Search

Background: The complicated mental illness known as borderline personality disorder (BPD) is characterized by impulsivity, emotional instability, and interpersonal challenges. Measurement of brain electrical activity using electroencephalography (EEG) can yield helpful biomarkers for a range of psychiatric conditions. Objectives: The present study utilizes comprehensive EEG analysis to identify distinct neural patterns in BPD, providing insights and potential biomarkers for diagnosing and understanding BPD. Materials & Methods: In a cross-sectional study conducted on students of Guilan University (Iran), 534 students were evaluated through the Millon clinical multiaxial inventory from September 2018 to September 2020. Fourteen students were recruited into this case-control study: Seven students with BPD and seven healthy controls. EEG recording was performed by a standard 21-channel device. The EEG signals were processed with the EEGLAB toolbox in the MATLAB environment. The absolute and relative powers were calculated using the fast Fourier transform function. The power spectral density (PSD) and the coherence between different channels were evaluated, too. The statistical analysis was performed using GraphPad Prism software. Results: The results showed a significant increase in the delta band range, mainly in the frontotemporal region, and a reduction in the power of the alpha band in students with BPD. The frontal brain region of BPD patients also showed reduced alpha band coherence. Conclusion: Students with BPD exhibit distinct EEG patterns, including increased delta band power and reduced alpha band power and coherence. These findings suggest potential EEG biomarkers for BPD, highlighting the utility of EEG in understanding this complex disorder.

Background: The complicated mental illness known as borderline personality disorder (BPD) is characterized by impulsivity, emotional instability, and interpersonal challenges. Measurement of brain electrical activity using electroencephalography (EEG) can yield helpful biomarkers for a range of psychiatric conditions. Objectives: The present study utilizes comprehensive EEG analysis to identify distinct neural patterns in BPD, providing insights and potential biomarkers for diagnosing and understanding BPD. Materials & Methods: In a cross-sectional study conducted on students of Guilan University (Iran), 534 students were evaluated through the Millon clinical multiaxial inventory from September 2018 to September 2020. Fourteen students were recruited into this case-control study: Seven students with BPD and seven healthy controls. EEG recording was performed by a standard 21-channel device. The EEG signals were processed with the EEGLAB toolbox in the MATLAB environment. The absolute and relative powers were calculated using the fast Fourier transform function. The power spectral density (PSD) and the coherence between different channels were evaluated, too. The statistical analysis was performed using GraphPad Prism software. Results: The results showed a significant increase in the delta band range, mainly in the frontotemporal region, and a reduction in the power of the alpha band in students with BPD. The frontal brain region of BPD patients also showed reduced alpha band coherence. Conclusion: Students with BPD exhibit distinct EEG patterns, including increased delta band power and reduced alpha band power and coherence. These findings suggest potential EEG biomarkers for BPD, highlighting the utility of EEG in understanding this complex disorder. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.

Network oscillations are essential for all cognitive functions. Oscillatory deficits are well established in psychiatric diseases and are recapitulated in animal models. They are significantly and specifically affected by pharmacological interventions using psychoactive compounds. Dopamine D4 receptor (D4R) activation was shown to enhance gamma rhythm in freely moving rats and to specifically affect slow delta and theta oscillations in the urethane-anesthetized rat model. The goal of this study was to test the effect of D4R activation on slow network oscillations at delta and theta frequencies during wake states, potentially supporting enhanced functional connectivity during dopamine-induced attention and cognitive processing. Network activity was recorded in the prefrontal cortex (PFC), hippocampus (HC) and nucleus reuniens (RE) in control conditions and after injecting the D4R agonist A-412997 (3 and 5 mg/kg; systemic administration). We found that A-412997 elicited a lasting (~40 min) wake state and drastically enhanced narrow-band delta oscillations in the PFC and RE in a dose-dependent manner. It also preferentially enhanced delta synchrony over theta coupling within the PFC-RE-HC circuit, strongly strengthening PFC-RE coupling. Thus, our findings indicate that the D4R may contribute to cognitive processes, at least in part, through acting on wake delta oscillations and that the RE, providing an essential link between the PFC and HC, plays a prominent role in this mechanism. [ABSTRACT FROM AUTHOR]

The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p < 0.0001) for the combined delta-gamma frequencies compared with the delta-only and gamma-only frequencies. There were also spatial differences in rs-MEG activity between mTBI and OI groups in both delta and gamma bands in frontal and temporal lobe, as well as more widespread differences in the brain. The ML algorithm accounted for 84.5% of the variance in predicting recovery measured by PCS changes between 3 weeks and 3 months post-injury in the mTBI group, and this was significantly lower (p < 10−4) in the OI group (65.6%). Frontal lobe pole (higher) gamma activity was significantly (p < 0.001) associated with (worse) PCS recovery exclusively in the mTBI group. These findings demonstrate a neural injury signature of pediatric mTBI and patterns of mTBI-induced neural injury related to behavioral recovery. [ABSTRACT FROM AUTHOR]

The vast majority of studies on hippocampal rhythms have been conducted on animals or humans in situations where their attention was focused on external stimuli or solving cognitive tasks. These studies formed the basis for the idea that rhythmical activity coordinates the work of neurons during information processing. However, at rest, when attention is not directed to external stimuli, brain rhythms do not disappear, although the parameters of oscillatory activity change. What is the functional load of rhythmical activity at rest? Hippocampal oscillatory activity during rest is called the non-theta state, as opposed to the theta state, a characteristic activity during active behavior.We dedicate our review to discussing the present state of the art in the research of the non-theta state. The key provisions of the review are as follows: (1) the non-theta state has its own characteristics of oscillatory and neuronal activity; (2) hippocampal non-theta state is possibly caused and maintained by change of rhythmicity of medial septal input under the influence of raphe nuclei; (3) there is no consensus in the literature about cognitive functions of the non-theta-non-ripple state; and (4) the antagonistic relationship between theta and delta rhythms observed in rodents is not always observed in humans. Most attention is paid to the non-theta-nonripple state, since this aspect of hippocampal activity has not been investigated properly and discussed in reviews. [ABSTRACT FROM AUTHOR]

The vast majority of studies on hippocampal rhythms have been conducted on animals or humans in situations where their attention was focused on external stimuli or solving cognitive tasks. These studies formed the basis for the idea that rhythmical activity coordinates the work of neurons during information processing. However, at rest, when attention is not directed to external stimuli, brain rhythms do not disappear, although the parameters of oscillatory activity change. What is the functional load of rhythmical activity at rest? Hippocampal oscillatory activity during rest is called the non-theta state, as opposed to the theta state, a characteristic activity during active behavior. We dedicate our review to discussing the present state of the art in the research of the non-theta state. The key provisions of the review are as follows: (1) the non-theta state has its own characteristics of oscillatory and neuronal activity; (2) hippocampal non-theta state is possibly caused and maintained by change of rhythmicity of medial septal input under the influence of raphe nuclei; (3) there is no consensus in the literature about cognitive functions of the non-theta-non-ripple state; and (4) the antagonistic relationship between theta and delta rhythms observed in rodents is not always observed in humans. Most attention is paid to the non-theta-non-ripple state, since this aspect of hippocampal activity has not been investigated properly and discussed in reviews.

Objective: The objective of this study was to investigate the impact of smoking during pregnancy on the development of the child. While previous research has established its detrimental effects during early childhood, understanding potential long-term consequences into adulthood remains limited. This study specifically aimed to explore the influence of prenatal smoking exposure on brain activity and whether internalizing and externalizing symptoms are influenced by prenatal smoking exposure in a cohort of young adults., Methods: Utilizing data from 176 participants (mean age M = 24.68, SD = 0.49) and their mothers enrolled in a longitudinal risk study (MARS), we employed Generalized Additive Mixed Models (GAMMs) to analyze electroencephalography (EEG) power at rest and behavioral outcomes derived from the Young Adult-Self-Report (YASR) scales. Both covariate-unadjusted and -adjusted models were used, taking into account participant variables such as sex and age, as well as maternal factors like psychopathology and alcohol consumption, in addition to smoking and alcohol intake by the participants themselves., Results: The study revealed a significant impact of prenatal smoking on delta and theta band power, indicating decreased slower brain activity in prenatally exposed individuals compared to unexposed counterparts. Additionally, individuals exposed to prenatal smoking exhibited significantly higher levels of externalizing behavior. While this association was strongly influenced by maternal psychopathology, the child's gender, and the child's own substance use, the effect on delta power band remained after adjusting for covariates., Conclusion: The findings suggest that prenatal smoking exposure may have enduring effects on brain activity patterns in young adulthood. Conversely, the influence on externalizing behaviors depended on familial factors (maternal psychopathology) and the lifestyle of the individual (substance use)., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tobias Banaschewski served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire. He received conference support or speaker’s fee by Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire and Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. Daniel Brandeis serves as an unpaid scientific consultant for an EU-funded neurofeedback trial. The present work is unrelated to the above grants and relationships. The other authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Sleep is regulated by homeostatic processes, yet the biological basis of sleep pressure that accumulates during wakefulness, triggers sleep, and dissipates during sleep remains elusive. We explored a causal relationship between cellular synaptic strength and electroencephalography delta power indicating macro-level sleep pressure by developing a theoretical framework and a molecular tool to manipulate synaptic strength. The mathematical model predicted that increased synaptic strength promotes the neuronal "down state" and raises the delta power. Our molecular tool (synapse-targeted chemically induced translocation of Kalirin-7, SYNCit-K), which induces dendritic spine enlargement and synaptic potentiation through chemically induced translocation of protein Kalirin-7, demonstrated that synaptic potentiation of excitatory neurons in the prefrontal cortex (PFC) increases nonrapid eye movement sleep amounts and delta power. Thus, synaptic strength of PFC excitatory neurons dictates sleep pressure in mammals.

One of the major breakthroughs of neurobiology was the identification of distinct ranges of oscillatory activity in the neuronal network that were found to be responsible for specific biological functions, both physiological and pathological in nature. Astrocytes, physically coupled by gap junctions and possessing the ability to simultaneously modulate the functions of a large number of surrounding synapses, are perfectly positioned to introduce synchronised oscillatory activity into the neural network. However, astrocytic somatic calcium signalling has not been investigated to date in the frequency ranges of common neuronal oscillations, since astrocytes are generally considered to be slow responders in terms of Ca 2+ signalling. Using high-frequency two-photon imaging, we reveal fast Ca 2+ oscillations in the soma of astrocytes in the delta (0.5-4 Hz) and theta (4-8 Hz) frequency bands in vivo in the rat cortex under ketamine-xylazine anaesthesia, which is known to induce permanent slow-wave sleep. The high-frequency astrocytic Ca 2+ signals were not observed under fentanyl anaesthesia, excluding the possibility that the signals were introduced by motion artefacts. We also demonstrate that these fast astrocytic Ca 2+ signals, previously considered to be exclusive to neurons, are present in a large number of astrocytes and are phase synchronised at the astrocytic network level. We foresee that the disclosure of these high-frequency astrocytic signals may help with understanding the appearance of synchronised oscillatory signals and may open up new avenues of treatment for neurological conditions characterised by altered neuronal oscillations.

Background: Following the standardized nomenclature proposed by the American Clinical Neurophysiology Society (ACNS), rhythmic high-amplitude delta activity with superimposed spikes (RHADS) can be reported as an extreme delta brush (EDB). The clinical implications of similar electrographic patterns being reported as RHADS versus EDB are important to highlight. We aim to review the electrographic characteristics of RHADS, evaluate whether RHADS is seen in other neurological disorders, and identify the similar and unique characteristics between RHADS and EDB to ultimately determine the most accurate way to differentiate and report these patterns. We believe that the differentiation of RHADS and EDB is important as there is a vast difference in the diagnostic approach and the medical management of associated underlying etiologies., Data Source: We conducted an extensive search on MEDLINE and Pubmed utilizing various combinations of keywords. Searching for "gamma polymerase and EEG", or "RHADS" or "Alpers syndrome and EEG" or "EEG" AND "Alpers-Huttenlocher syndrome"., Results: Three articles were found to be focused on the description of "RHADS" pattern in Alpers Syndrome. No publication to date were found when searching for the terms "EDB" AND "children", AND "infant" AND "adolescent" excluding "encephalitis" and "neonate". Although RHADS and EDB appear as similar EEG patterns, meticulous analysis can differentiate them. RHADS is not exclusive to patients with Alpers-Huttenlocher syndrome and may manifest in regions beyond the posterior head region. Reactivity to eye-opening and response to anesthesia can be two other elements that help in the differentiation of these patterns., Conclusion: RHADS is not exclusive to patients with AHS and may manifest in regions beyond the posterior head region. Reactivity to eye-opening and response to anesthesia are features that help in the differentiation of these patterns., (© 2024. Children's Hospital, Zhejiang University School of Medicine.)

This study was aimed at comparing the delta rhythm frequencies of electroencephalographic waves after inducing either with thiopentone or propofol intravenously in basset hound dogs. Frontopolar montage was used to obtain the electroencephalographic waves. The depth of anaesthesia was also assessed clinically by recording the clinical parameters viz., heart rate, oxygen saturation, endtidal carbon dioxide and blood pressure as well as by checking the reflexes. The animals induced with thiopentone had a lower frequency compared to the animals induced with propofol at all time periods. The frequency obtained immediately after inducing with thiopentone was 3.50 ± 2.40 Hz whereas the frequency obtained immediately after inducing with propofol was 3.94 ± 2.25 Hz. Whereas the frequency obtained at the 60th minute time period after thiopentone induction was 2.13 ± 0.49 Hz and after propofol induction was 2.72 ± 1.56 Hz. The reflexes were absent during the observation periods. [ABSTRACT FROM AUTHOR]

Understanding human sleep requires appropriate animal models. Sleep has been extensively studied in rodents, although rodent sleep differs substantially from human sleep. Here we investigate sleep in tree shrews, small diurnal mammals phylogenetically close to primates, and compare it to sleep in rats and humans using electrophysiological recordings from frontal cortex of each species. Tree shrews exhibited consolidated sleep, with a sleep bout duration parameter, τ, uncharacteristically high for a small mammal, and differing substantially from the sleep of rodents that is often punctuated by wakefulness. Two NREM sleep stages were observed in tree shrews: NREM, characterized by high delta waves and spindles, and an intermediate stage (IS-NREM) occurring on NREM to REM transitions and consisting of intermediate delta waves with concomitant theta-alpha activity. While IS-NREM activity was reliable in tree shrews, we could also detect it in human EEG data, on a subset of transitions. Finally, coupling events between sleep spindles and slow waves clustered near the beginning of the sleep period in tree shrews, paralleling humans, whereas they were more evenly distributed in rats. Our results suggest considerable homology of sleep structure between humans and tree shrews despite the large difference in body mass between these species. Dimanico et al investigated sleep in tree shrews using electrophysiological recordings and compared it to equivalent read-outs in rats and humans. They reported that there was considerable homology of sleep structure between humans and tree shrews despite the difference in body mass between these species. [ABSTRACT FROM AUTHOR]

Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]

This study focused on detecting the reflections of healing and change in cortex activation in full-face transplantation and lesions patients on EEG activity. Face transplant patients have facial lesions before transplantation and, to identify pre-face transplant patients' brain activity in the absence of pre-transplant recordings, we used data obtained from pre-transplant facial lesion patients. Ten healthy, four facial lesion and three full-face transplant patients participated in this study. EEG data recorded for four different sensory stimuli (brush from the right face, right hand, left face, and left-hand regions) were analyzed using wavelet packet transform method. EEG waves were analyzed for standard bands. Our findings indicate significant change in the 2-4 Hz frequency range which may be a result of ongoing or previous cortical reorganization for face lesion and transplant patients. Alterations of the delta wave seen in patients with facial lesion and face transplant can also be explained by the intense central plasticity. Our findings show that the delta band differences might be used as a marker in the evaluation of post-transplant cortical plasticity in the future., (© 2024. The Author(s).)

ABSTRACT Objective: To verify if, by three distinct quantifiers, the measured electroencephalographic signal at rest is different from the signal measured during a word reading situation, especially considering the faster rhythms, gamma and high-gamma, as it occurs in clinical rhythms (delta to beta). Methods: A total of 96 electroencephalographic signals measured from neurologically healthy volunteers were evaluated at two moments: resting and word reading. Each signal segment was measured by three quantifiers that separately assess normalized power, percent power, and right and left hemisphere coherence. The Mann-Whitney test was used to compare the results of the quantifiers in each brain range. Results: The gamma and high-gamma rhythms presented a more distinct behavior when comparing the analyzed moments (resting and reading) than the clinical rhythms. Conclusion: This finding contributes to the scarce literature on faster rhythms, which can contain information that is normally disregarded in neurological clinical practice.

• The FFA plays a preponderant role in discriminating confusable facial expressions. • Images of low confusion induce larger LPP amplitude than those of high confusion. • Images of low confusion induce stronger delta rhythm than those of high confusion. Although the recognition of facial expressions seems automatic and effortless, discrimination of expressions can still be error prone. Common errors are often due to visual similarities between some expressions (e.g., fear and surprise). However, little is known about the neural mechanisms underlying such a confusion effect. To address this question, we recorded the magnetoencephalography (MEG) while participants judged facial expressions that were either easily confused with or easily distinguished from other expressions. The results showed that the fusiform face area (FFA), rather than the posterior superior temporal sulcus (pSTS), played a preponderant role in discriminating confusable facial expressions. No difference between high confusion and low confusion conditions was observed on the M170 component in either the FFA or the pSTS, whilst a difference between two conditions started to emerge in the late positive potential (LPP), with the low confusion condition eliciting a larger LPP amplitude in the FFA. In addition, the power of delta was stronger in the time window of LPP component. This confusion effect was reflected in the FFA, which might be associated with the perceptual-to-conceptual shift. [ABSTRACT FROM AUTHOR]

Cross-modal perception allows olfactory information to integrate with other sensory modalities. Olfactory representations are processed by multisensory cortical pathways, where the aspects related to the haptic sensations are integrated. This complex reality allows the development of an integrated perception, where olfactory aspects compete with haptic and/or trigeminal activations. It is assumed that this integration involves both perceptive electrophysiological and metabolic/hemodynamic aspects, but there are no studies evaluating these activations in parallel. The aim of this study was to investigate brain dynamics during a cross-modal olfactory and haptic attention task, preceded by an exploratory session. The assessment of cross-modal dynamics was conducted through simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording, evaluating both electrophysiological and hemodynamic activities. The study consisted of two experimental sessions and was conducted with a sample of ten healthy subjects (mean age 25 ± 5.2 years). In Session 1, the subjects were trained to manipulate 3D haptic models (HC) and to smell different scents (SC). In Session 2, the subjects were tested during an attentive olfactory task, in order to investigate the olfactory event-related potentials (OERP) N1 and late positive component (LPC), and EEG rhythms associated with fNIRS components (oxy-Hb and deoxy-Hb). The main results of this study highlighted, in Task 1, a higher fNIRS oxy-Hb response during SC and a positive correlation with the delta rhythm in the central and parietal EEG region of interest. In Session 2, the N1 OERP highlighted a greater amplitude in SC. A negative correlation was found in HC for the deoxy-Hb parietal with frontal and central N1, and for the oxy-Hb frontal with N1 in the frontal, central and parietal regions of interests (ROIs). A negative correlation was found in parietal LPC amplitude with central deoxy-Hb. The data suggest that cross-modal valence modifies the attentional olfactory response and that the dorsal cortical/metabolic pathways are involved in these responses. This can be considered as an important starting point for understanding integrated cognition, as the subject could perceive in an ecological context.

Although visual input arrives continuously, sensory information is segmented into (quasi-)discrete events. Here, we investigated the neural correlates of spatiotemporal binding in humans with magnetoencephalography using two tasks where separate flashes were presented on each trial but were perceived, in a bistable way, as either a single or two separate events. The first task (two-flash fusion) involved judging one versus two flashes, whereas the second task (apparent motion: AM) involved judging coherent motion versus two stationary flashes. Results indicate two different functional networks underlying two unique aspects of temporal binding. In two-flash fusion trials, involving an integration window of ∼50 msec, evoked responses differed as a function of perceptual interpretation by ∼25 msec after stimuli offset. Multivariate decoding of subjective perception based on prestimulus oscillatory phase was significant for alpha-band activity in the right medial temporal (V5/MT) area, with the strength of prestimulus connectivity between early visual areas and V5/MT being predictive of performance. In contrast, the longer integration window (∼130 msec) for AM showed evoked field differences only ∼250 msec after stimuli offset. Phase decoding of the perceptual outcome in AM trials was significant for theta-band activity in the right intraparietal sulcus. Prestimulus theta-band connectivity between V5/MT and intraparietal sulcus best predicted AM perceptual outcome. For both tasks, phase effects found could not be accounted by concomitant variations in power. These results show a strong relationship between specific spatiotemporal binding windows and specific oscillations, linked to the information flow between different areas of the where and when visual pathways., (© 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)

Interactions between different cortical rhythms, such as slow and fast oscillations, have been hypothesized to underlie many cognitive functions. In patients diagnosed with schizophrenia, there is some evidence indicating that the interplay between slow and fast oscillations might be impaired or disrupted. In this study, we investigated multiple oscillatory interactions in schizophrenia using a novel approach based on information theory. This method allowed us to investigate interactions from a new perspective, where two or more rhythm interactions could be analyzed at the same time. We calculated the mutual information of multiple rhythms (MIMR) for EEG segments registered in resting state. Following previous studies, we focused on rhythm interactions between theta, alpha, and gamma. The results showed that, in general, MIMR was higher in patients than in controls for alpha-gamma and theta-gamma couplings. This finding of an increased coupling between slow and fast rhythms in schizophrenia may indicate complex interactions in the Default Mode Network (DMN) related to hyperactivation of internally guided cognition., (© 2023. The Author(s).)

Objective: To verify if, by three distinct quantifiers, the measured electroencephalographic signal at rest is different from the signal measured during a word reading situation, especially considering the faster rhythms, gamma and high-gamma, as it occurs in clinical rhythms (delta to beta). Methods: A total of 96 electroencephalographic signals measured from neurologically healthy volunteers were evaluated at two moments: resting and word reading. Each signal segment was measured by three quantifiers that separately assess normalized power, percent power, and right and left hemisphere coherence. The Mann-Whitney test was used to compare the results of the quantifiers in each brain range. Results: The gamma and high-gamma rhythms presented a more distinct behavior when comparing the analyzed moments (resting and reading) than the clinical rhythms. Conclusion: This finding contributes to the scarce literature on faster rhythms, which can contain information that is normally disregarded in neurological clinical practice. [ABSTRACT FROM AUTHOR]

Facial Expression (FE) recognition is a major marker of emotional ability. Behavioral studies show that FE recognition ability decreases with aging. Studying how event-related brain oscillations change with normal aging is important to better understand the underlying mechanisms of emotional processes. The aim of this study is to investigate changes in FE recognition due to normal aging using the EEG-Brain Oscillations approach. Fifteen young and fifteen elderly healthy subjects were included in the study. 15 photographs were used with 5 different FEs (angry, happy, neutral, sad, fearful). After each EEG recording session, subjects were asked to identify the FEs that were presented. Event-related delta, theta and, alpha phase-locking and frequency-band responses were analyzed. In the FE recognition part of the study, young subjects obtained better scores than the elderly subjects. There was a significant result regarding the locationXgroup comparison in the delta response; the young group had a higher delta response than the elderly group over the occipital area. There were significant locationXgroup differences in the theta and alpha phase locking values; the elderly group had higher theta and alpha phase locking values than the young group in the frontal area. Group differences were significant in the theta response and theta phase locking; the elderly individuals' theta response and phase locking values were higher compared to those of the young individuals. In elderly individuals, FE recognition impairment has been observed. It has been shown that the impairment may be characterized by decreased occipital delta responses and phase locking. This can be interpreted to mean that elderly individuals may have developed different brain dynamics as a compensating mechanism since they are not as efficient as young individuals in performing these functions. • Facial expressions (FE) recognition is a fundamental marker of emotional abilities. • Emotional abilities change with aging. • We have suggested electrophysiological neural correlates of the emotional aging. • Dynamic patterns of FE has been explored in the elderly via the brain oscillations. [ABSTRACT FROM AUTHOR]

Delta waves have been described as periods of generalized silence across the cortex, and their alternation with periods of endogenous activity results in the slow oscillation of slow-wave sleep. Despite evidence that delta waves are instrumental for memory consolidation, their specific role in reshaping cortical functional circuits remains puzzling. In a rat model, we found that delta waves are not periods of complete silence and that the residual activity is not mere neuronal noise. Instead, cortical cells involved in learning a spatial memory task subsequently formed cell assemblies during delta waves in response to transient reactivation of hippocampal ensembles during ripples. This process occurred selectively during endogenous or induced memory consolidation. Thus, delta waves represent isolated cortical computations tightly related to ongoing information processing underlying memory consolidation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the limiting behavior of Riemann solutions to the Euler equations for compressible fluids in power law is studied as the adiabatic exponent goes to zero. We show that the limit solution forms the delta wave of the zero pressure gas dynamics in the distribution sense. At the end of the paper, we also give some numerical results to present the phenomena of concentration. Highlights • Delta wave, Riemann solutions. • Formation of delta wave. • Pressureless Limit of Euler equations. • Zero pressure gas dynamics. • Numerical simulations. [ABSTRACT FROM AUTHOR]

Rhythmic activity in populations of neurons is associated with cognitive and motor function. Our understanding of the neuronal mechanisms underlying these core brain functions has benefitted from demonstrations of cellular, synaptic, and network phenomena, leading to the generation of discrete rhythms at the local network level. However, discrete frequencies of rhythmic activity rarely occur alone. Despite this, little is known about why multiple rhythms are generated together or what mechanisms underlie their interaction to promote brain function. One overarching theory is that different temporal scales of rhythmic activity correspond to communication between brain regions separated by different spatial scales. To test this, we quantified the cross-frequency interactions between two dominant rhythms--theta and delta activity--manifested during magnetoencephalography recordings of subjects performing a word-pair semantic decision task. Semantic processing has been suggested to involve the formation of functional links between anatomically disparate neuronal populations over a range of spatial scales, and a distributed network was manifest in the profile of theta-delta coupling seen. Furthermore, differences in the pattern of theta-delta coupling significantly correlated with semantic outcome. Using an established experimental model of concurrent delta and theta rhythms in neocortex, we show that these outcome-dependent dynamics could be reproduced in a manner determined by the strength of cholinergic neuromodulation. Theta-delta coupling correlated with discrete neuronal activity motifs segregated by the cortical layer, neuronal intrinsic properties, and long-range axonal targets. Thus, the model suggested that local, interlaminar neocortical theta-delta coupling may serve to coordinate both cortico-cortical and corticosubcortical computations during distributed network activity. [ABSTRACT FROM AUTHOR]

First attempts have demonstrated that the application of alpha/delta neurofeedback in the treatment of chronic tinnitus leads to a reduction of symptoms at the group level. However, recent research also suggests that chronic tinnitus is a decidedly heterogeneous phenomenon, one that requires treatment of distinct subgroups or even on an individual level. Thus, the purpose of this study was to evaluate an individually adjusted alpha/delta neurofeedback protocol. Following previous studies, the delta band fixed between 3 and 4 Hz was chosen as the frequency for inhibition. However, unlike the previous studies, the frequency range for the rewarded alpha band was not fixed between 8 and 12 Hz but rather individually determined according to each patient's specific alpha peak frequency (IAF). Twenty-six chronic tinnitus patients participated in 15 weekly neurofeedback training sessions and extensive pre- and post-tests, as well as follow-up testing 3 and 6 months after training. The main outcome measures were tinnitus-related distress measured with the Tinnitus Handicap Inventory (THI) and Tinnitus Questionnaire (TQ), tinnitus loudness, and pre- and post-training resting-state EEG activity in trained frequency bands. In Results, the neurofeedback protocol led to a significant reduction of tinnitus-related distress and tinnitus loudness. While distress remained on a low level even 6 months after the completion of training, loudness returned to baseline levels in the follow-up period. In addition, resting-state EEG activity showed an increase in the trained alpha/delta ratio over the course of the training. This ratio increase was related to training-induced changes of tinnitus-related distress as measured with TQ, mainly due to increases in the alpha frequency range. In sum, this study confirms the alpha/delta neurofeedback as a suitable option for the treatment of chronic tinnitus and represents a first step towards the development of individual neurofeedback protocols. This clinical trial was registered online at ClinicalTrials.gov (NCT02383147) and kofam.ch (SNCTP000001313). [ABSTRACT FROM AUTHOR]

Sleep is a physiological state that plays important role in the recovery of fatigue. However, the relationship between the physiological status of sleep and subjective fatigue remains unknown. In the present study, we hypothesized that the non-recovery of fatigue at wake time due to non-restorative sleep might be ascribed to changes in specific parameters of electroencephalography (EEG) and heart rate variability (HRV) in poor sleepers. Twenty healthy female shift-working nurses participated in the study. Subjective fatigue was assessed using the visual analog scale (VAS) at bedtime and wake time. During sleep on the night between 2 consecutive day shifts, the EEG powers at the frontal pole, HRV based on electrocardiograms, and distal-proximal gradient of skin temperature were recorded and analyzed. The results indicated that the subjects with high fatigue on the VAS at wake time exhibited (1) a decrease in deep non-rapid eye movement (NREM) (stageN3) sleep duration in the first sleep cycle; (2) a decrease in REM latency; (3) a decrease in ultra-slow and delta EEG powers, particularly from 30 to 65 min after sleep onset; (4) a decrease in the total power of HRV, particularly from 0 to 30 min after sleep onset; (5) an increase in the very low frequency component of HRV; and (6) a smaller increase in the distal-proximal gradient of skin temperature, than those of the subjects with low fatigue levels. The correlational and structural equation modeling analyses of these parameters suggested that an initial decrease in the total power of HRV from 0 to 30 min after sleep onset might inhibit the recovery from fatigue during sleep (i.e., increase the VAS score at wake time) via its effects on the ultra-slow and delta powers from 30 to 65 min after sleep onset, stageN3 duration in the first sleep cycle, REM latency, and distal-proximal gradient of skin temperature. These findings suggest an important role of these physiological factors in recovery from fatigue during sleep, and that interventions to modify these physiological factors might ameliorate fatigue at wake time. [ABSTRACT FROM AUTHOR]

Abrupt withdrawal from antiepileptic drugs is followed by increased occurrence of epileptic seizures, a phenomenon known as the “rebound effect”. By stopping treatment with levetiracetam (LEV 300 mg/kg/day, n = 15; vs. saline, n = 15), we investigated the rebound effect in adult male Sprague-Dawley rats. LEV was continuously administered using osmotic minipumps, 7 weeks after the intraperitoneal administration of kainic acid (15 mg/kg). The effects of LEV were determined by comparing time intervals, treatments, and interactions between these main factors. Seizures were evaluated by video-electrocorticographic recordings and power band spectrum analysis. Furthermore, we assessed endogenous neurosteroid levels by liquid chromatography-electrospray-tandem mass spectrometry. LEV significantly reduced the percentage of rats experiencing seizures, reduced the seizure duration, and altered cerebral levels of neurosteroids. In the first week of LEV discontinuation, seizures increased abruptly up to 700% (p = 0.002, Tukey’s test). The power of delta band in the seizure postictal component was related to the seizure occurrence after LEV withdrawal (r2 = 0.73, p < 0.001). Notably, allopregnanolone hippocampal levels were positively related to the seizure occurrence (r2 = 0.51, p = 0.02) and to the power of delta band (r2 = 0.67, p = 0.004). These findings suggest a role for the seizure postictal component in the rebound effect, which involves an imbalance of hippocampal neurosteroid levels.

Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.

Depressive disorder has been known to be associated with high variability in resting-state electroencephalography (EEG) signals. However, this phenomenon is often ignored in stimulus-related brain activities. This study proposed a new method to explore the EEG variability evoked by transcranial magnetic stimulation (TMS, TMS-EEG) in depressive disorder (DE) patients. The TMS-EEG data were collected from 34 DE patients and 36 healthy controls (HC). The maximum eigenvalue of the real binary correlation matrix, calculated between different trials using cross-correlation and surrogate methods, was extracted to assess trial-by-trial variability (TTV) of TMS-EEG. The new method was found to more sensitive and reliable than the standard deviation method. DE patients exhibited significantly smaller TTV in Gamma band and greater TTV in Delta band than HC. Furthermore, the HAMD-17 scores were negatively correlated with TTV values in Gamma band. This study represented the first investigation into the TTV in TMS-EEG data and revealed abnormal values in DE patients. Those findings enhance our understanding of TMS-EEG technology and provide valuable insights for studying the characteristics of DE.

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.

We consider a Riemann problem for the shallow water system ut+(v+12u2)x=0, vt+(u+uv)x=0 and evaluate all singular solutions of the form u(x,t)=l(t)+b(t)H(x-γ(t))+a(t)δ(x-γ(t)), v(x,t)=k(t)+c(t)H(x-γ(t)), where l,b,a,k,c,γ:R→R are C1-functions of time t, H is the Heaviside function, and δ stands for the Dirac measure with support at the origin. A product of distributions, not constructed by approximation processes, is used to define a solution concept, that is a consistent extension of the classical solution concept. Results showing the advantage of this framework are briefly presented in the introduction. [ABSTRACT FROM AUTHOR]

Signal transforms are very important tools to extract useful information from scientific, engineering, or medical raw data. Unfortunately, traditional transform techniques impose unrealistic assumptions on the signal, often producing erroneous interpretation of results. Well-known integral transforms, such as short-time Fourier transform, though have fast implementation algorithms (e.g., FFT), are still computationally expensive. They have multiple parameters that should be tuned, and it is not readily clear how to tune them for long-duration non-stationary signals. To solve these problems, one needs a computationally inexpensive transform with no parameters that will highlight important data aspects. We propose a simple transform based on extrema points of the signal. The transform value at a given point is calculated based on the distance and magnitude difference of two extrema points it lies between, rather than considering every point around it. We discuss implementation of the developed algorithm and show examples of successfully applying the transform to noise-corrupted synthetic signals and to sleep studies detecting delta wave in brain EEG signal. Ideas for improvement and further research are discussed. [ABSTRACT FROM AUTHOR]

Left hemispheric anterior and posterior activities are related to positive affect and inversely right-sided anterior and posterior brain activity is associated with negative affect. In this present study the anterior and posterior regions of the brain were analysed using Electro Encephalogram (EEG) with relaxation therapy intervention. The study was framed as quasi experimental design with pre-test and post-test having sample (N=5) of five high achievers with the mean age of 16.2 ± 1.64 was selected for the study. Initially the pre-test values of brain regions of all the students were recorded and test anxiety scores were also measured using test anxiety inventory by Charles D. Spielberger (1980). Relaxation therapy intervention programme was conducted for all the students for about a month in order to reduce the adverse emotions. At the end of the intervention programme post-test was recorded on brain regions and test anxiety scores. Significant differences were observed in absolute powers of pre and post-test scores of anterior and posterior regions and also in alpha and delta waves. Differences were noticed in pre and post-test asymmetries of anterior and posterior regions. Findings suggested that left sided brain activity is responsible for reduction in test anxiety. Relaxation therapy acts as a useful tool to reduce test anxiety by increasing the positive affect and the changes occurred are not only functional and it can be structural too to produce long term effects. [ABSTRACT FROM AUTHOR]

Objective: Unknown ethiology Background: Lown-Ganong-Levine syndrome, includes a short PR interval, normal QRS complex, and paroxysmal tachycardia. The pathophysiology of this syndrome includes an accessory pathway connecting the atria and the atrioventricular (AV) node (James fiber), or between the atria and the His bundle (Brechenmacher fiber). Similar features are seen in enhanced atrioventricular nodal conduction (EAVNC), with the underlying pathophysiology due to a fast pathway to the AV node, and with the diagnosis requiring specific electrophysiologic criteria. Case Report: A 17-year-old man presented with a history of recurrent narrow-complex and wide-complex tachycardia on electrocardiogram (ECG). An electrophysiologic study showed an unusually short atrial to His (AH) conduction interval and a normal His to ventricle (HV) interval, without a delta wave. Two stable AH intervals coexisted in the same atrial pacing cycle length. In the recovery curve study, this pathway had a flat conduction curve without an AH increase until the last 60 ms, before reaching the effective refractory period. These ECG changes did not respond to an adenosine challenge. When this pathway became intermittent, there was a paradoxical response to adenosine challenge with conduction via a short AH interval, but without conduction block. Catheter ablation of the AV nodal region resulted in a normalized AH interval, decremental conduction properties, and resulted in a positive response to an adenosine challenge. Conclusions: In this case of Lown-Ganong-Levine syndrome, electrophysiologic studies supported the role of the accessory pathway of James fibers. [ABSTRACT FROM AUTHOR]

Neural mass models (NMMs) are increasingly used to uncover the large-scale mechanisms of brain rhythms in health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite being considered low dimensional in comparison to micro-scale, neuronal network models, with regards to understanding the relationship between parameters and dynamics, NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. Therefore, we need alternative methods to characterise dynamics of NMMs in high dimensional parameter spaces. Here, we introduce a statistical framework that enables the efficient exploration of the relationship between model parameters and selected features of the simulated, emergent model dynamics of NMMs. We combine the classical machine learning approaches of trees and random forests to enable studying the effect that varying multiple parameters has on the dynamics of a model. The method proceeds by using simulations to transform the mathematical model into a database. This database is then used to partition parameter space with respect to dynamic features of interest, using random forests. This allows us to rapidly explore dynamics in high dimensional parameter space, capture the approximate location of qualitative transitions in dynamics and assess the relative importance of all parameters in the model in all dimensions simultaneously. We apply this method to a commonly used NMM in the context of transitions to seizure dynamics. We find that the inhibitory sub-system is most crucial for the generation of seizure dynamics, confirm and expand previous findings regarding the ratio of excitation and inhibition, and demonstrate that previously overlooked parameters can have a significant impact on model dynamics. We advocate the use of this method in future to constrain high dimensional parameter spaces enabling more efficient, person-specific, model calibration. [ABSTRACT FROM AUTHOR]

Background We present the results of an analysis of the low frequency (LF) (0.25–1 Hz) and delta (1–4 Hz) waves during human sleep. Our objective was to investigate whether LF and delta waves should be considered as separate entities. Methods The slow-wave electroencephalogram (EEG) activity of 2 sets of 10 young, healthy volunteers was analysed utilising the rarely-used Lomb-Scargle periodogram. This method has advantages over the more commonly-used Fast Fourier Transform analysis. Results During the night, the frequencies of the most powerful waves are concentrated in the 0.5–2 Hz range and show a continuous tendency to shift towards slower frequencies during sleep. Comparison with existing methods When considering the frequency dynamics of slow-wave activity below 3 Hz, the unifying theory of LF and delta waves is more parsimonious than the idea that there is a different origin and regulation for the two sub-bands. Conclusions The unifying theory of LF and delta waves is the simplest explanation for the slow-wave activity of the EEG below 3 Hz. This finding is important for the clinical use of slow-wave activity. [ABSTRACT FROM AUTHOR]

Using the diagram technique for the Hubbard operators an integral equation that determines the order parameter of the superconducting phase Δ ( p ) was obtained in the framework of t – J – V and t – J *– V models on triangular lattice. It is shown that there are two scenarios of formation of superconducting phase with gapless spectrum at the critical concentration of carriers x c . The effect of three-center terms on implementation of this phase was also considered. [ABSTRACT FROM AUTHOR]

The dynamic attending theory proposes that rhythms entrain periodic fluctuations of attention which modulate the gain of sensory input. However, temporal expectancies can also be driven by the mere passage of time (foreperiod effect). It is currently unknown how these two types of temporal expectancy relate to each other, i.e. whether they work in parallel and have distinguishable neural signatures. The current research addresses this issue. Participants either tapped a 1 Hz rhythm (active task) or were passively presented with the same rhythm using tactile stimulators (passive task). Based on this rhythm an auditory target was then presented early, in synchrony, or late. Behavioural results were in line with the dynamic attending theory as RTs were faster for in- compared to out-of-synchrony targets. Electrophysiological results suggested self-generated and externally induced rhythms to entrain neural oscillations in the delta frequency band. Auditory ERPs showed evidence of two distinct temporal expectancy processes. Both tasks demonstrated a pattern which followed a linear foreperiod effect. In the active task, however, we also observed an ERP effect consistent with the dynamic attending theory. This study shows that temporal expectancies generated by a rhythm and expectancy generated by the mere passage of time can work in parallel and sheds light on how these mechanisms are implemented in the brain. [ABSTRACT FROM AUTHOR]

Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40–50 s long blocks of data from 64 scalp electrodes were equally high (80–90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces. [ABSTRACT FROM AUTHOR]

This paper is devoted to the Riemann problem for the data-fitted second-order macroscopic production model when one of the Riemann initial data is at a vacuum state or at rest. The Riemann solutions are constructed completely by using the continuous method with respect to the initial data, which include some non-classical waves such as the delta standing wave and the superposition of a shock wave and a contact discontinuity etc. In addition, an example is given to explain the wave interaction problem when the vacuum state is involved. [ABSTRACT FROM AUTHOR]

Brain dynamic changes associated with schizophrenia are largely equivocal, with interpretation complicated by many factors, such as the presence of therapeutic agents and the complex nature of the syndrome itself. Evidence for a brain-wide change in individual network oscillations, shared by all patients, is largely equivocal, but stronger for lower (delta) than for higher (gamma) bands. However, region-specific changes in rhythms across multiple, interdependent, nested frequencies may correlate better with pathology. Changes in synaptic excitation and inhibition in schizophrenia disrupt delta rhythm-mediated cortico-cortical communication, while enhancing thalamocortical communication in this frequency band. The contrasting relationships between delta and higher frequencies in thalamus and cortex generate frequency mismatches in inter-regional connectivity, leading to a disruption in temporal communication between higher-order brain regions associated with mental time travel. [ABSTRACT FROM AUTHOR]

Cold-sensitive and nociceptive neural pathways interact to shape the quality and intensity of thermal and pain perception. Yet the central processing of cold thermosensation in the human brain has not been extensively studied. Here, we used magnetoencephalography and EEG in healthy volunteers to investigate the time course (evoked fields and potentials) and oscillatory activity associated with the perception of cold temperature changes. Nonnoxious cold stimuli consisting of Δ3°C and Δ5°C decrements from an adapting temperature of 35°C were delivered on the dorsum of the left hand via a contact thermode. Coldevoked fields peaked at around 240 and 500 ms, at peak latencies similar to the N1 and P2 cold-evoked potentials. Importantly, coldrelated changes in oscillatory power indicated that innocuous thermosensation is mediated by oscillatory activity in the range of delta (1-4 Hz) and gamma (55-90 Hz) rhythms, originating in operculoinsular cortical regions. We suggest that delta rhythms coordinate functional integration between operculo-insular and frontoparietal regions, while gamma rhythms reflect local sensory processing in operculo-insular areas. NEW & NOTEWORTHY: Using magnetoencephalography, we identified spatiotemporal features of central cold processing, with respect to the time course, oscillatory profile, and neural generators of cold-evoked responses in healthy human volunteers. Cold thermosensation was associated with low- and high-frequency oscillatory rhythms, both originating in operculo-insular regions. These results support further investigations of central cold processing using magnetoencephalography or EEG and the clinical utility of cold-evoked potentials for neurophysiological assessment of cold-related smallfiber function and damage. [ABSTRACT FROM AUTHOR]

Patients with chronic pain due to neuropathy or musculoskeletal injury frequently exhibit reduced alpha and increased theta power densities. However, little is known about electrical brain activity and chronic pain in patients with rheumatoid arthritis (RA). For this purpose, we evaluated power densities of spontaneous electroencephalogram (EEG) band frequencies (delta, theta, alpha, and beta) in females with persistent pain due to RA. This was a cross-sectional study of 21 participants with RA and 21 healthy controls (mean age = 47.20; SD = 10.40). EEG was recorded at rest over five minutes with participant's eyes closed. Twenty electrodes were placed over five brain regions (frontal, central, parietal, temporal, and occipital). Significant differences were observed in depression and anxiety with higher scores in RA participants than healthy controls (p = .002). Participants with RA exhibited increased average absolute alpha power density in all brain regions when compared to controls (F (1.39) = 6.39, p = .016), as well as increased average relative alpha power density (F (1.39) = 5.82, p = .021) in all regions, except the frontal region, controlling for depression/anxiety. Absolute theta power density also increased in the frontal, central, and parietal regions for participants with RA when compared to controls (F(1,39) = 4.51, p = .040), controlling for depression/anxiety. Differences were not exhibited on beta and delta absolute and relative power densities. The diffuse increased alpha may suggest a possible neurogenic mechanism for chronic pain in individuals with RA.