understand and explain MF-induced physiological changes in tackling, the feasibility of monitoring brain activity (such as electroencephalogram) and neuromuscular function (such as electromyogram) needs to be investigated. The findings from this study may also contribute to the development of more effective tackle training programs for injury prevention and performance. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

OBJECTIVE: Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. METHODS: We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time. RESULTS: GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P  central > anterior, Kruskal-Wallis P

OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected nothing, suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.

We previously reported that pan-cortical effects occur when cognitive tasks end afterdischarges. For this report, we analyzed wavelet cross-coherence changes during cognitive tasks used to terminate afterdischarges studying multiple time segments and multiple groups of inter-electrode-con distances. We studied 12 patients with intractable epilepsy, with 970 implanted electrode contacts, and 39,871 electrode contact combinations. When cognitive tasks ended afterdischarges, coherence varied similarly across the cortex throughout the tasks, but there were gradations with time, distance, and frequency: (1) They tended to progressively decrease relative to baseline with time and then to increase toward baseline when afterdischarges ended. (2) During most time segments, decreases from baseline were largest for the closest inter-contact distances, moderate for intermediate inter-contact distances, and smallest for the greatest inter-contact distances. With respect to our patients intractable epilepsy, the changes found suggest that future therapies might treat regions beyond those closest to regions of seizure onset and treat later in a seizures evolution. Similar considerations might apply to other disorders. Our findings also suggest that cognitive tasks can result in pan-cortical coherence changes that participate in underlying attention, perhaps complementing the better-known regional mechanisms.

Elevated "neural noise" has been advanced as an explanation of autism and autistic sensory experiences. However, functional neuroimaging measures of neural noise may be vulnerable to contamination by recording noise. This study explored variability of electrophysiological responses to tones of different intensities in 127 autistic and 79 typically-developing children aged 2-5 years old. A rigorous data processing pipeline, including advanced visualizations of different signal sources that were maximally independent across different time lags, was used to identify and eliminate putative recording noise. Inter-trial variability was measured using median absolute deviations (MADs) of EEG amplitudes across trials and inter-trial phase coherence (ITPC). ITPC was elevated in autism in the 50 and 60 dB intensity conditions, suggesting diminished (rather than elevated) neural noise in autism, although reduced ITPC to soft 50 dB sounds was associated with increased loudness discomfort. Autistic and non-autistic participants did not differ in MADs, and indeed, the vast majority of the statistical tests examined in this study yielded no significant effects. These results appear inconsistent with the neural noise account.

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Consciousness is thought to be regulated by bidirectional information transfer between the cortex and thalamus, but the nature of this bidirectional communication - and its possible disruption in unconsciousness - remains poorly understood. Here, we present two main findings elucidating mechanisms of corticothalamic information transfer during conscious states. First, we identify a highly preserved spectral channel of cortical-thalamic communication that is present during conscious states, but which is diminished during the loss of consciousness and enhanced during psychedelic states. Specifically, we show that in humans, mice, and rats, information sent from either the cortex or thalamus via δ/θ/α waves (∼1-13 Hz) is consistently encoded by the other brain region by high γ waves (52-104 Hz); moreover, unconsciousness induced by propofol anesthesia or generalized spike-and-wave seizures diminishes this cross-frequency communication, whereas the psychedelic 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) enhances this low-to-high frequency interregional communication. Second, we leverage numerical simulations and neural electrophysiology recordings from the thalamus and cortex of human patients, rats, and mice to show that these changes in cross-frequency cortical-thalamic information transfer may be mediated by excursions of low-frequency thalamocortical electrodynamics toward/away from edge-of-chaos criticality, or the phase transition from stability to chaos. Overall, our findings link thalamic-cortical communication to consciousness, and further offer a novel, mathematically well-defined framework to explain the disruption to thalamic-cortical information transfer during unconscious states.

Disruption of SYNGAP1 directly causes a genetically identifiable neurodevelopmental disorder (NDD) called SYNGAP1-related intellectual disability (SRID). Without functional SynGAP1 protein, individuals are developmentally delayed and have prominent features of intellectual disability (ID), motor impairments, and epilepsy. Over the past two decades, there have been numerous discoveries indicating the critical role of Syngap1. Several rodent models with a loss of Syngap1 have been engineered, identifying precise roles in neuronal structure and function, as well as key biochemical pathways key for synapse integrity. Homozygous loss of SYNGAP1/Syngap1 is lethal. Heterozygous mutations of Syngap1 result in a broad range of behavioral phenotypes. Our in vivo functional data, using the original mouse model from the Huganir laboratory, corroborated behaviors including robust hyperactivity and deficits in learning and memory in young adults. Furthermore, we described impairments in the domain of sleep, characterized using neurophysiological data that was collected with wireless, telemetric electroencephalography (EEG). Syngap1+/- mice exhibited elevated spiking events and spike trains, in addition to elevated power, most notably in the delta power frequency. For the first time, we illustrated that primary neurons from Syngap1+/- mice displayed: 1) increased network firing activity, 2) greater bursts, 3) and shorter inter-burst intervals between peaks, by utilizing high density microelectrode arrays (HD-MEA). Our work bridges in vitro electrophysiological neuronal activity and function with in vivo neurophysiological brain activity and function. These data elucidate quantitative, translational biomarkers in vivo and in vitro that can be utilized for the development and efficacy assessment of targeted treatments for SRID.

Observers can discriminate between correct versus incorrect perceptual decisions with feelings of confidence. The centro-parietal positivity build-up rate (CPP slope) has been suggested as a likely neural signature of accumulated evidence, which may guide both perceptual performance and confidence. However, CPP slope also covaries with reaction time, which also covaries with confidence in previous studies, and performance and confidence typically covary; thus, CPP slope may index signatures of perceptual performance rather than confidence per se. Moreover, perceptual metacognition-including neural correlates-has largely been studied in vision, with few exceptions. Thus, we lack understanding of domain-general neural signatures of perceptual metacognition outside vision. Here we designed a novel auditory pitch identification task and collected behavior with simultaneous 32-channel EEG in healthy adults. Participants saw two tone labels which varied in tonal distance on each trial (e.g., C vs D, C vs F), then heard a single auditory tone; they identified which label was correct and rated confidence. We found that pitch identification confidence varied with tonal distance, but performance, metacognitive sensitivity (trial-by-trial covariation of confidence with accuracy), and reaction time did not. Interestingly, however, while CPP slope covaried with performance and reaction time, it did not significantly covary with confidence. We interpret these results to mean that CPP slope is likely a signature of first-order perceptual processing and not confidence-specific signals or computations in auditory tasks. Our novel pitch identification task offers a valuable method to examine the neural correlates of auditory and domain-general perceptual confidence.

A large body of research has linked childhood family socioeconomic status (SES) to neurodevelopment in childhood and adolescence. However, it remains unclear to what extent childhood family SES relates to brain functioning in adulthood. To address this gap, the present study investigated the associations between retrospective accounts of objective and subjective childhood family SES and two well-established electrophysiological indices of brain functioning in adulthood-the MMN and P3b event-related potentials (ERP) components, as neural correlates of automatic change detection and cognitive control respectively. Higher objective childhood family SES, as proxied by parent educational attainment in childhood, was associated with larger (more positive) P3b amplitudes in adulthood. In contrast, there was no association between childhood parent educational attainment and the magnitude of MMN. Adult reports of subjective family SES during childhood were not related to the magnitude of MMN or P3b. These findings suggest that the links between childhood parent educational attainment and brain functioning may extend into adulthood, especially for brain functions supporting cognitive control. These results also imply that, when using retrospective accounts of childhood family SES, objective and subjective reports likely proxy different childhood experiences that have distinct links with specific neurodevelopmental outcomes, and that some of these links may not persist into adulthood. Our findings lay the groundwork for future investigations on how and why childhood family SES relates to brain functioning in adulthood.

BackgroundSleep disturbances are a prevalent and complex comorbidity in neurodevelopmental disorders (NDDs). Dup15q syndrome (duplications of 15q11.2-13.1) is a genetic disorder highly penetrant for NDDs such as autism and intellectual disability and it is frequently accompanied by significant disruptions in sleep patterns. The 15q critical region harbors genes crucial for brain development, notably UBE3A and a cluster of gamma-aminobutyric acid type A receptor (GABAAR) genes. We previously described an electrophysiological biomarker of the syndrome, marked by heightened beta oscillations (12-30 Hz) in individuals with Dup15q syndrome, akin to electroencephalogram (EEG) alterations induced by allosteric modulation of GABAARs. Those with Dup15q syndrome exhibited increased beta oscillations during the awake resting state and during sleep, and they showed profoundly abnormal NREM sleep. This study aims to assess the translational validity of these EEG signatures and to delve into their neurobiological underpinnings by quantifying sleep physiology in chromosome-engineered mice with maternal (matDp/ + mice) or paternal (patDp/ + mice) inheritance of the full 15q11.2-13.1-equivalent duplication, and mice with duplication of just the UBE3A gene (Ube3a overexpression mice; Ube3a OE mice) and comparing the sleep metrics with their respective wildtype (WT) littermate controls.MethodsWe collected 48-h EEG/EMG recordings from 35 (23 male, 12 female) 12-24-week-old matDp/ + , patDp/ + , Ube3a OE mice, and their WT littermate controls. We quantified baseline sleep, sleep fragmentation, spectral power dynamics during sleep states, and recovery following sleep deprivation. Within each group, distinctions between Dup15q mutant mice and WT littermate controls were evaluated using analysis of variance (ANOVA) and student's t-test. The impact of genotype and time was discerned through repeated measures ANOVA, and significance was established at p

Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing.

BACKGROUND: Juvenile idiopathic epilepsy (JIE) is categorized as a generalized epilepsy. Epilepsy classification entails electrocortical characterization and localization of epileptic discharges (ED) using electroencephalography (EEG). HYPOTHESIS/OBJECTIVES: Characterize epilepsy in Egyptian Arabian foals with JIE using EEG. ANIMALS: Sixty-nine foals (JIE, 48; controls, 21). METHODS: Retrospective study. Inclusion criteria consisted of Egyptian Arabian foals: (1) JIE group diagnosed based on witnessed or recorded seizures, and neurological and EEG findings, and (2) control group of healthy nonepileptic age-matched foals. Clinical data were obtained in 48 foals. Electroencephalography with photic stimulation was performed under standing sedation in 37 JIE foals and 21 controls. RESULTS: Abnormalities on EEG were found in 95% of epileptic foals (35 of 37) and in 3 of 21 control asymptomatic foals with affected siblings. Focal ED were detected predominantly in the central vertex with diffusion into the centroparietal or frontocentral regions (n = 35). Generalization of ED occurred in 14 JIE foals. Epileptic discharges commonly were seen during wakefulness (n = 27/37 JIE foals) and sedated sleep (n = 35/37 JIE foals; 3/21 controls). Photic stimulation triggered focal central ED in 15 of 21 JIE foals. CONCLUSIONS AND CLINICAL IMPORTANCE: Juvenile idiopathic epilepsy has a focal onset of ED at the central vertex with spread resulting in clinical generalized tonic-clonic seizures with facial motor activity and loss of consciousness. Electroencephalography with photic stimulation contributes to accurate phenotyping of epilepsy. Foals with this benign self-limiting disorder might serve as a naturally occurring animal model for self-limited epilepsy in children.

Faces are among the most important visual stimuli that humans perceive in everyday life. While extensive literature has examined emotional processing and social evaluations of faces, most studies have examined either topic using unimodal approaches. In this review, we promote the use of multimodal cognitive neuroscience approaches to study these processes, using two lines of research as examples: ambiguity in facial expressions of emotion and social trait judgment of faces. In the first set of studies, we identified an event-related potential that signals emotion ambiguity using electroencephalography and we found convergent neural responses to emotion ambiguity using functional neuroimaging and single-neuron recordings. In the second set of studies, we discuss how different neuroimaging and personality-dimensional approaches together provide new insights into social trait judgments of faces. In both sets of studies, we provide an in-depth comparison between neurotypicals and people with autism spectrum disorder. We offer a computational account for the behavioral and neural markers of the different facial processing between the two groups. Finally, we suggest new practices for studying the emotional processing and social evaluations of faces. All data discussed in the case studies of this review are publicly available.

• We investigated if postural instability can induce motor resonance. • The unstable, compared to stable, display dampened left temporal theta activity. • The unstable display enhanced left temporal-prefrontal connectivity. • Biological and scramble stimuli differently modulated temporal cortex. • Observed postural (in)stability is encoded in cortical theta oscillations. This study investigates whether postural equilibration follows the same principles of motor resonance as goal-oriented actions, namely, whether an individual activates the same neuronal substrates when experiencing postural perturbation as when observing another individual in this condition. To address this question, we examined electroencephalographic dynamics while subjects observed point-light displays featuring an unstable human display, a stable human display, and their respective scrambled counterparts lacking shape information and biological motion. We focused on theta band (4–7 Hz), which is a fundamental frequency for modulating brain activity during challenging balance tasks and reflects postural stability monitoring. Rather than mirroring activity, our findings suggest an inhibitory response to postural instability. Theta event-related synchronization in the left temporal cortex was dampened for the unstable display as compared to its scramble counterpart and to the stable display. This low theta response coincided with an increase in left temporal-prefrontal connectivity, compatible with top-down inhibitory mechanisms. By contrast, the stronger theta response to the stable display as compared to the unstable one could be due to the difficulty of recognizing low-motion biological stimuli, or alternatively, to a facilitation of stimulus processing and strengthening of the mirroring response. The response facilitation for stable posture, coupled with a diminished response to the unstable display, could contribute to a broader mechanism mitigating postural threats and ensuring stable balance. Future investigations should leverage these findings to explore how posture-related responses correlate with perceptual and motor expertise, and to more clearly define these mechanisms during dynamic social interactions. [ABSTRACT FROM AUTHOR]

• Tapping while listening to speech decreases movement pace but not regularity and tends to lower speech comprehension. • Tapping while listening to speech leads to preserved cortical tracking of speech (CTS) and corticokinematic coupling (CKC). • CKC and CTS can be recorded simultaneously in clinical settings. Rationale: Cortical activity is coupled with streams of sensory stimulation. The coupling with the temporal envelope of heard speech is known as the cortical tracking of speech (CTS), and that with movement kinematics is known as the corticokinematic coupling (CKC). Simultaneous measurement of both couplings is desirable in clinical settings, but it is unknown whether the inherent dual-tasking condition has an impact on CTS or CKC. We aim to determine whether and how CTS and CKC levels are affected when recorded simultaneously. Twenty-three healthy young adults underwent 64-channel EEG recordings while listening to stories and while performing repetitive finger-tapping movements in 3 conditions: separately (audio- or tapping-only) or simultaneously (audio-tapping). CTS and CKC values were estimated using coherence analysis between each EEG signal and speech temporal envelope (CTS) or finger acceleration (CKC). CTS was also estimated as the reconstruction accuracy of a decoding model. Across recordings, CTS assessed with reconstruction accuracy was significant in 85 % of the subjects at phrasal frequency (0.5 Hz) and in 68 % at syllabic frequencies (4–8 Hz), and CKC was significant in over 85 % of the subjects at movement frequency and its first harmonic. Comparing CTS and CKC values evaluated in separate recordings to those in simultaneous recordings revealed no significant difference and moderate-to-high levels of correlation. Conclusion. Despite the subtle behavioral effects, CTS and CKC are not evidently altered by the dual-task setting inherent to recording them simultaneously and can be evaluated simultaneously using EEG in clinical settings. [ABSTRACT FROM AUTHOR]

• Study explored cognitive-motor interference as task difficulty increased in adults. • Cognitive errors rose with motor complexity during dual-task walking. • P3a amplitude increased, while P3b latency lengthened under task complexity. • P3 analysis revealed increased attentional demand with cognitive difficulty. • Results highlight need to balance attentional resources in cognitive-motor tasks. Dual-task paradigms, which involve performing cognitive and motor tasks simultaneously, are commonly used to study how attentional resources are allocated and managed under varying task demands. This study aimed to investigate cognitive-motor interferences (CMI) under different levels of cognitive and motor task difficulty without instruction on task prioritization. 17 healthy young adults performed an auditory oddball task with increasing cognitive and motor (walking vs. sitting) difficulty. Cognitive and motor performances, along with P3 (P3a and P3b) brainwave components, were analysed. Increasing cognitive difficulty resulted in more errors and increased P3a amplitude, reflecting enhanced attentional demand, while P3b remained unaffected. This suggests a threshold effect on attentional resources. Motor complexity lengthened P3a and P3b latencies without affecting amplitude, indicating delayed attentional resource recruitment. Additionally, walking with the most difficult cognitive task increased cognitive error, suggesting attentional resource limits. With increased motor and cognitive complexity, CMI emerged, leading to cognitive error increase and improved gait stability without amplitude changes in P3a and P3b. Two hypotheses were proposed: motor prioritization and motor facilitation. Our study suggests managing attentional resources to balance cognitive and motor tasks rather than linearly increasing task complexity. Viewing dual tasks as a new, integrated task is proposed, supported by previous neural network integration studies. Thus, understanding how the brain organizes tasks in response to constraints is crucial for comprehending complex task execution. [ABSTRACT FROM AUTHOR]

This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment. [ABSTRACT FROM AUTHOR]

Background: This study was aimed at analyzing cognitive function and quantitative electroencephalogram (qEEG) in patients with isolated REM sleep behavior disorder (iRBD) based on the presence of depression and at evaluating the impact of depression on phenoconversion to neurodegenerative diseases. Methods: Individuals diagnosed with iRBD via polysomnography were included. Based on the presence of depression, patients were categorized into two groups. Neuropsychological tests and qEEG were conducted following the diagnosis of iRBD, and outcomes were compared between the two groups. Patients were regularly followed to monitor their phenoconversion status. Cox regression analysis was performed to assess the hazard ratio associated with depression. Results: Ninety iRBD patients (70% males) were included, with a median age of 66.3 years. Depression was identified in 26 (28.9%) of these patients. The depressed group showed significantly poorer performance only in color reading subtest of Stroop (p = 0.029) compared to the nondepressed group, showing reduced processing speed. In qEEG, relative gamma power (p = 0.034) and high gamma power (p = 0.020) in the parietal region were significantly higher in the depressed group than in the nondepressed group. Depression was associated with a hazard ratio of 3.32 for the risk of phenoconversion to neurodegenerative diseases in iRBD patients (p = 0.011). Conclusion: Depressive symptoms in iRBD patients should be closely monitored as they could aggravate cognitive dysfunction and increase the risk of phenoconversion to neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Background: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. Methods: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. Results: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. Conclusions: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency. [ABSTRACT FROM AUTHOR]

Background: Patients with migraine often experience not only headache pain but also cognitive dysfunction, particularly in attention, which is frequently overlooked in both diagnosis and treatment. The influence of these attentional deficits on the pain-related clinical characteristics of migraine remains poorly understood, and clarifying this relationship could improve care strategies. Methods: This study included 52 patients with migraine and 34 healthy controls. We employed the Attentional Network Test for Interactions and Vigilance–Executive and Arousal Components paradigm, combined with electroencephalography, to assess attentional deficits in patients with migraine, with an emphasis on phasic alerting, orienting, executive control, executive vigilance, and arousal vigilance. An extreme gradient boosting binary classifier was trained on features showing group differences to distinguish patients with migraine from healthy controls. Moreover, an extreme gradient boosting regression model was developed to predict clinical characteristics of patients with migraine using their attentional deficit features. Results: For general performance, patients with migraine presented a larger inverse efficiency score, a higher prestimulus beta-band power spectral density and a lower gamma-band event-related synchronization at Cz electrode, and stronger high alpha-band activity at the primary visual cortex, compared to healthy controls. Although no behavior differences in three basic attentional networks were found, patients showed magnified N1 amplitude and prolonged latency of P2 for phasic alerting-trials as well as an increased orienting evoked-P1 amplitude. For vigilance function, improvements in the hit rate of executive vigilance-trials were exhibited in controls but not in patients. Besides, patients with migraine exhibited longer reaction time as well as larger variability in arousal vigilance-trials than controls. The binary classifier developed by such attentional deficit features achieved an F1 score of 0.762 and an accuracy of 0.779 in distinguishing patients with migraine from healthy controls. Crucially, the predicted value available from the regression model involving attentional deficit features significantly correlated with the real value for the frequency of headache. Conclusions: Patients with migraine demonstrated significant attentional deficits, which can be used to differentiate migraine patients from healthy populations and to predict clinical characteristics. These findings highlight the need to address cognitive dysfunction, particularly attentional deficits, in the clinical management of migraine. [ABSTRACT FROM AUTHOR]

Stimuli encountered in the environment are continuously evaluated according to their affective stimulus significance. Numerous event‐related potential studies have shown that the early posterior negativity (EPN) and the late positive potential (LPP) are larger for high than low arousing emotional pictures. The group approach has been recently extended to the study of the individual case. Usually, many exemplars are used to represent an emotion category. Determining how many pictures are needed to reliably assess affective stimulus evaluation processes at the individual level is crucial when moving toward the goal of exploring idiosyncratic emotional stimuli. Accordingly, in the present study (N = 16), singular images displaying erotic, neutral, and mutilation content were shown 800 times while dense sensor EEG was recorded. At the group level, enhanced EPN and LPP amplitudes for high compared to low arousing stimuli emerged. At the single subject level, significantly larger amplitudes to the erotic than neutral image were observed in 15 out of 16 tests for the EPN and LPP components. Regarding the mutilation image, 15 participants showed a significant EPN effect, while the LPP effect was only found in 10 cases. Notably, emotional modulation of the EPN and LPP was stable over time. The present study contributes to the development of experimental designs tailored to the needs of the case‐by‐case approach. Since the process of affective stimulus evaluation is considered as a process common‐to‐all, the use of a singular stimulus exemplar may prove useful to investigate the idiosyncratic nature of emotion. [ABSTRACT FROM AUTHOR]

Background Case Presentation Conclusions Cefepime, a fourth‐generation cephalosporin, has neurotoxic side effects such as encephalopathy. Baseline conditions, including blood–brain barrier (BBB) impairment and renal dysfunction, are known to associate with elevated central nervous concentration of cefepime. Although BBB dysfunction occurs with depression or cancer, currently, neither is regarded as a risk factor for cefepime‐induced encephalopathy.A 79‐year‐old woman with a history of depression and rectal cancer was hospitalized for a bacterial liver abscess. Brain metastasis and other causes for delirium were excluded, and no renal dysfunction was observed. However, 11 days after cefepime and metronidazole administration, the patient suddenly developed confusion, disorientation, and myoclonus, with no apparent changes on brain magnetic resonance imaging. Electroencephalography revealed a consistent tri‐phasic wave pattern. Clinical symptoms were well consistent with cefepime‐induced encephalopathy; hence, cefepime and metronidazole were discontinued, followed by rapid physical and mental recovery, with no aftereffects.In terms of BBB dysfunction, depression and cancer might be possible occult risk factors for cefepime‐induced encephalopathy. Doctors need to pay attention to encephalopathy risk when administering cefepime in patients with depression or cancer because the psychiatric symptoms of encephalopathy, depression, and delirium from other causes are often confusing, leading to misdiagnosis and a poor prognosis. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of experts to assess its potential applicability.First, we performed clinical validation on an internal data set. Seven experts reviewed all EEG studies. Performance agreement between experts and the network was compared at both the EEG and IED levels. All EEG recordings were also processed with Persyst. Subsequently, we performed external validation, with data from four centers, using a hybrid approach, where detections by the deep neural network were reviewed by an expert. In case of disagreement with the original report, the EEG recording was annotated independently by five experts.For internal validation we included 22 EEG studies with IEDs and 28 EEG studies from controls. At the EEG level, our network showed performance similar to that of the experts. For individual IED detection, the sensitivities between experts ranged from 20.7%–86.4%, whereas the sensitivity of our network was 82.5% (confidence interval [CI]: 77.7%–87.4%) at 99% specificity and a false detection rate (FDR) of <.2/min, outperforming Persyst, with 64.6% sensitivity (CI: 61.4%–67.9%) at 98% specificity. External validation in 174 EEG studies demonstrated that all 85 EEG recordings classified as normal in the original report were classified correctly, with an FDR of .10/min. Of the 89 EEG studies with IEDs according to the report, 56 were correctly classified (Cohen's κ = .62). Visual analysis of the remaining 33 EEG recordings showed high interobserver variability among the five experts (Fleiss’ κ = .13).Our deep neural network detects IEDs on par with clinical experts. The external validation in a hybrid approach showed substantial agreement with the original report. Disagreement was due mainly to high interobserver variability. Our deep neural network may support visual EEG analysis and assist in diagnostics, particularly when human resources are limited. [ABSTRACT FROM AUTHOR]

[Display omitted] • N400 and P600 ERPs evoked in adolescents ignoring sentences with semantic errors. • Semantic processing is fundamentally different in passive versus active contexts. • We provide 22 control adolescent high-density electrophysiological BIDs datasets. Language comprehension requires semantic processing of individual words and their context within a sentence. Well-characterized event-related potential (ERP) components (the N400 and late positivity component (LPC/P600)) provide neuromarkers of semantic processing, and are robustly evoked when semantic errors are introduced into sentences. These measures are useful for evaluating semantic processing in clinical populations, but it is not known whether they can be evoked in more severe neurodevelopmental disorders where explicit attention to the sentence inputs cannot be objectively assessed (i.e., when sentences are passively listened to). We evaluated whether N400 and LPC/P600 could be detected in adolescents who were explicitly ignoring sentence inputs. Specifically, it was asked whether explicit attention to spoken inputs was required for semantic processing, or if a degree of automatic processing occurs when the focus of attention is directed elsewhere? High-density ERPs were acquired from twenty-two adolescents (12–17 years), under two experimental conditions: 1. individuals actively determined whether the final word in a sentence was congruent or incongruent with sentence context, or 2. passively listened to background sentences while watching a video. When sentences were ignored, N400 and LPC/P600 were robustly evoked to semantic errors, albeit with reduced amplitudes and protracted/delayed latencies. Statistically distinct topographic distributions during passive versus active paradigms pointed to distinct generator configurations for semantic processing as a function of attention. Covert semantic processing continues in neurotypical adolescents when explicit attention is withdrawn from sentence inputs. As such, this approach could be used to objectively investigate semantic processing in populations with communication deficits. [ABSTRACT FROM AUTHOR]

[Display omitted] • EEG can track modulation of cortical proprioceptive processing to joint rotations. • Volitional muscle activation intensifies the cortical proprioceptive processing. • Cortical inhibition is enhanced during passive joint-rotation stimulation. • Mechanisms likely at muscle, spinal, sub-cortical and cortical levels. Cortical proprioceptive processing of intermittent, passive movements can be assessed by extracting evoked and induced electroencephalographic (EEG) responses to somatosensory stimuli. Although the existent prior research on somatosensory stimulations, it remains unknown to what extent ongoing volitional muscle activation modulates the proprioceptive cortical processing of passive ankle-joint rotations. Twenty-five healthy volunteers (28.8 ± 7 yr, 14 males) underwent a total of 100 right ankle-joint passive rotations (4° dorsiflexions, 4 ± 0.25 s inter-stimulus interval, 30°/s peak angular velocity) evoked by a movement actuator during passive condition with relaxed ankle and active condition with a constant plantarflexion torque of 5 ± 2.5 Nm. Simultaneously, EEG, electromyographic (EMG) and kinematic signals were collected. Spatiotemporal features of evoked and induced EEG responses to the stimuli were extracted to estimate the modulation of the cortical proprioceptive processing between the active and passive conditions. Proprioceptive stimuli during the active condition elicited robustly ∼26 % larger evoked response and ∼38 % larger beta suppression amplitudes, but ∼42 % weaker beta rebound amplitude over the primary sensorimotor cortex than the passive condition, with no differences in terms of response latencies. These findings indicate that the active volitional motor task during naturalistic proprioceptive stimulation of the ankle joint enhances related cortical activation and reduces related cortical inhibition with respect to the passive condition. Possible factors explaining these results include mechanisms occurring at several levels of the proprioceptive processing from the peripheral muscle (i.e. mechanical, muscle spindle status, etc.) to the different central (i.e. spinal, sub-cortical and cortical) levels. [ABSTRACT FROM AUTHOR]