Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

Neurovascular coupling (NVC) provides important insights into the intricate activity of brain functioning and may aid in the early diagnosis of brain diseases. Emerging evidences have shown that NVC could be assessed by the coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). However, this endeavor presents significant challenges due to the absence of standardized methodologies and reliable techniques for coupling analysis of these two modalities. In this study, we introduced a novel method, i.e., the collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM) approach to advance coupling analysis of EEG and fNIRS. To validate the robustness and reliability of the CMVGP-CCM method, we conducted extensive experiments using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths. In addition, we employed the CMVGP-CCM method to explore the NVC between EEG and fNIRS signals collected from 26 healthy participants using a working memory (WM) task. Results revealed a significant causal effect of EEG signals, particularly the delta, theta, and alpha frequency bands, on the fNIRS signals during WM. This influence was notably observed in the frontal lobe, and its strength exhibited a decline as cognitive demands increased. This study illuminates the complex connections between brain electrical activity and cerebral blood flow, offering new insights into the underlying NVC mechanisms of WM.

A multiscale symbolic approach to decoding delta and ripple oscillation bands as biomarkers for epileptiform discharges.

We use a multiscale symbolic approach to study the complex dynamics of temporal lobe refractory epilepsy employing high-resolution intracranial electroencephalogram (iEEG). We consider the basal and preictal phases and meticulously analyze the dynamics across frequency bands, focusing on high-frequency oscillations up to 240 Hz. Our results reveal significant periodicities and critical time scales within neural dynamics across frequency bands. By bandpass filtering neural signals into delta, theta, alpha, beta, gamma, and ripple high-frequency bands (HFO), each associated with specific neural processes, we examine the distinct nonlinear dynamics. Our method introduces a reliable approach to pinpoint intrinsic time lag scales τ within frequency bands of the basal and preictal signals, which are crucial for the study of refractory epilepsy. Using metrics such as permutation entropy (H), Fisher information (F), and complexity (C), we explore nonlinear patterns within iEEG signals. We reveal the intrinsic τmax that maximize complexity within each frequency band, unveiling the nonlinear subtle patterns of the temporal structures within the basal and preictal signal. Examining the H×F and C×F values allows us to identify differences in the delta band and a band between 200 and 220 Hz (HFO 6) when comparing basal and preictal signals. Differences in Fisher information in the delta and HFO 6 bands before seizures highlight their role in capturing important system dynamics. This offers new perspectives on the intricate relationship between delta oscillations and HFO waves in patients with focal epilepsy, highlighting the importance of these patterns and their potential as biomarkers.

Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency.

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.

Preoperative recovery sleep ameliorates postoperative cognitive dysfunction aggravated by sleep fragmentation in aged mice by enhancing EEG delta-wave activity and LFP theta oscillation in hippocampal CA1.

Sleep fragmentation (SF) is a common sleep problem experienced during the perioperative period by older adults, and is associated with postoperative cognitive dysfunction (POCD). Increasing evidence indicates that delta-wave activity during non-rapid eye movement (NREM) sleep is involved in sleep-dependent memory consolidation and that hippocampal theta oscillations are related to spatial exploratory memory. Recovery sleep (RS), a self-regulated state of sleep homeostasis, enhances delta-wave power and memory performance in sleep-deprived older mice. However, it remains unclear whether RS therapy has a positive effect on cognitive changes following SF in older mouse models. Therefore, this study aimed to explore whether preoperative RS can alleviate cognitive deficits in aged mice with SF. A model of preoperative 24-h SF combined with exploratory laparotomy-induced POCD was established in 18-month-old mice. Aged mice were treated with preoperative 6-h RS following SF and postoperative 6-h RS following surgery, respectively. The changes in hippocampus-dependent cognitive function were investigated using behavioral tests, electroencephalography (EEG), local field potential (LFP), magnetic resonance imaging, and neuromorphology. Mice that underwent 24-h SF combined with surgery exhibited severe spatial memory impairment; impaired cognitive performance could be alleviated by preoperative RS treatment. In addition, preoperative RS increased NREM sleep; enhanced EEG delta-wave activity and LFP theta oscillation in the hippocampal CA1; and improved hippocampal perfusion, microstructural integrity, and neuronal damage. Taken together, these results provide evidence that preoperative RS may ameliorate the severity of POCD aggravated by SF by enhancing delta slow-wave activity and hippocampal theta oscillation, and by ameliorating the reduction in regional cerebral blood flow and white matter microstructure integrity in the hippocampus.

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality.

BACKGROUND: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. OBJECTIVES: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. METHODS: The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. RESULTS: During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. CONCLUSIONS: The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

Mutual information of multiple rhythms in schizophrenia.

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.

Distinct Cortical Networks Subserve Spatio-temporal Sampling in Vision through Different Oscillatory Rhythms.

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.

CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network.

Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieves a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from our Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://github.com/LianghuiGuo/CLEP-STS-Net.

Frontal Intermittent Rhythmic Delta Activity Is a Useful Diagnostic Tool of Neurotoxicity After CAR T-Cell Infusion.

BACKGROUND AND OBJECTIVES: Chimeric antigen receptor (CAR) T-cell therapies have dramatically improved the prognosis of patients with relapsed or refractory hematologic malignancies; however, cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome (ICANS) occur in ∼100 and 50% of patients, respectively. This study aimed to determine whether EEG patterns may be considered as diagnostic tools for ICANS. METHODS: Patients who received CAR T-cell therapy at Montpellier University Hospital between September 2020 and July 2021 were prospectively enrolled. Neurologic signs/symptoms and laboratory parameters were monitored daily for 14 days after CAR T-cell infusion. EEG and brain MRI were performed between day 6 and 8 after CAR T-cell infusion. EEG was performed again on the day of ICANS occurrence, if outside this time window. All collected data were compared between patients with and without ICANS. RESULTS: Thirty-eight consecutive patients were enrolled (14 women; median age: 65 years, interquartile range: [55-74]). ICANS was observed in 17 of 38 patients (44%) after a median time of 6 days after CAR T-cell infusion (4-8). The median ICANS grade was 2 (1-3). Higher C-reactive protein peak (146 mg/L [86-256], p = 0.004) at day 4 (3-6), lower natremia (131 mmol/L [129-132], p = 0.005) at day 5 (3-6), and frontal intermittent rhythmic delta activity (FIRDA, p < 0.001) on EEG between days 6 and 8 after infusion were correlated with ICANS occurrence. FIRDA was only observed in patients with ICANS (N = 15/17, sensitivity of 88%) and disappeared after ICANS resolution, usually after steroid therapy. Except for hyponatremia, no other toxic/metabolic marker was associated with FIRDA (p = 0.002). The plasma concentration of copeptin, a surrogate marker of antidiuretic hormone secretion, assessed at day 7 after infusion, was significantly higher in patients with (N = 8) than without (N = 6) ICANS (p = 0.043). DISCUSSION: FIRDA is a reliable diagnostic tool for ICANS, with a sensitivity of 88% and a negative predictive value of 100%. Moreover, as this EEG pattern disappeared concomitantly with ICANS resolution, FIRDA could be used to monitor neurotoxicity. Finally, our study suggests a pathogenic pathway that starts with increased C-reactive protein, followed by hyponatremia and eventually ICANS and FIRDA. More studies are required to confirm our results. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that FIRDA on spot EEG accurately distinguishes patients with ICANS compared with those without after CAR T-cell therapy for hematologic malignancy.

IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.

This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert-Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5-4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500-600, 800-900, 800-900, and 500-600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons.

High prevalence of electroencephalographic frontal intermittent rhythmic delta activity in patients with moderately severe COVID-19.

INTRODUCTION: The aim of our study was to analyse EEG findings in patients with COVID-19 not requiring respiratory support. MATERIAL AND METHODS: We reviewed EEGs performed in patients with COVID-19 between April 2020 and May 2021 at the University Hospital in Kraków, Poland. Demographic and clinical data, including comorbid conditions, discharge disposition, survival, neuroimaging findings, laboratory results, and treatment was collected. RESULTS: The study included 44 EEGs performed in 35 patients (51.4% females), aged 65.5 ± 13.9 years. Almost all patients had at least one comorbidity, and one-third had one or more preexisting neurological conditions. Three quarters of EEGs were abnormal. The most frequent EEG finding was background slowing (16 patients; 45.7%). Frontal findings included frontally predominant rhythmic delta (FIRDA) in 10 (28.6%) patients and focal slowing in the left frontal lobe. Patients with abnormal EEG significantly more often required oxygen supplementation (p = 0.003) and were less likely to recover (p = 0.048). CONCLUSIONS AND CLINICAL IMPLICATIONS: Patients with COVID-19 infection may frequently manifest with an abnormal EEG. FIRDA seems to be a frequent EEG pattern in less severe cases of COVID-19 infection. Future studies are needed to establish whether COVID-19 infection increases the risk for FIRDA, and to investigate its pathogenesis.

Kinase signalling in excitatory neurons regulates sleep quantity and depth.

Progress has been made in the elucidation of sleep and wakefulness regulation at the neurocircuit level1,2. However, the intracellular signalling pathways that regulate sleep and the neuron groups in which these intracellular mechanisms work remain largely unknown. Here, using a forward genetics approach in mice, we identify histone deacetylase 4 (HDAC4) as a sleep-regulating molecule. Haploinsufficiency of Hdac4, a substrate of salt-inducible kinase 3 (SIK3)3, increased sleep. By contrast, mice that lacked SIK3 or its upstream kinase LKB1 in neurons or with a Hdac4S245A mutation that confers resistance to phosphorylation by SIK3 showed decreased sleep. These findings indicate that LKB1-SIK3-HDAC4 constitute a signalling cascade that regulates sleep and wakefulness. We also performed targeted manipulation of SIK3 and HDAC4 in specific neurons and brain regions. This showed that SIK3 signalling in excitatory neurons located in the cerebral cortex and the hypothalamus positively regulates EEG delta power during non-rapid eye movement sleep (NREMS) and NREMS amount, respectively. A subset of transcripts biased towards synaptic functions was commonly regulated in cortical glutamatergic neurons through the expression of a gain-of-function allele of Sik3 and through sleep deprivation. These findings suggest that NREMS quantity and depth are regulated by distinct groups of excitatory neurons through common intracellular signals. This study provides a basis for linking intracellular events and circuit-level mechanisms that control NREMS.

Alzheimer's Disease with Epileptiform EEG Activity: Abnormal Cortical Sources of Resting State Delta Rhythms in Patients with Amnesic Mild Cognitive Impairment.

BACKGROUND: Patients with amnesic mild cognitive impairment due to Alzheimer's disease (ADMCI) typically show a "slowing" of cortical resting-state eyes-closed electroencephalographic (rsEEG) rhythms. Some of them also show subclinical, non-convulsive, and epileptiform EEG activity (EEA) with an unclear relationship with that "slowing." OBJECTIVE: Here we tested the hypothesis that the "slowing" of rsEEG rhythms is related to EEA in ADMCI patients. METHODS: Clinical and instrumental datasets in 62 ADMCI patients and 38 normal elderly (Nold) subjects were available in a national archive. No participant had received a clinical diagnosis of epilepsy. The eLORETA freeware estimated rsEEG cortical sources. The area under the receiver operating characteristic curve (AUROCC) indexed the accuracy of eLORETA solutions in the classification between ADMCI-EEA and ADMCI-noEEA individuals. RESULTS: EEA was observed in 15% (N = 8) of the ADMCI patients. The ADMCI-EEA group showed: 1) more abnormal Aß42 levels in the cerebrospinal fluid as compared to the ADMCI-noEEA group and 2) higher temporal and occipital delta (<4 Hz) rsEEG source activities as compared to the ADMCI-noEEA and Nold groups. Those source activities showed moderate accuracy (AUROCC = 0.70-0.75) in the discrimination between ADMCI-noEEA versus ADMCI-EEA individuals. CONCLUSION: It can be speculated that in ADMCI-EEA patients, AD-related amyloid neuropathology may be related to an over-excitation in neurophysiological low-frequency (delta) oscillatory mechanisms underpinning cortical arousal and quiet vigilance.

Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.

Drowsiness is a leading cause of accidents on the road as it negatively affects the driver's ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.

Accelerated Aging Characterizes the Early Stage of Alzheimer's Disease.

For Alzheimer's disease (AD), aging is the main risk factor, but whether cognitive impairments due to aging resemble early AD deficits is not yet defined. When working with mouse models of AD, the situation is just as complicated, because only a few studies track the progression of the disease at different ages, and most ignore how the aging process affects control mice. In this work, we addressed this problem by comparing the aging process of PS2APP (AD) and wild-type (WT) mice at the level of spontaneous brain electrical activity under anesthesia. Using local field potential recordings, obtained with a linear probe that traverses the posterior parietal cortex and the entire hippocampus, we analyzed how multiple electrical parameters are modified by aging in AD and WT mice. With this approach, we highlighted AD specific features that appear in young AD mice prior to plaque deposition or that are delayed at 12 and 16 months of age. Furthermore, we identified aging characteristics present in WT mice but also occurring prematurely in young AD mice. In short, we found that reduction in the relative power of slow oscillations (SO) and Low/High power imbalance are linked to an AD phenotype at its onset. The loss of SO connectivity and cortico-hippocampal coupling between SO and higher frequencies as well as the increase in UP-state and burst durations are found in young AD and old WT mice. We show evidence that the aging process is accelerated by the mutant PS2 itself and discuss such changes in relation to amyloidosis and gliosis.

Brain, interrupted: alpha/delta EEG ratio in survivors of pre- and post-natal adversity.

RESULTS: Young adults born at extremely low birth weight (prenatal adversity; N = 64, Mage = 23.14 years, SDage = 1.26 years) had a lower alpha/delta ratio score compared to normal birth weight controls (N = 76, Mage = 23.60 years, SDage = 1.09 years), while youth exposed to child maltreatment (postnatal adversity; N = 39, Mage = 16.18 years, SDage = 1.15) had a higher alpha/delta ratio compared to controls (N = 23, Mage = 16.00 years, SDage = 1.50 years). CONCLUSIONS: Our results suggest that being exposed to pre- and post-natal adversity may have different long-term consequences on brain development. We speculate that these differences might be associated with some of the different functional outcomes known to characterize each type of adverse experience.

Reliability of Neural Entrainment in the Human Auditory System.

Auditory stimuli are often rhythmic in nature. Brain activity synchronizes with auditory rhythms via neural entrainment, and entrainment seems to be beneficial for auditory perception. However, it is not clear to what extent neural entrainment in the auditory system is reliable over time, which is a necessary prerequisite for targeted intervention. The current study aimed to establish the reliability of neural entrainment over time and to predict individual differences in auditory perception from associated neural activity. Across two different sessions, human listeners (21 females, 17 males) detected silent gaps presented at different phase locations of a 2 Hz frequency-modulated (FM) noise while EEG activity was recorded. As expected, neural activity was entrained by the 2 Hz FM noise. Moreover, gap detection was sinusoidally modulated by the phase of the 2 Hz FM into which the gap fell. Critically, both the strength of neural entrainment as well as the modulation of performance by the stimulus rhythm were highly reliable over sessions. Moreover, gap detection was predictable from pregap neural 2 Hz phase and alpha amplitude. Our results demonstrate that neural entrainment in the auditory system and the resulting behavioral modulation are reliable over time, and both entrained delta and nonentrained alpha oscillatory activity contribute to near-threshold stimulus perception. The latter suggests that improving auditory perception might require simultaneously targeting entrained brain rhythms as well as the alpha rhythm.SIGNIFICANCE STATEMENT Neural activity synchronizes to the rhythms in sounds via neural entrainment, which seems to be important for successful auditory perception. A natural hypothesis is that improving neural entrainment, for example, via brain stimulation, should benefit perception. However, the extent to which neural entrainment is reliable over time, a necessary prerequisite for targeted intervention, has not been established. Using electroencephalogram recordings, we demonstrate that both neural entrainment to FM sounds and stimulus-induced behavioral modulation are reliable over time. Moreover, moment-by-moment fluctuations in perception are best predicted by entrained delta phase and nonentrained alpha amplitude. This work suggests that improving auditory perception might require simultaneously targeting entrained brain rhythms as well as the alpha rhythm.

Pros and cons in tinnitus brain: Enhancement of global connectivity for alpha and delta waves.

Interactions among cortical areas of tinnitus brain remained unclear. Weaker alpha and stronger delta activities in tinnitus have been noted over auditory cortices. However, the interplay between a single substrate with whole brain within alpha/delta band remained unknown. Thirty-one patients with chronic tinnitus were recruited. Thirty-four healthy volunteers served as controls. Magnetoencephalographic measurements of spontaneous activities were performed. The strength of alpha/delta activities was analyzed. By dividing cortices into 38 regions of interest (ROIs), measurements of connectivity were performed using amplitude envelope correlation (AEC). Global connectivity was calculated by adding and averaging connectivity of single ROI with every other region. There were no significant differences in mean power of alpha and delta band between groups, despite the trend of stronger alpha and weaker delta band in controls. The global connectivity of alpha wave was significantly stronger in tinnitus for left frontal pole, and of delta wave for bilateral pars orbitalis, bilateral superior temporal, bilateral middle temporal, right pars triangularis, right transverse temporal, right inferior temporal, and right supra-marginal. The global connectivity of alpha/delta waves was enhanced for tinnitus in designated ROIs of frontal/temporal/parietal lobes. The underlying mechanism(s) might be associated with augmentation/modulation of tinnitus perception. Our results corroborated the evolving consensus about neural correlates inside frontal/temporal/parietal lobes as essential elements of hubs for central processing of tinnitus. Further study to explore the resolution of effective connectivity between those ROIs and respective substrates by using AEC will be necessary for the evaluation of pathogenetic scenario for tinnitus.

Delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants.

The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding at multiple time scales. Neurophysiological signals are known to track the amplitude envelope of adult-directed speech (ADS), particularly in the theta-band. Acoustic analysis of infant-directed speech (IDS) has revealed significantly greater modulation energy than ADS in an amplitude-modulation (AM) band centred on ∼2 Hz. Accordingly, cortical tracking of IDS by delta-band neural signals may be key to language acquisition. Speech also contains acoustic information within its higher-frequency bands (beta, gamma). Adult EEG and MEG studies reveal an oscillatory hierarchy, whereby low-frequency (delta, theta) neural phase dynamics temporally organize the amplitude of high-frequency signals (phase amplitude coupling, PAC). Whilst consensus is growing around the role of PAC in the matured adult brain, its role in the development of speech processing is unexplored. Here, we examined the presence and maturation of low-frequency (<12 Hz) cortical speech tracking in infants by recording EEG longitudinally from 60 participants when aged 4-, 7- and 11- months as they listened to nursery rhymes. After establishing stimulus-related neural signals in delta and theta, cortical tracking at each age was assessed in the delta, theta and alpha [control] bands using a multivariate temporal response function (mTRF) method. Delta-beta, delta-gamma, theta-beta and theta-gamma phase-amplitude coupling (PAC) was also assessed. Significant delta and theta but not alpha tracking was found. Significant PAC was present at all ages, with both delta and theta -driven coupling observed.