Event-related phase reorganization may explain evoked neural dynamics

The Role of the LC-NE System in Attention: From Cells, to Systems, to Sensory-Motor Control

Attention control is a fundamental cognitive function that enables individuals to sustain focus, shift attention flexibly, and filter distractions in a goal-directed manner. The locus coeruleus-norepinephrine (LC-NE) system plays a pivotal role in this process by dynamically regulating arousal, prioritizing salient stimuli, and optimizing cognitive performance. This review synthesizes evidence from molecular, cellular, systems, cognitive neuroscience, and behavioral studies to elucidate the LC-NE system's role in attention control. We first examine the neurophysiological mechanisms of the LC, highlighting its distinct firing patterns-tonic and phasic activity-and their impact on attention. Next, we integrate findings from animal models, human neuroimaging, electrophysiology, and computational modeling, demonstrating how LC-NE activity influences sensory processing, cognitive flexibility, and executive function. We interpret these findings through the lens of three major theoretical frameworks: Adaptive Gain Theory (AGT), which describes how LC activity optimizes task engagement, the Network Reset Hypothesis (NRH), which describes how optimizes network connectivity, and the Glutamate Amplifies NE Effects (GANE) model, which explains how NE enhances neural selectivity and suppresses irrelevant signals. Collectively, the evidence underscores the LC-NE system's role in modulating the signal-to-noise ratio in cortical and subcortical circuits, thereby shaping attention and behavior. We conclude by discussing implications for individual differences, age-related cognitive decline, and emphasizing the need for interdisciplinary research that integrates emerging technologies to further unravel the complexities of LC function.

Targeting Neural Oscillations for Cognitive Enhancement in Alzheimer's Disease

Alzheimer's disease (AD), the most prevalent form of dementia, is marked by progressive cognitive decline, affecting memory, language, orientation, and behavior. Pathological hallmarks include extracellular amyloid plaques and intracellular tau tangles, which disrupt synaptic function and connectivity. Neural oscillations, the rhythmic synchronization of neuronal activity across frequency bands, are integral to cognitive processes but become dysregulated in AD, contributing to network dysfunction and memory impairments. Targeting these oscillations has emerged as a promising therapeutic strategy. Preclinical studies have demonstrated that specific frequency modulations can restore oscillatory balance, improve synaptic plasticity, and reduce amyloid and tau pathology. In animal models, interventions, such as gamma entrainment using sensory stimulation and transcranial alternating current stimulation (tACS), have shown efficacy in enhancing memory function and modulating neuroinflammatory responses. Clinical trials have reported promising cognitive improvements with repetitive transcranial magnetic stimulation (rTMS) and deep brain stimulation (DBS), particularly when targeting key hubs in memory-related networks, such as the default mode network (DMN) and frontal-parietal network. Moreover, gamma-tACS has been linked to increased cholinergic activity and enhanced network connectivity, which are correlated with improved cognitive outcomes in AD patients. Despite these advancements, challenges remain in optimizing stimulation parameters, individualizing treatment protocols, and understanding long-term effects. Emerging approaches, including transcranial pulse stimulation (TPS) and closed-loop adaptive neuromodulation, hold promise for refining therapeutic strategies. Integrating neuromodulation with pharmacological and lifestyle interventions may maximize cognitive benefits. Continued interdisciplinary efforts are essential to refine these approaches and translate them into clinical practice, advancing the potential for neural oscillation-based therapies in AD.

Event-Related Brain Oscillations Changes in Major Depressive Disorder Patients During Emotional Face Recognition

Major depressive disorder (MDD) is a disorder with multiple impairments, among which emotion disorder is the most main one. Nowadays, evoked activity (EA), such as event-related potential (ERP), has mostly been studied for MDD, but induced activity (IA) analysis is still lacking. In this paper, EA, IA and event-related spectral perturbation (ERSP) were studied and compared between MDD patients and healthy controls (HC). Electroencephalogram (EEG) of 26 healthy controls and 21 MDD patients were recorded during three different facial expression (positive, neutral, negative) recognition tasks. Two phases of task execution process were studied, the early stage (0-200 ms after stimuli), and the late stage (200-500 ms after stimuli). ERSP, EA index and IA index of θ (4-7 Hz), α (8-13 Hz) and β (14-30 Hz) frequency bands were calculated and compared between two groups for two phases, respectively. In the early stage, the results indicated a decreased IA in α band in MDD compared to HC in frontal and parieto-occipital areas during neutral and negative face recognition. During the late stage, reduced IA and lower ERSP were also observed in α band in frontal and parieto-occipital areas in MDD during neutral and negative face recognition. Moreover, IA in θ band in MDD was lower than HC during negative face recognition. The findings reflected the abnormality of negative emotion processing in MDD, which could help to interpret the neural mechanism of depression.

Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice

We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. Our analysis revealed significant variances in these complexities, not only within the specific theta and delta bands but across a wide frequency spectrum. Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. This integrated model significantly enhances the accuracy of sleep stage identification, achieving an accuracy of 97.4-98.1% for novel wild-type mice, on par with the agreement level among human scorers (97.3-97.8%). The efficacy of SAC was validated through tests conducted on wild-type mice, and it demonstrated remarkable success in identifying sleep architecture abnormalities in narcoleptic mice as well. This approach not only facilitates automated scoring of sleep/wakefulness states but also holds the potential to uncover detailed physiological insights, thereby advancing EEG-based sleep research.

Spectral tuning and after-effects in neural entrainment

Neural entrainment has become a popular technique to non-invasively manipulate brain rhythms via external, periodic stimulation. However, there is still debate regarding its underlying mechanisms and effects on brain activity. Here, we used EEG recordings during a visual entrainment paradigm to assess characteristic changes in the spectral content of EEG signals due to entrainment. Our results demonstrate that entrainment not only increases synchrony between neural oscillations and the entraining stimulus but also elicits previously unreported spectral tuning effects and long-lasting after-effects. These findings offer compelling evidence for the presence of dedicated, flexible, and adaptive mechanisms for neural entrainment, which may have key roles in adjusting the sensitivity and dynamic range of brain oscillators in response to environmental temporal structures.

Transcranial magnetic stimulation effects support an oscillatory model of ERP genesis

Whether prestimulus oscillatory brain activity contributes to the generation of post-stimulus-evoked neural responses has long been debated, but findings remain inconclusive. We first investigated the hypothesized relationship via EEG recordings during a perceptual task with this correlational evidence causally probed subsequently by means of online rhythmic transcranial magnetic stimulation. Both approaches revealed a close link between prestimulus individual alpha frequency (IAF) and P1 latency, with faster IAF being related to shorter latencies, best explained via phase-reset mechanisms. Moreover, prestimulus alpha amplitude predicted P3 size, best explained via additive (correlational and causal evidence) and baseline shift mechanisms (correlational evidence), each with distinct prestimulus alpha contributors. Finally, in terms of performance, faster prestimulus IAF and shorter P1 latencies were both associated with higher task accuracy, while lower prestimulus alpha amplitudes and higher P3 amplitudes were associated with higher confidence ratings. Our results are in favor of the oscillatory model of ERP genesis and modulation, shedding new light on the mechanistic relationship between prestimulus oscillations and functionally relevant evoked components.

Just a phase? Causal probing reveals spurious phasic dependence of sustained attention

For over a decade, electrophysiological studies have reported correlations between attention / perception and the phase of spontaneous brain oscillations. To date, these findings have been interpreted as evidence that the brain uses neural oscillations to sample and predict upcoming stimuli. Yet, evidence from simulations have shown that analysis artefacts could also lead to spurious pre-stimulus oscillations that appear to predict future brain responses. To address this discrepancy, we conducted an experiment in which visual stimuli were presented in time to specific phases of spontaneous alpha and theta oscillations. This allowed us to causally probe the role of ongoing neural activity in visual processing independent of the stimulus-evoked dynamics. Our findings did not support a causal link between spontaneous alpha / theta rhythms and behaviour. However, spurious correlations between theta phase and behaviour emerged offline using gold-standard time-frequency analyses. These findings are a reminder that care should be taken when inferring causal relationships between neural activity and behaviour using acausal analysis methods.

A Novel Hybrid Brain-Computer Interface Combining the Illusion-Induced VEP and SSVEP

Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated through two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.

Spectral and phase-coherence correlates of impaired auditory mismatch negativity (MMN) in schizophrenia: A MEG study

BACKGROUND

Reduced auditory mismatch negativity (MMN) is robustly impaired in schizophrenia. However, mechanisms underlying dysfunctional MMN generation remain incompletely understood. This study aimed to examine the role of evoked spectral power and phase-coherence towards deviance detection and its impairments in schizophrenia.

METHODS

Magnetoencephalography data was collected in 16 male schizophrenia patients and 16 male control participants during an auditory MMN paradigm. Analyses of event-related fields (ERF), spectral power and inter-trial phase-coherence (ITPC) focused on Heschl's gyrus, superior temporal gyrus, inferior/medial frontal gyrus and thalamus.

RESULTS

MMNm ERF amplitudes were reduced in patients in temporal, frontal and subcortical regions, accompanied by decreased theta-band responses, as well as by a diminished gamma-band response in auditory cortex. At theta/alpha frequencies, ITPC to deviant tones was reduced in patients in frontal cortex and thalamus. Patients were also characterized by aberrant responses to standard tones as indexed by reduced theta-/alpha-band power and ITPC in temporal and frontal regions. Moreover, stimulus-specific adaptation was decreased at theta/alpha frequencies in left temporal regions, which correlated with reduced MMNm spectral power and ERF amplitude. Finally, phase-reset of alpha-oscillations after deviant tones in left thalamus was impaired, which correlated with impaired MMNm generation in auditory cortex. Importantly, both non-rhythmic and rhythmic components of spectral activity contributed to the MMNm response.

CONCLUSIONS

Our data indicate that deficits in theta-/alpha- and gamma-band activity in cortical and subcortical regions as well as impaired spectral responses to standard sounds could constitute potential mechanisms for dysfunctional MMN generation in schizophrenia, providing a novel perspective towards MMN deficits in the disorder.

The effect of acute exercise on attentional control and theta power in young adults

Exercise has a profound impact on one's health, and it is becoming increasingly accepted that exercise also benefits cognitive functioning. Yet, the neural mechanism for which cognitive enhancement occurs is less understood. Therefore, the purpose of our study was to experimentally test whether an acute exercise activity was able to increase theta power and behavioral performance during an executive functioning attentional control task. Participants were randomly assigned to either a stationary-bike exercise or a resting control condition. Thereafter, they completed the Eriksen flanker task, and most participants completed this while EEG data were recorded. From the flanker task data, we demonstrated an interaction effect from both accuracy and reaction time measurements. Importantly, the exercise group was more accurate than the control group in incongruent trials. From the EEG data, theta power was overall higher in the exercise group, especially during the congruent trials, compared to controls. Our results add to the limited but growing body of research that suggests acute exercise produces a general increase in theta power, which in turn may play a role in enhancing cognitive performance. These results, combined with previous research, could have widespread implications in multiple settings such as in the investigation of a biomarker of physical fitness, neurorehabilitation, and in education.

Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

Modulation of rhythmic visual stimulation on left-right attentional asymmetry

The rhythmic visual stimulation (RVS)-induced oscillatory brain responses, namely steady-state visual evoked potentials (SSVEPs), have been widely used as a biomarker in studies of neural processing based on the assumption that they would not affect cognition. However, recent studies have suggested that the generation of SSVEPs might be attributed to neural entrainment and thus could impact brain functions. But their neural and behavioral effects are yet to be explored. No study has reported the SSVEP influence on functional cerebral asymmetry (FCA). We propose a novel lateralized visual discrimination paradigm to test the SSVEP effects on visuospatial selective attention by FCA analyses. Thirty-eight participants covertly shifted their attention to a target triangle appearing in either the lower-left or -right visual field (LVF or RVF), and judged its orientation. Meanwhile, participants were exposed to a series of task-independent RVSs at different frequencies, including 0 (no RVS), 10, 15, and 40-Hz. As a result, it showed that target discrimination accuracy and reaction time (RT) varied significantly across RVS frequency. Furthermore, attentional asymmetries differed for the 40-Hz condition relative to the 10-Hz condition as indexed by enhanced RT bias to the right visual field, and larger Pd EEG component for attentional suppression. Our results demonstrated that RVSs had frequency-specific effects on left-right attentional asymmetries in both behavior and neural activities. These findings provided new insights into the functional role of SSVEP on FCAs.

A static paradigm based on illusion-induced VEP for brain-computer interfaces

OBJECTIVE

Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential (IVEP) is proposed for BCIs to enhance visual experience and practicality.

APPROACH

This study explored the responses to baseline and illusion tasks including the Rotating-Tilted-Lines (RTL) illusion and Rotating-Snakes (RS) illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials (ERPs) and amplitude modulation of evoked oscillatory responses.

MAIN RESULTS

The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis (TRCA) was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s.

SIGNIFICANCE

The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.

Neurocognitive Dynamics of Prosodic Salience over Semantics during Explicit and Implicit Processing of Basic Emotions in Spoken Words

How language mediates emotional perception and experience is poorly understood. The present event-related potential (ERP) study examined the explicit and implicit processing of emotional speech to differentiate the relative influences of communication channel, emotion category and task type in the prosodic salience effect. Thirty participants (15 women) were presented with spoken words denoting happiness, sadness and neutrality in either the prosodic or semantic channel. They were asked to judge the emotional content (explicit task) and speakers' gender (implicit task) of the stimuli. Results indicated that emotional prosody (relative to semantics) triggered larger N100, P200 and N400 amplitudes with greater delta, theta and alpha inter-trial phase coherence (ITPC) and event-related spectral perturbation (ERSP) values in the corresponding early time windows, and continued to produce larger LPC amplitudes and faster responses during late stages of higher-order cognitive processing. The relative salience of prosodic and semantics was modulated by emotion and task, though such modulatory effects varied across different processing stages. The prosodic salience effect was reduced for sadness processing and in the implicit task during early auditory processing and decision-making but reduced for happiness processing in the explicit task during conscious emotion processing. Additionally, across-trial synchronization of delta, theta and alpha bands predicted the ERP components with higher ITPC and ERSP values significantly associated with stronger N100, P200, N400 and LPC enhancement. These findings reveal the neurocognitive dynamics of emotional speech processing with prosodic salience tied to stage-dependent emotion- and task-specific effects, which can reveal insights into understanding language and emotion processing from cross-linguistic/cultural and clinical perspectives.

Comparison of event-related modulation index and traditional methods for evaluating phase-amplitude coupling using simulated brain signals

The investigation of brain oscillations and connectivity has become an important topic in the recent decade. There are several types of interactions between neuronal oscillations, and one of the most interesting among these interactions is phase-amplitude coupling (PAC). Several methods have been proposed to measure the strength of PAC, including the phase-locking value, circular-linear correlation, and modulation index. In the current study, we compared these traditional PAC methods with simulated electroencephalogram signals. Further, to assess the PAC value at each time point, we also compared two recently established methods, event-related phase-locking value and event-related circular-linear correlation, with our newly proposed event-related modulation index (ERMI). Results indicated that the ERMI has better temporal resolution and is more tolerant to noise than the other two event-related methods, suggesting the advantages of utilizing ERMI in evaluating the strength of PAC within a brain region.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Infra-low frequency neurofeedback and insomnia as a model of CNS dysregulation

This paper will review what is conventionally known of sleep homeostasis and focus on insomnia as a primary manifestation of brain dysregulation, whether as a solitary symptom or as part of a larger syndrome such as post-traumatic stress disorder, PTSD. It will discuss in brief behavioral/mindfulness treatments that have been used to treat neurologic diseases, as this is germane to the phenomenology of neurofeedback (NF). It will explore how neurofeedback may work at the subconscious level and cover the current clinical experience of the effectiveness of this technique in the treatment of insomnia. It will conclude with a case presentation.

Time-frequency signatures evoked by single-pulse deep brain stimulation to the subcallosal cingulate

Precision targeting of specific white matter bundles that traverse the subcallosal cingulate (SCC) has been linked to efficacy of deep brain stimulation (DBS) for treatment resistant depression (TRD). Methods to confirm optimal target engagement in this heterogenous region are now critical to establish an objective treatment protocol. As yet unexamined are the time-frequency features of the SCC evoked potential (SCC-EP), including spectral power and phase-clustering. We examined these spectral features—evoked power and phase clustering—in a sample of TRD patients (n = 8) with implanted SCC stimulators. Electroencephalogram (EEG) was recorded during wakeful rest. Location of electrical stimulation in the SCC target region was the experimental manipulation. EEG was analyzed at the surface level with an average reference for a cluster of frontal sensors and at a time window identified by prior study (50–150 ms). Morlet wavelets generated indices of evoked power and inter-trial phase clustering. Enhanced phase clustering at theta frequency (4–7 Hz) was observed in every subject and was significantly correlated with SCC-EP magnitude, but only during left SCC stimulation. Stimulation to dorsal SCC evinced stronger phase clustering than ventral SCC. There was a weak correlation between phase clustering and white matter density. An increase in evoked delta power (2–4 Hz) was also coincident with SCC-EP, but was less consistent across participants. DBS evoked time-frequency features index mm-scale changes to the location of stimulation in the SCC target region and correlate with structural characteristics implicated in treatment optimization. Results also imply a shared generative mechanism (inter-trial phase clustering) between evoked potentials evinced by electrical stimulation and evoked potentials evinced by auditory/visual stimuli and behavioral tasks. Understanding how current injection impacts downstream cortical activity is essential to building new technologies that adapt treatment parameters to individual differences in neurophysiology.

Treatment effects on event-related EEG potentials and oscillations in Alzheimer's disease

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

Neural Bases of Age-Related Sensorimotor Slowing in the Upper and Lower Limbs

With advanced age, there is a loss of reaction speed that may contribute to an increased risk of tripping and falling. Avoiding falls and injuries requires awareness of the threat, followed by selection and execution of the appropriate motor response. Using event-related potentials (ERPs) and a simple visual reaction task (RT), the goal of our study was to distinguish sensory and motor processing in the upper- and lower-limbs while attempting to uncover the main cause of age-related behavioral slowing. Strength (amplitudes) as well as timing and speed (latencies) of various stages of stimulus- and motor-related processing were analyzed in 48 healthy individuals (young adults, n = 24, mean age = 34 years; older adults, n = 24, mean age = 67 years). The behavioral results showed a significant age-related slowing, where the younger compared to older adults exhibited shorter RTs for the upper- (222 vs. 255 ms; p = 0.006, respectively) and the lower limb (257 vs. 274 ms; p = 0.048, respectively) as well as lower variability in both modalities (p = 0.001). Using ERP indices, age-related slowing of visual stimulus processing was characterized by overall larger amplitudes with delayed latencies of endogenous potentials in older compared with younger adults. While no differences were found in the P1 component, the later components of recorded potentials for visual stimuli processing were most affected by age. This was characterized by increased N1 and P2 amplitudes and delayed P2 latencies in both upper and lower extremities. The analysis of motor-related cortical potentials (MRCPs) revealed stronger MRCP amplitude for upper- and a non-significant trend for lower limbs in older adults. The MRCP amplitude was smaller and peaked closer to the actual motor response for the upper- than for the lower limb in both age groups. There were longer MRCP onset latencies for lower- compared to upper-limb in younger adults, and a non-significant trend was seen in older adults. Multiple regression analyses showed that the onset of the MRCP peak consistently predicted reaction time across both age groups and limbs tested. However, MRCP rise time and P2 latency were also significant predictors of simple reaction time, but only in older adults and only for the upper limbs. Our study suggests that motor cortical processes contribute most strongly to the slowing of simple reaction time in advanced age. However, late-stage cortical processing related to sensory stimuli also appears to play a role in upper limb responses in the elderly. This process most likely reflects less efficient recruitment of neuronal resources required for the upper and lower extremity response task in older adults.

Event-Related Phase Synchronization Propagates Rapidly across Human Ventral Visual Cortex

Visual inputs to early visual cortex integrate with semantic, linguistic and memory inputs in higher visual cortex, in a manner that is rapid and accurate, and enables complex computations such as face recognition and word reading. This implies the existence of fundamental organizational principles that enable such efficiency. To elaborate on this, we performed intracranial recordings in 82 individuals while they performed tasks of varying visual and cognitive complexity. We discovered that visual inputs induce highly organized posterior-to-anterior propagating patterns of phase modulation across the ventral occipitotemporal cortex. At individual electrodes there was a stereotyped temporal pattern of phase progression following both stimulus onset and offset, consistent across trials and tasks. The phase of low frequency activity in anterior regions was predicted by the prior phase in posterior cortical regions. This spatiotemporal propagation of phase likely serves as a feed-forward organizational influence enabling the integration of information across the ventral visual stream. This phase modulation manifests as the early components of the event related potential; one of the most commonly used measures in human electrophysiology. These findings illuminate fundamental organizational principles of the higher order visual system that enable the rapid recognition and characterization of a variety of inputs.

Long-Latency Event-Related Potentials (300-1000 ms) of the Visual Insight

The line of insight research methods that have high temporal and surface resolution is not large—these are EEGs, EPs, and fMRI, as well as their combinations and various options for assessing temporal events of random understanding. The objective of this research was to study the classification of insight for visual illusory images consisting of several objects simultaneously according to the analysis of early, middle, late, and ultra-late components (up to 1000 ms) of event-related potentials (ERPs). ERP research on 42 healthy subjects (men) aged 20–28 years was performed. The stimuli were a line of visual images with an incomplete set of signs, as well as images-illusions, which, with different perceptions, represent different images. The results showed the similarity of the tests to correct recognition of fragments of unrecognition and double images. At the intermediate stage of perception (100–200 ms), in both cases, the activity of the central and frontal cortex decreased, mainly in the left hemisphere. At the later stages of information processing (300–500 ms), the temporal-parietal and occipital brain parts on the right were activated, with the difference that when double objects were perceived, this process expanded to 700–800 ms with the activation of the central and occipital fields of the right hemisphere. Outcomes allowed discussing two possible options for actualizing the mechanisms of long-term memory that ensure the formation of insight—the simultaneous perception of images as part of an illusion. The first of them is associated with the inhibition of the frontal cortex at the stage of synthesis of information flows, with the subsequent activation of the occipital brain parts. The second variant is traditional and manifests itself in the activation of the frontal brain areas, with the subsequent excitation of all brain fields by the mechanisms of exhaustive search.

Alteration of Neural Network Activity With Aging Focusing on Temporal Complexity and Functional Connectivity Within Electroencephalography

With the aging process, brain functions, such as attention, memory, and cognitive functions, degrade over time. In a super-aging society, the alteration of neural activity owing to aging is considered crucial for interventions for the prevention of brain dysfunction. The complexity of temporal neural fluctuations with temporal scale dependency plays an important role in optimal brain information processing, such as perception and thinking. Complexity analysis is a useful approach for detecting cortical alteration in healthy individuals, as well as in pathological conditions, such as senile psychiatric disorders, resulting in changes in neural activity interactions among a wide range of brain regions. Multi-fractal (MF) and multi-scale entropy (MSE) analyses are known methods for capturing the complexity of temporal scale dependency of neural activity in the brain. MF and MSE analyses exhibit high accuracy in detecting changes in neural activity and are superior with regard to complexity detection when compared with other methods. In addition to complex temporal fluctuations, functional connectivity reflects the integration of information of brain processes in each region, described as mutual interactions of neural activity among brain regions. Thus, we hypothesized that the complementary relationship between functional connectivity and complexity could improve the ability to detect the alteration of spatiotemporal patterns observed on electroencephalography (EEG) with respect to aging. To prove this hypothesis, this study investigated the relationship between the complexity of neural activity and functional connectivity in aging based on EEG findings. Concretely, MF and MSE analyses were performed to evaluate the temporal complexity profiles, and phase lag index analyses assessing the unique profile of functional connectivity were performed based on the EEGs conducted for young and older participants. Subsequently, these profiles were combined through machine learning. We found that the complementary relationship between complexity and functional connectivity improves the classification accuracy among aging participants. Thus, the outcome of this study could be beneficial in formulating interventions for the prevention of age-related brain dysfunction.

Phonetic Encoding Contributes to the Processing of Linguistic Prosody at the Word Level: Cross-Linguistic Evidence From Event-Related Potentials

PURPOSE

This study aimed to examine whether abstract knowledge of word-level linguistic prosody is independent of or integrated with phonetic knowledge.

METHOD

Event-related potential (ERP) responses were measured from 18 adult listeners while they listened to native and nonnative word-level prosody in speech and in nonspeech. The prosodic phonology (speech) conditions included disyllabic pseudowords spoken in Chinese and in English matched for syllabic structure, duration, and intensity. The prosodic acoustic (nonspeech) conditions were hummed versions of the speech stimuli, which eliminated the phonetic content while preserving the acoustic prosodic features.

RESULTS

We observed language-specific effects on the ERP that native stimuli elicited larger late negative response (LNR) amplitude than nonnative stimuli in the prosodic phonology conditions. However, no such effect was observed in the phoneme-free prosodic acoustic control conditions.

CONCLUSIONS

The results support the integration view that word-level linguistic prosody likely relies on the phonetic content where the acoustic cues embedded in. It remains to be examined whether the LNR may serve as a neural signature for language-specific processing of prosodic phonology beyond auditory processing of the critical acoustic cues at the suprasyllabic level.

Alpha suppression indexes a spotlight of visual-spatial attention that can shine on both perceptual and memory representations

Although researchers have been recording the human electroencephalogram (EEG) for almost a century, we still do not completely understand what cognitive processes are measured by the activity of different frequency bands. The 8- to 12-Hz activity in the alpha band has long been a focus of this research, but our understanding of its links to cognitive mechanisms has been rapidly evolving recently. Here, we review and discuss the existing evidence for two competing perspectives about alpha activity. One view proposes that the suppression of alpha-band power following the onset of a stimulus array measures attentional selection. The competing view is that this same activity measures the buffering of the task-relevant representations in working memory. We conclude that alpha-band activity following the presentation of stimuli appears to be due to the operation of an attentional selection mechanism, with characteristics that mirror the classic views of attention as selecting both perceptual inputs and representations already stored in memory.

Coherent theta activity in the medial and orbital frontal cortices encodes reward value

This study examined how the medial frontal (MFC) and orbital frontal (OFC) cortices process reward information. We simultaneously recorded local field potentials in the two areas as rats consumed liquid sucrose rewards. Both areas exhibited a 4-8 Hz 'theta' rhythm that was phase-locked to the lick cycle. The rhythm tracked shifts in sucrose concentrations and fluid volumes, demonstrating that it is sensitive to differences in reward magnitude. The coupling between the rhythm and licking was stronger in MFC than OFC and varied with response vigor and absolute reward value in the MFC. Spectral analysis revealed zero-lag coherence between the cortical areas, and found evidence for a directionality of the rhythm, with MFC leading OFC. Our findings suggest that consummatory behavior generates simultaneous theta range activity in the MFC and OFC that encodes the value of consumed fluids, with the MFC having a top-down role in the control of consumption.

Prediction of cortical theta oscillations in humans for phase-locked visual stimulation

BACKGROUND

The timing of an event within an oscillatory phase is considered to be one of the key strategies used by the brain to code and process neural information. Whereas existing methods of studying this phenomenon are chiefly based on retrospective analysis of electroencephalography (EEG) data, we now present a method to study it prospectively. New method: We present a system that allows for the delivery of visual stimuli at a specific phase of the cortical theta oscillation by fitting a sine to raw surface EEG data to estimate and predict the phase. One noteworthy feature of the method is that it can minimize potentially confounding effects of previous trials by using only a short sequence of past data.

RESULTS

In a trial with 10 human participants we achieved a significant phase locking with an inter-trial phase coherence of 0.39. We demonstrated successful phase locking on synthetic signals with a signal-to-noise ratio of less than - 20 dB. Comparison with existing method(s): We compared the new method to an autoregressive method published in the literature and found the new method was superior in mean phase offset, circular standard deviation, and prediction latency.

CONCLUSIONS

By fitting sine waves to raw EEG traces, we locked visual stimuli to arbitrary phases within the theta oscillatory cycle of healthy humans.

Identification of Electroencephalogram Signals in Alzheimer's Disease by Multifractal and Multiscale Entropy Analysis

Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are emphasized. As a diagnostic index for AD patients, evaluating the complexity of the dependence of the electroencephalography (EEG) signal on the temporal scale of Alzheimer's disease (AD) patients is effective. Multiscale entropy analysis and multifractal analysis have been performed individually, and their usefulness as diagnostic indicators has been confirmed, but the complemental relationship between these analyses, which may enhance diagnostic accuracy, has not been investigated. We hypothesize that combining multiscale entropy and fractal analyses may add another dimension to understanding the alteration of EEG dynamics in AD. In this study, we performed both multiscale entropy and multifractal analyses on EEGs from AD patients and healthy subjects. We found that the classification accuracy was improved using both techniques. These findings suggest that the use of multiscale entropy analysis and multifractal analysis may lead to the development of AD diagnostic tools.

Neural coding of formant-exaggerated speech and nonspeech in children with and without autism spectrum disorders

The presence of vowel exaggeration in infant-directed speech (IDS) may adapt to the age-appropriate demands in speech and language acquisition. Previous studies have provided behavioral evidence of atypical auditory processing towards IDS in children with autism spectrum disorders (ASD), while the underlying neurophysiological mechanisms remain unknown. This event-related potential (ERP) study investigated the neural coding of formant-exaggerated speech and nonspeech in 24 4- to 11-year-old children with ASD and 24 typically-developing (TD) peers. The EEG data were recorded using an alternating block design, in which each stimulus type (exaggerated/non-exaggerated sound) was presented with equal probability. ERP waveform analysis revealed an enhanced P1 for vowel formant exaggeration in the TD group but not in the ASD group. This speech-specific atypical processing in ASD was not found for the nonspeech stimuli which showed similar P1 enhancement in both ASD and TD groups. Moreover, the time-frequency analysis indicated that children with ASD showed differences in neural synchronization in the delta-theta bands for processing acoustic formant changes embedded in nonspeech. Collectively, the results add substantiating neurophysiological evidence (i.e., a lack of neural enhancement effect of vowel exaggeration) for atypical auditory processing of IDS in children with ASD, which may exert a negative effect on phonetic encoding and language learning. LAY SUMMARY: Atypical responses to motherese might act as a potential early marker of risk for children with ASD. This study investigated the neural responses to such socially relevant stimuli in the ASD brain, and the results suggested a lack of neural enhancement responding to the motherese even in individuals without intellectual disability.

Critical Dynamics in Spontaneous Resting-State Oscillations Are Associated With the Attention-Related P300 ERP in a Go/Nogo Task

Sustained attention is the ability to continually concentrate on task-relevant information, even in the presence of distraction. Understanding the neural mechanisms underlying this ability is critical for comprehending attentional processes as well as neuropsychiatric disorders characterized by attentional deficits, such as attention deficit hyperactivity disorder (ADHD). In this study, we aimed to investigate how trait-like critical oscillations during rest relate to the P300 evoked potential-a biomarker commonly used to assess attentional deficits. We measured long-range temporal correlations (LRTC) in resting-state EEG oscillations as index for criticality of the signal. In addition, the attentional performance of the subjects was assessed as reaction time variability (RTV) in a continuous performance task following an oddball paradigm. P300 amplitude and latencies were obtained from EEG recordings during this task. We found that, after controlling for individual variability in task performance, LRTC were positively associated with P300 amplitudes but not latencies. In line with previous findings, good performance in the sustained attention task was related to higher P300 amplitudes and earlier peak latencies. Unexpectedly, we observed a positive relationship between LRTC in ongoing oscillations during rest and RTV, indicating that greater criticality in brain oscillations during rest relates to worse task performance. In summary, our results show that resting-state neuronal activity, which operates near a critical state, relates to the generation of higher P300 amplitudes. Brain dynamics close to criticality potentially foster a computationally advantageous state which promotes the ability to generate higher event-related potential (ERP) amplitudes.

Theta Phase Synchrony Is Sensitive to Corollary Discharge Abnormalities in Early Illness Schizophrenia but Not in the Psychosis Risk Syndrome

BACKGROUND

Prior studies have shown that the auditory N1 event-related potential component elicited by self-generated vocalizations is reduced relative to played back vocalizations, putatively reflecting a corollary discharge mechanism. Schizophrenia patients and psychosis risk syndrome (PRS) youth show deficient N1 suppression during vocalization, consistent with corollary discharge dysfunction. Because N1 is an admixture of theta (4-7 Hz) power and phase synchrony, we examined their contributions to N1 suppression during vocalization, as well as their sensitivity, relative to N1, to corollary discharge dysfunction in schizophrenia and PRS individuals.

METHODS

Theta phase and power values were extracted from electroencephalography data acquired from PRS youth (n = 71), early illness schizophrenia patients (ESZ; n = 84), and healthy controls (HCs; n = 103) as they said "ah" (Talk) and then listened to the playback of their vocalizations (Listen). A principal component analysis extracted theta intertrial coherence (ITC; phase consistency) and event-related spectral power, peaking in the N1 latency range. Talk-Listen suppression scores were analyzed.

RESULTS

Talk-Listen suppression was greater for theta ITC (Cohen's d = 1.46) than for N1 in HC (d = 0.63). Both were deficient in ESZ, but only N1 suppression was deficient in PRS. When deprived of variance shared with theta ITC suppression, N1 suppression no longer differentiated ESZ and PRS individuals from HC. Deficits in theta ITC suppression were correlated with delusions (P = .007) in ESZ. Theta power suppression did not differentiate groups.

CONCLUSIONS

Theta ITC-suppression during vocalization is a more sensitive index of corollary discharge-mediated auditory cortical suppression than N1 suppression and is more sensitive to corollary discharge dysfunction in ESZ than in PRS individuals.

Language Dysfunction in Schizophrenia: Assessing Neural Tracking to Characterize the Underlying Disorder(s)?

Deficits in language production and comprehension are characteristic of schizophrenia. To date, it remains unclear whether these deficits arise from dysfunctional linguistic knowledge, or dysfunctional predictions derived from the linguistic context. Alternatively, the deficits could be a result of dysfunctional neural tracking of auditory information resulting in decreased auditory information fidelity and even distorted information. Here, we discuss possible ways for clinical neuroscientists to employ neural tracking methodology to independently characterize deficiencies on the auditory-sensory and abstract linguistic levels. This might lead to a mechanistic understanding of the deficits underlying language related disorder(s) in schizophrenia. We propose to combine naturalistic stimulation, measures of speech-brain synchronization, and computational modeling of abstract linguistic knowledge and predictions. These independent but likely interacting assessments may be exploited for an objective and differential diagnosis of schizophrenia, as well as a better understanding of the disorder on the functional level-illustrating the potential of neural tracking methodology as translational tool in a range of psychotic populations.

The phase of Theta oscillations modulates successful memory formation at encoding

Several studies have shown that attention and perception can depend upon the phase of ongoing neural oscillations at stimulus onset. Here, we extend this idea to the memory domain. We tested the hypothesis that ongoing fluctuations in neural activity impact memory encoding in two experiments using a picture paired-associates task in order to gauge episodic memory performance. Experiment 1 was behavioural only and capitalized on the principle of phase resetting. We tested if subsequent memory performance fluctuates rhythmically, time-locked to a resetting cue presented before the to-be-remembered pairs at different time intervals. We found an indication that behavioural performance was periodically and selectively modulated at Theta frequency (~4 Hz). In Experiment 2, we focused on pre-stimulus ongoing activity using scalp EEG while participants performed a paired-associates task. The pre-registered analysis, using large electrode clusters and generic Theta and Alpha spectral ranges, returned null results of the pre-stimulus phase-behaviour correlation. However, as expected from prior literature, we found that variations in stimulus-related Theta-power predicted subsequent memory performance. Therefore, we used this post-stimulus effect in Theta power to guide a post-hoc pre-stimulus phase analysis in terms of scalp and frequency of interest. This analysis returned a correlation between the pre-stimulus Theta phase and subsequent memory. Altogether, these results suggest that pre-stimulus Theta activity at encoding may impact later memory performance.

Brain oscillations evoked by sound motion

The present study investigates the event-related oscillations underlying the motion-onset response (MOR) evoked by sounds moving at different velocities. EEG was recorded for stationary sounds and for three patterns of sound motion produced by changes in interaural time differences. We explored the effect of motion velocity on the MOR potential, and also on the event-related spectral perturbation (ERSP) and inter-trial phase coherence (ITC) calculated from the time-frequency decomposition of EEG signals. The phase coherence of slow oscillations increased with an increase in motion velocity similarly to the magnitude of cN1 and cP2 components of the MOR response. The delta-to-alpha inter-trial spectral power remained at the same level up to, but not including, the highest velocity, suggesting that gradual spatial changes within the sound did not induce non-coherent activity. Conversely, the abrupt sound displacement induced theta-alpha oscillations which had low phase consistency. The findings suggest that the MOR potential could be mainly generated by the phase resetting of slow oscillations, and the degree of phase coherence may be considered as a neurophysiological indicator of sound motion processing.

Who said what? The effects of speech tempo on target detection and information extraction in a multi-talker situation: An ERP and functional connectivity study

People with normal hearing can usually follow one of the several concurrent speakers. Speech tempo affects both the separation of concurrent speech streams and information extraction from them. The current study varied the tempo of two concurrent speech streams to investigate these processes in a multi-talker situation. Listeners performed a target-detection and a content-tracking task, while target-related ERPs and functional brain networks sensitive to speech tempo were extracted from the EEG signal. At slower than normal speech tempo, building the two streams required longer processing times, and possibly the utilization of higher-order, for example, syntactic and semantic cues. The observed longer reaction times and higher connectivity strength in a theta band network associated with frontal control over auditory/speech processing are compatible with this notion. With increasing tempo, target detection performance decreased and the N2b and the P3b amplitudes increased. These data suggest an increased need for strictly allocating target-detection-related resources at higher tempo. This was also reflected by the observed increase in the strength of gamma-band networks within and between frontal, temporal, and cingular areas. At the fastest tested speech tempo, there was a sharp drop in recognition memory performance, while target detection performance increased compared to the normal speech tempo. This was accompanied by a significant increase in the strength of a low alpha network associated with the suppression of task-irrelevant speech. These results suggest that participants prioritized the immediate target detection task over the continuous content tracking, likely due to some capacity limit reached the fastest speech tempo.

Deterministic characteristics of spontaneous activity detected by multi-fractal analysis in a spiking neural network with long-tailed distributions of synaptic weights

Cortical neural networks maintain autonomous electrical activity called spontaneous activity that represents the brain's dynamic internal state even in the absence of sensory stimuli. The spatio-temporal complexity of spontaneous activity is strongly related to perceptual, learning, and cognitive brain functions; multi-fractal analysis can be utilized to evaluate the complexity of spontaneous activity. Recent studies have shown that the deterministic dynamic behavior of spontaneous activity especially reflects the topological neural network characteristics and changes of neural network structures. However, it remains unclear whether multi-fractal analysis, recently widely utilized for neural activity, is effective for detecting the complexity of the deterministic dynamic process. To verify this point, we focused on the log-normal distribution of excitatory postsynaptic potentials (EPSPs) to evaluate the multi-fractality of spontaneous activity in a spiking neural network with a log-normal distribution of EPSPs. We found that the spiking activities exhibited multi-fractal characteristics. Moreover, to investigate the presence of a deterministic process in the spiking activity, we conducted a surrogate data analysis against the time-series of spiking activity. The results showed that the spontaneous spiking activity included the deterministic dynamic behavior. Overall, the combination of multi-fractal analysis and surrogate data analysis can detect deterministic complex neural activity. The multi-fractal analysis of neural activity used in this study could be widely utilized for brain modeling and evaluation methods for signals obtained by neuroimaging modalities.

High Phase Synchronization in Alpha Band Activity in Older Subjects With High Creativity

Despite growing evidence that high creativity leads to mental well-being in older individuals, the neurophysiological bases of creativity remain elusive. Creativity reportedly involves multiple brain areas and their functional interconnections. In particular, functional magnetic resonance imaging (fMRI) is used to investigate the role of patterns of functional connectivity between the default network and other networks in creative activity. These interactions among networks play the role of integrating various neural processes to support creative activity and involve attention, cognitive control, and memory. The electroencephalogram (EEG) enables researchers to capture a pattern of band-specific functional connectivity, as well as moment-to-moment dynamics of brain activity; this can be accomplished even in the resting-state by exploiting the excellent temporal resolution of the EEG. Furthermore, the recent advent of functional connectivity analysis in EEG studies has focused on the phase-difference variable because of its fine spatio-temporal resolution. Therefore, we hypothesized that the combining method of EEG signals having high-temporal resolution and the phase synchronization analysis having high-spatio-temporal resolutions brings a new insight of functional connectivity regarding high creative activity of older participants. In this study, we examined the resting-state EEG signal in 20 healthy older participants and estimated functional connectivities using the phase lag index (PLI), which evaluates the phase synchronization of EEG signals. Individual creativity was assessed using the S-A creativity test in a separate session before the EEG recording. In the analysis of associations of EEG measures with the S-A test scores, the covariate effect of the intelligence quotient was evaluated. As a result, higher individual S-A scores were significantly associated with higher node degrees, defined as the average PLI of a node (electrode) across all links with the remaining nodes, across all nodes at the alpha band. A conventional power spectrum analysis revealed no significant association with S-A scores in any frequency band. Older participants with high creativity exhibited high functional connectivity even in the resting-state, irrespective of intelligence quotient, which supports the theory that creativity entails widespread brain connectivity. Thus, PLIs derived from EEG data may provide new insights into the relationship between functional connectivity and creativity in healthy older people.

PSPH-D-18-00526: Effect of a dual orexin receptor antagonist (DORA-12) on sleep and event-related oscillations in rats exposed to ethanol vapor during adolescence

RATIONALE

Sleep difficulties are one of the problems associated with adolescent binge drinking. However, the mechanisms underlying adolescent alcohol-associated sleep disturbances and potential targets for therapy remain under investigated. Orexin receptor antagonists may have therapeutic value in the treatment of insomnia, yet the use of this class of drugs in the treatment of sleep disturbances following adolescent alcohol exposure has not been studied.

OBJECTIVES

This study employed a model whereby ethanol vapor exposure occurred for 5 weeks during adolescence (AIE), and waking event-related oscillations (EROs) and EEG sleep were subsequently evaluated in young adult rats. The ability of two doses (10, 30 mg/kg PO) of a dual orexin receptor antagonist (DORA-12) to modify sleep, EEG, and EROs was investigated in AIE rats and controls.

RESULTS

Adolescent vapor exposure was found to produce a fragmentation of sleep, in young adults, that was partially ameliorated by DORA-12. DORA-12 also produced increases in delta and theta power in waking EROs recorded before sleep, and deeper sleep as indexed by increases in delta and theta power in the sleep EEG in both ethanol and control rats. Rats given DORA-12 also fell asleep faster than vehicle-treated rats as measured by a dose-dependent reduction in the latency to both the first slow wave and REM sleep episodes.

CONCLUSIONS

This study showed that DORA-12 can affect the sleep disturbance that is associated with a history of adolescent ethanol exposure and also has several other sleep-promoting effects that are equivalent in both ethanol and control rats.

Novel translational phenotypes and biomarkers for creatine transporter deficiency

Creatine transporter deficiency is a metabolic disorder characterized by intellectual disability, autistic-like behaviour and epilepsy. There is currently no cure for creatine transporter deficiency, and reliable biomarkers of translational value for monitoring disease progression and response to therapeutics are sorely lacking. Here, we found that mice lacking functional creatine transporter display a significant alteration of neural oscillations in the EEG and a severe epileptic phenotype that are recapitulated in patients with creatine transporter deficiency. In-depth examination of knockout mice for creatine transporter also revealed that a decrease in EEG theta power is predictive of the manifestation of spontaneous seizures, a frequency that is similarly affected in patients compared to healthy controls. In addition, knockout mice have a highly specific increase in haemodynamic responses in the cerebral cortex following sensory stimuli. Principal component and Random Forest analyses highlighted that these functional variables exhibit a high performance in discriminating between pathological and healthy phenotype. Overall, our findings identify novel, translational and non-invasive biomarkers for the analysis of brain function in creatine transporter deficiency, providing a very reliable protocol to longitudinally monitor the efficacy of potential therapeutic strategies in preclinical, and possibly clinical, studies.

Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease

Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among wide-spread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-and-regional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods to neurophysiological data may provide a novel understanding of the neural network processes in both healthy brains and pathological conditions.

Effect of suvorexant on event-related oscillations and EEG sleep in rats exposed to chronic intermittent ethanol vapor and protracted withdrawal

STUDY OBJECTIVES

Insomnia is a prominent complaint in patients with alcohol use disorders (AUD). However, despite the importance of sleep in the maintenance of sobriety, treatment options for sleep disturbance associated with a history of AUD are currently limited. Recent clinical trials have demonstrated that suvorexant, a dual Hct/OX receptor antagonist, normalizes sleep in patients with primary insomnia; yet, its potential for the treatment of sleep pathology associated with AUD has not been investigated in either preclinical or clinical studies.

METHODS

This study employed a model whereby ethanol vapor exposure or control conditions were administered for 8 weeks to adult rats. Waking event-related oscillations (EROs) and EEG sleep were evaluated at baseline before exposure and again following 24 hr of withdrawal from the exposure. Subsequently, the ability of vehicle (VEH) and two doses (10, 30 mg/kg IP) of suvorexant to modify EROs, sleep, and the sleep EEG was investigated.

RESULTS

After 24 hr following EtOH withdrawal, the ethanol-treated group had increases in waking ERO θ and β activity, more fragmented sleep (shorter duration and increased frequency of slow wave (SW) and rapid eye movement [REM] sleep episodes), and increased θ and β power in REM and SW sleep. Suvorexant induced a dose-dependent decrease in the latency to REM and SW sleep onsets but also produced REM and SW sleep fragmentation and increased β energy in waking EROs when compared with VEH.

CONCLUSIONS

Taken together, these studies suggest that suvorexant has overall sleep-promoting effects, but it may exacerbate some aspects of sleep and EEG pathology.

Aberrant Prestimulus Oscillations in Developmental Dyslexia Support an Underlying Attention Shifting Deficit

Developmental dyslexia (DD) impairs reading and writing acquisition in 5–10% of children, compromising schooling, academic success, and everyday adult life. DD associates with reduced phonological skills, evident from a reduced auditory mismatch negativity (MMN) in the electroencephalogram (EEG). It was argued that such phonological deficits are secondary to an underlying deficit in the shifting of attention to upcoming speech sounds. Here, we tested whether the aberrant MMN in individuals with DD is a function of EEG correlates of prestimulus attention shifting; based on prior findings, we focused prestimulus analyses on alpha-band oscillations. We administered an audio–visual oddball paradigm to school children with and without DD. Children with DD showed EEG markers of deficient attention switching (i.e., increased prestimulus alpha-band intertrial phase coherence [ITPC]) to precede and predict their reduced MMN—aberrantly increased ITPC predicted an aberrantly reduced MMN. In interaction, ITPC and MMN predicted reading abilities, such that poor readers showed both high ITPC and a reduced MMN, the reverse being true in good readers. Prestimulus ITPC may be an overlooked biomarker of deficient attention shifting in DD. The findings support the proposal that an attention shifting deficit underlies phonological deficits in DD, entailing new opportunities for targeted intervention.

Lateralization of brain responses to auditory motion: A study using single-trial analysis

The present study investigates hemispheric asymmetry of the ERPs and low-frequency oscillatory responses evoked in both hemispheres of the brain by the sound stimuli with delayed onset of motion. EEG was recorded for three patterns of sound motion produced by changes in interaural time differences. Event-related spectral perturbation (ERSP) and inter-trial phase coherence (ITC) were computed from the time-frequency decomposition of EEG signals. The participants either read books of their choice (passive listening) or indicated the sound trajectories perceived using a graphic tablet (active listening). Our goal was to find out whether the lateralization of the motion-onset response (MOR) and oscillatory responses to sound motion were more consistent with the right-hemispheric dominance, contralateral or neglect model of interhemispheric asymmetry. Apparent dominance of the right hemisphere was found only in the ERSP responses. Stronger contralaterality of the left hemisphere corresponding to the "neglect model" of asymmetry was shown by the MOR components and by the phase coherence of the delta-alpha oscillations. Velocity and attention did not change consistently the interhemispheric asymmetry of both the MOR and the oscillatory responses. Our findings demonstrate how the lateralization pattern shown by the MOR potential was interrelated with that of the motion-related single-trial measures.

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity

Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert-Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.

Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer's Disease Diagnosis

Despite growing evidence of aberrant neuronal complexity in Alzheimer's disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD.

Phase synchronization varies systematically with linguistic structure composition

Computation in neuronal assemblies is putatively reflected in the excitatory and inhibitory cycles of activation distributed throughout the brain. In speech and language processing, coordination of these cycles resulting in phase synchronization has been argued to reflect the integration of information on different timescales (e.g. segmenting acoustics signals to phonemic and syllabic representations; (Giraud and Poeppel 2012 Nat. Neurosci. 15, 511 (doi:10.1038/nn.3063)). A natural extension of this claim is that phase synchronization functions similarly to support the inference of more abstract higher-level linguistic structures (Martin 2016 Front. Psychol. 7, 120; Martin and Doumas 2017 PLoS Biol. 15, e2000663 (doi:10.1371/journal.pbio.2000663); Martin and Doumas. 2019 Curr. Opin. Behav. Sci. 29, 77-83 (doi:10.1016/j.cobeha.2019.04.008)). Hale et al. (Hale et al. 2018 Finding syntax in human encephalography with beam search. arXiv 1806.04127 (http://arxiv.org/abs/1806.04127)) showed that syntactically driven parsing decisions predict electroencephalography (EEG) responses in the time domain; here we ask whether phase synchronization in the form of either inter-trial phrase coherence or cross-frequency coupling (CFC) between high-frequency (i.e. gamma) bursts and lower-frequency carrier signals (i.e. delta, theta), changes as the linguistic structures of compositional meaning (viz., bracket completions, as denoted by the onset of words that complete phrases) accrue. We use a naturalistic story-listening EEG dataset from Hale et al. to assess the relationship between linguistic structure and phase alignment. We observe increased phase synchronization as a function of phrase counts in the delta, theta, and gamma bands, especially for function words. A more complex pattern emerged for CFC as phrase count changed, possibly related to the lack of a one-to-one mapping between 'size' of linguistic structure and frequency band-an assumption that is tacit in recent frameworks. These results emphasize the important role that phase synchronization, desynchronization, and thus, inhibition, play in the construction of compositional meaning by distributed neural networks in the brain. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.