Reference-free identification of components of checkerboard-evoked multichannel potential fields

Decoding the Preparation Stage of Target Shooting under Audiovisual Restricted Conditions: Investigating Neural Mechanisms Using Microstate Analysis

Shooting is a fine sport that is greatly influenced by mental state, and the neural activity of brain in the preparation stage of shooting has a direct influence on the level of shooting. In order to explore the brain neural mechanism in the preparation stage of pistol shooting under audiovisual restricted conditions, and to reveal the intrinsic relationship between brain activity and shooting behavior indicators, the electroencephalography (EEG) signals and seven shooting behaviors including shooting performance, gun holding stability, and firing stability, were experimentally captured from 30 shooters, these shooters performed pistol shooting under three conditions, normal, dim, and noisy. Using EEG microstates combined with standardized low-resolution brain electromagnetic tomography (sLORETA) traceability analysis method, we investigated the difference between the microstates characteristics under audiovisual restricted conditions and normal condition, the relationship between the microstates characteristics and the behavioral indicators during the shooting preparation stage under different conditions. The experimental results showed that microstate 1 corresponded to microstate A, microstate 2 corresponded to microstate B, and microstate 4 corresponded to microstate D; Microstate 3 was a unique template, which was localized in the occipital lobe, its function was to generate the "vision for action"; The dim condition significantly reduced the shooter's performance, whereas the noisy condition had less effect on the shooter's performance; In audiovisual restricted conditions, the microstate characteristics were significantly different from those in the normal condition. Microstate 4' parameters decreased significantly while microstate 3' parameters increased significantly under restricted visual and auditory conditions; Dim condition required more shooting skills from the shooter; There was a significant relationship between characteristics of microstates and indicators of shooting behavior; It was concluded that in order to obtain good shooting performance, shooters should improve attention and concentrate on the adjustment of collimator and target's center leveling relation, but the focus was slightly different in the three conditions; Microstates that are more important for accomplishing the task have less variation in their characteristics over time; Similar conclusions to previous studies were obtained at the same time, i.e., increased visual attention prior to shooting is detrimental to shooting performance, and there is a high positive correlation with microstate D for task completion. The experimental results further reveal the brain neural mechanism in the shooting preparation stage, and the extracted neural markers can be used as effective functional indicators for monitoring the brain state in the shooting preparation stage of pistols.

Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes

Engaging in phone conversations or other cognitively challenging tasks while driving detrimentally impacts cognitive functions and has been associated with increased risk of accidents. Existing EEG methods have been shown to differentiate between load and no load, but not between different levels of cognitive load. Furthermore, it has not been investigated whether EEG measurements of load can be used to predict safety outcomes in critical events. EEG microstates analysis, categorizing EEG signals into a concise set of prototypical functional states, has been used in other task contexts with good results, but has not been applied in the driving context. Here, this gap is addressed by means of a driving simulation experiment. Three phone use conditions (no phone use, hands-free, and handheld), combined with two task difficulty levels (single- or double-digit addition and subtraction), were tested before and during a rear-end collision conflict. Both conventional EEG spectral power and EEG microstates were analyzed. The results showed that different levels of cognitive load influenced EEG microstates differently, while EEG spectral power remained unaffected. A distinct EEG pattern emerged when drivers engaged in phone tasks while driving, characterized by a simultaneous increase and decrease in two of the EEG microstates, suggesting a heightened focus on auditory information, potentially at a cost to attention reorientation ability. The increase and decrease in these two microstates follow a monotonic sequence from baseline to hands-free simple, hands-free complex, handheld simple, and finally handheld complex, showing sensitivity to task difficulty. This pattern was found both before and after the lead vehicle braked. Furthermore, EEG microstates prior to the lead vehicle braking improved predictions of safety outcomes in terms of minimum time headway after the lead vehicle braked, clearly suggesting that these microstates measure brain states which are indicative of impaired driving. Additionally, EEG microstates are more predictive of safety outcomes than task difficulty, highlighting individual differences in task effects. These findings enhance our understanding of the neural dynamics involved in distracted driving and can be used in methods for evaluating the cognitive load induced by in-vehicle systems.

Neurophysiological measures of covert semantic processing in neurotypical adolescents actively ignoring spoken sentence inputs: A high-density event-related potential (ERP) study

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

Adaptation patterns and their associations with mismatch negativity: An electroencephalogram (EEG) study with controlled expectations

Adaptation refers to the decreased neural response that occurs after repeated exposure to a stimulus. While many electroencephalogram (EEG) studies have investigated adaptation by using either single or multiple repetitions, the adaptation patterns under controlled expectations manifested in the two main auditory components, N1 and P2, are still largely unknown. Additionally, although multiple repetitions are commonly used in mismatch negativity (MMN) experiments, it is unclear how adaptation at different time windows contributes to this phenomenon. In this study, we conducted an EEG experiment with 37 healthy adults using a random stimulus arrangement and extended tone sequences to control expectations. We tracked the amplitudes of the N1 and P2 components across the first 10 tones to examine adaptation patterns. Our findings revealed an L-shaped adaptation pattern characterised by a significant decrease in N1 amplitude after the first repetition (N1 initial adaptation), followed by a continuous, linear increase in P2 amplitude after the first repetition (P2 subsequent adaptation), possibly indicating model adjustment. Regression analysis demonstrated that the peak amplitudes of both the N1 initial adaptation and the P2 subsequent adaptation significantly accounted for variance in MMN amplitude. These results suggest distinct adaptation patterns for multiple repetitions across different components and indicate that the MMN reflects a combination of two processes: the initial adaptation in the N1 and a continuous model adjustment effect in the P2. Understanding these processes separately could have implications for models of cognitive processing and clinical disorders.

Influence of Large-Scale Brain State Dynamics on the Evoked Response to Brain Stimulation

Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.

Lower frequency range of auditory input facilitates stream segregation in older adults

The current study investigated the effect of lower frequency input on stream segregation acuity in older, normal hearing adults. Using event-related brain potentials (ERPs) and perceptual performance measures, we previously showed that stream segregation abilities were less proficient in older compared to younger adults. However, in that study we used frequency ranges greater than 1500 Hz. In the current study, we lowered the target frequency range below 1500 Hz and found similar stream segregation abilities in younger and older adults. These results indicate that the perception of complex auditory scenes is influenced by the spectral content of the auditory input and suggest that lower frequency ranges of input in older adults may facilitate listening ability in complex auditory environments. These results also have implications for the advancement of prosthetic devices.

Abnormal Global Cortical Responses in Drug-Naïve Patients With Schizophrenia Following Orbitofrontal Cortex Stimulation: A Concurrent Transcranial Magnetic Stimulation-Electroencephalography Study

BACKGROUND

Abnormalities in cortical excitability and plasticity have been considered to underlie the pathophysiology of schizophrenia. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) can provide a direct evaluation of cortical responses to TMS. Here, we employed TMS-EEG to investigate cortical responses to orbitofrontal cortex (OFC) stimulation in schizophrenia.

METHODS

In total, we recruited 92 drug-naïve patients with first-episode schizophrenia and 51 age- and sex-matched healthy individuals. For each participant, one session of 1-Hz repetitive TMS (rTMS) was delivered to the right OFC, and TMS-EEG data were obtained to explore the change in cortical-evoked activities before and immediately after rTMS during the eyes-closed state. The MATRICS Consensus Cognitive Battery was used to assess neurocognitive performance.

RESULTS

The cortical responses indexed by global mean field amplitudes (i.e., P30, N45, and P60) were larger in patients with schizophrenia than in healthy control participants at baseline. Furthermore, after one session of 1-Hz rTMS over the right OFC, the N100 amplitude was significantly reduced in the healthy control group but not in the schizophrenia group. In the healthy control participants, there was a significant correlation between modulation of P60 amplitude by rTMS and working memory; however, this correlation was absent in patients with schizophrenia.

CONCLUSIONS

Aberrant global cortical responses following right OFC stimulation were found in patients with drug-naïve first-episode schizophrenia, supporting its significance in the primary pathophysiology of schizophrenia.

Processing of emotional connotations in Chinese monomorphic and compound words reflected by the early posterior negativity

Writing stands as one of humanity's most profound inventions, facilitating the efficient sharing and transmission of vast amounts of information. Similar to images and facial expressions, visual (written) words possess the ability to evoke emotional connotations. Understanding how the brain perceives these emotional nuances encoded in highly symbolic visual words is a key focus of the emerging field of "affective neurolinguistics." At the core of this inquiry lies the examination of the early posterior negativity (EPN), an event-related potentials (ERPs) component peaking around 300 ms after stimulus onset in the occipitotemporal scalp region. EPN has consistently emerged in response to emotional stimuli, encompassing pictures, faces, and visual words. However, prior research has notably lacked observation of EPN in response to Chinese emotional words, raising questions about potential differences in emotional processing between Chinese and other languages. Given the logographic nature of the Chinese writing system and the prevalence of compound words in the Chinese lexicon, this study aims to explore whether the emotional processing of Chinese monomorphic and compound words elicits an EPN response. Two experiments were conducted: Experiment 1 utilized one-character words (monomorphic words), while Experiment 2 employed two-character words (compound words). Participants were assigned a go/no-go task, instructed to respond to unknown words (word recognition task) or blue stimuli (color decision task). Data analysis using a data-driven mass univariate approach revealed significant ERP differences between emotional and neutral words. Notably, the time course, scalp topography, and cortical generators of the difference ERP presented a characteristic EPN response in both experiments. These findings strongly support the notion that the processing of emotional connotations in both Chinese monomorphic and compound words is reflected by the EPN, paving the way for future research using EPN as an emotion-related ERP component for investigating emotional processing of Chinese words.

Exploring the potential of combining transcranial magnetic stimulation and electroencephalography to investigate mild cognitive impairment and Alzheimer's disease: a systematic review

Transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are non-invasive techniques used for neuromodulation and recording brain electrical activity, respectively. The integration of TMS-EEG has emerged as a valuable tool for investigating the complex mechanisms involved in age-related disorders, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). By systematically synthesizing TMS-EEG studies, this review aims to shed light on the neurophysiological mechanisms underlying MCI and AD, while also exploring the practical applications of TMS-EEG in clinical settings. PubMed, ScienceDirect, and PsychInfo were selected as the databases for this review. The 22 eligible studies included a total of 592 individuals with MCI or AD as well as 301 cognitively normal adults. TMS-EEG assessments unveiled specific patterns of corticospinal excitability, plasticity, and brain connectivity that distinguished individuals on the AD spectrum from cognitively normal older adults. Moreover, the TMS-induced EEG features were observed to be correlated with cognitive performance and the presence of AD pathological biomarkers. The comprehensive examination of the existing studies demonstrates that the combination of TMS and EEG has yielded valuable insights into the neurophysiology of MCI and AD. This integration shows great potential for early detection, monitoring disease progression, and anticipating response to treatment. Future research is of paramount importance to delve into the potential utilization of TMS-EEG for treatment optimization in individuals with MCI and AD.

Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach

Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.

Prefrontal oscillatory slowing in early-course schizophrenia is associated with worse cognitive performance and negative symptoms: a TMS-EEG study

BACKGROUND

Abnormalities in dorsolateral prefrontal cortex (DLPFC) oscillations are neurophysiological signatures of schizophrenia thought to underlie its cognitive deficits. Transcranial magnetic stimulation with electroencephalography (TMS-EEG) provides a measure of cortical oscillations unaffected by sensory relay functionality and/or patients' level of engagement, which are important confounding factors in schizophrenia. Previous TMS-EEG work showed reduced fast, gamma-range oscillations and a slowing of the main DLPFC oscillatory frequency, or natural frequency, in chronic schizophrenia. However, it is unclear whether this DLPFC natural frequency slowing is present in early-course schizophrenia (EC-SCZ) and is associated with symptom severity and cognitive dysfunction.

METHODS

We applied TMS-EEG to the left DLPFC in 30 EC-SCZ and 28 healthy control (HC) subjects. Goal-directed working memory performance was assessed using the "AX" Continuous Performance Task (AX-CPT). The EEG frequency with the highest cumulative power at the stimulation site, or natural frequency, was extracted. We also calculated the local Relative Spectral Power (RSP) as the average power in each frequency band divided by the broadband power.

RESULTS

Compared to HC, EC-SCZ had reduced DLPFC natural frequency (p=0.0000002, Cohen's d=-2.32) and higher DLPFC beta-range RSP (p=0.0003, Cohen's d=0.77). In EC-SCZ, the DLPFC natural frequency was inversely associated with negative symptoms. Across all participants, the beta-band RSP negatively correlated with the AX-CPT performance.

CONCLUSIONS

A DLPFC oscillatory slowing is an early pathophysiological biomarker of schizophrenia that is associated with its symptom severity and cognitive impairments. Future work should assess whether non-invasive neurostimulation can ameliorate prefrontal oscillatory deficits and related clinical functions in EC-SCZ.

Neural mechanism underlying preview effects and masked priming effects in visual word processing

Two classic experimental paradigms - masked repetition priming and the boundary paradigm - have played a pivotal role in understanding the process of visual word recognition. Traditionally, these paradigms have been employed by different communities of researchers, with their own long-standing research traditions. Nevertheless, a review of the literature suggests that the brain-electric correlates of word processing established with both paradigms may show interesting similarities, in particular with regard to the location, timing, and direction of N1 and N250 effects. However, as of yet, no direct comparison has been undertaken between the two paradigms. In the current study, we used combined eye-tracking/EEG to perform such a within-subject comparison using the same materials (single Chinese characters) as stimuli. To facilitate direct comparisons, we used a simplified version of the boundary paradigm - the single word boundary paradigm. Our results show the typical early repetition effects of N1 and N250 for both paradigms. However, repetition effects in N250 (i.e., a reduced negativity following identical-word primes/previews as compared to different-word primes/previews) were larger with the single word boundary paradigm than with masked priming. For N1 effects, repetition effects were similar across the two paradigms, showing a larger N1 after repetitions as compared to alternations. Therefore, the results indicate that at the neural level, a briefly presented and masked foveal prime produces qualitatively similar facilitatory effects on visual word recognition as a parafoveal preview before a single saccade, although such effects appear to be stronger in the latter case.

Electrophysiological Correlates of Reward Anticipation in Subjects with Schizophrenia: An ERP Microstate Study

The current study aimed to investigate alterations of event-related potentials (ERPs) microstate during reward anticipation in subjects with schizophrenia (SCZ), and their association with hedonic experience and negative symptoms. EEG data were recorded in thirty SCZ and twenty-three healthy controls (HC) during the monetary incentive delay task in which reward, loss and neutral cues were presented. Microstate analysis and standardized low-resolution electromagnetic tomography (sLORETA) were applied to EEG data. Furthermore, analyses correlating a topographic index (the ERPs score), calculated to quantify brain activation in relationship to the microstate maps, and scales assessing hedonic experience and negative symptoms were performed. Alterations in the first (125.0-187.5 ms) and second (261.7-414.1 ms) anticipatory cue-related microstate classes were observed. In SCZ, reward cues were associated to shorter duration and earlier offset of the first microstate class as compared to the neutral condition. In the second microstate class, the area under the curve was smaller for both reward and loss anticipation cues in SCZ as compared to HC. Furthermore, significant correlations between ERPs scores and the anticipation of pleasure scores were detected, while no significant association was found with negative symptoms. sLORETA analysis showed that hypo-activation of the cingulate cortex, insula, orbitofrontal and parietal cortex was detected in SCZ as compared to HC. Abnormalities in ERPs could be traced already during the early stages of reward processing and were associated with the anticipation of pleasure, suggesting that these dysfunctions might impair effective evaluation of incoming pleasant experiences. Negative symptoms and anhedonia are partially independent results.

Neurophysiological markers of disease severity and cognitive dysfunction in major depressive disorder: A TMS-EEG study

Background

Transcranial magnetic stimulation-electroencephalography (TMS-EEG) is a powerful technique to study the neuropathology and biomarkers of major depressive disorder (MDD). This study investigated cortical activity and its relationship with clinical symptoms and cognitive dysfunction in MDD patients by indexing TMS-EEG biomarkers in the dorsolateral prefrontal cortex (DLPFC).

Methods

133 patients with MDD and 76 healthy individuals participated in this study. Single-pulse TMS was performed on the left DLPFC to obtain TMS-evoked potential (TEP) indices. TMS-EEG waveforms and components were determined by global mean field amplitude. We used the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) to measure participants' cognitive function.

Results

Patients with MDD had a lower excitatory P180 index compared to healthy controls, and P180 amplitude was negatively correlated with the severity of depressive and anxiety symptoms in patients with MDD. In the MDD group, P30 amplitude was negatively associated with RBANS Visuospatial/ Constructional index and total score.

Conclusions

TMS-EEG findings suggest that abnormal cortical excitation and inhibition induced by TMS on the DLPFC are associated with the severity of clinical symptoms and cognitive dysfunction in patients with MDD. P180 and P30 have the potential to serve as neurophysiological biomarkers of clinical symptoms and cognitive dysfunction in MDD patients, respectively.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Longstanding Auditory Sensory and Semantic Differences in Preterm Born Children

More than 10% of births are preterm, and the long-term consequences on sensory and semantic processing of non-linguistic information remain poorly understood. 17 very preterm-born children (born at < 33 weeks gestational age) and 15 full-term controls were tested at 10 years old with an auditory object recognition task, while 64-channel auditory evoked potentials (AEPs) were recorded. Sounds consisted of living (animal and human vocalizations) and manmade objects (e.g. household objects, instruments, and tools). Despite similar recognition behavior, AEPs strikingly differed between full-term and preterm children. Starting at 50ms post-stimulus onset, AEPs from preterm children differed topographically from their full-term counterparts. Over the 108-224ms post-stimulus period, full-term children showed stronger AEPs in response to living objects, whereas preterm born children showed the reverse pattern; i.e. stronger AEPs in response to manmade objects. Differential brain activity between semantic categories could reliably classify children according to their preterm status. Moreover, this opposing pattern of differential responses to semantic categories of sounds was also observed in source estimations within a network of occipital, temporal and frontal regions. This study highlights how early life experience in terms of preterm birth shapes sensory and object processing later on in life.

Repetition Suppression for Familiar Visual Words Through Acceleration of Early Processing

The visual N1 (N170) component with occipito-temporal negativity and fronto-central positivity is sensitive to visual expertise for print. Slightly later, an N200 component with an increase after stimulus repetition was reported to be specific for Chinese, but found at centro-parietal electrodes against a mastoid reference. Given the unusual location, temporal proximity to the N1, and atypical repetition behavior, we aimed at clarifying the relation between the two components. We collected 128-channel EEG data from 18 native Chinese readers during a script decision experiment. Familiar Chinese one- and two-character words were presented among unfamiliar Korean control stimuli with half of the stimuli immediately repeated. Stimulus repetition led to a focal increase in the N1 onset and to a wide-spread decrease in the N1 offset, especially for familiar Chinese and also prominently near the mastoids. A TANOVA analysis corroborated robust repetition effects in the N1 offset across ERP maps with a modulation by script familiarity around 300 ms. Microstate analyses revealed a shorter N1 microstate duration after repetitions, especially for Chinese. The results demonstrate that the previously reported centro-parietal N200 effects after repetitions reflect changes during the N1 offset at occipito-temporal electrodes including the mastoids. Although larger for Chinese, repetition effects could also be found for two-character Korean words, suggesting that they are not specific for Chinese. While the decrease of the N1 offset after repetition is in agreement with a repetition suppression effect, the microstate findings suggest that at least part of the facilitation is due to accelerated processing after repetition.

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

Investigating cortical excitability and inhibition in patients with schizophrenia: A TMS-EEG study

BACKGROUND

Transcranial magnetic stimulation (TMS) combined with electromyography (EMG) has widely been used as a non-invasive brain stimulation tool to assess excitation/inhibition (E/I) balance. E/I imbalance is a putative mechanism underlying symptoms in patients with schizophrenia. Combined TMS-electroencephalography (TMS-EEG) provides a detailed examination of cortical excitability to assess the pathophysiology of schizophrenia. This study aimed to investigate differences in TMS-evoked potentials (TEPs), TMS-related spectral perturbations (TRSP) and intertrial coherence (ITC) between patients with schizophrenia and healthy controls.

MATERIALS AND METHODS

TMS was applied over the motor cortex during EEG recording. Differences in TEPs, TRSP and ITC between the patient and healthy subjects were analysed for all electrodes at each time point, by applying multiple independent sample t-tests with a cluster-based permutation analysis to correct for multiple comparisons.

RESULTS

Patients demonstrated significantly reduced amplitudes of early and late TEP components compared to healthy controls. Patients also showed a significant reduction of early delta (50-160 ms) and theta TRSP (30-250ms),followed by a reduction in alpha and beta suppression (220-560 ms; 190-420 ms). Patients showed a reduction of both early (50-110 ms) gamma increase and later (180-230 ms) gamma suppression. Finally, the ITC was significantly lower in patients in the alpha band, from 30 to 260 ms.

CONCLUSION

Our findings support the putative role of impaired GABA-receptor mediated inhibition in schizophrenia impacting excitatory neurotransmission. Further studies can usefully elucidate mechanisms underlying specific symptoms clusters using TMS-EEG biometrics.

Changes in visually and auditory attended audiovisual speech processing in cochlear implant users: A longitudinal ERP study

Limited auditory input, whether caused by hearing loss or by electrical stimulation through a cochlear implant (CI), can be compensated by the remaining senses. Specifically for CI users, previous studies reported not only improved visual skills, but also altered cortical processing of unisensory visual and auditory stimuli. However, in multisensory scenarios, it is still unclear how auditory deprivation (before implantation) and electrical hearing experience (after implantation) affect cortical audiovisual speech processing. Here, we present a prospective longitudinal electroencephalography (EEG) study which systematically examined the deprivation- and CI-induced alterations of cortical processing of audiovisual words by comparing event-related potentials (ERPs) in postlingually deafened CI users before and after implantation (five weeks and six months of CI use). A group of matched normal-hearing (NH) listeners served as controls. The participants performed a word-identification task with congruent and incongruent audiovisual words, focusing their attention on either the visual (lip movement) or the auditory speech signal. This allowed us to study the (top-down) attention effect on the (bottom-up) sensory cortical processing of audiovisual speech. When compared to the NH listeners, the CI candidates (before implantation) and the CI users (after implantation) exhibited enhanced lipreading abilities and an altered cortical response at the N1 latency range (90-150 ms) that was characterized by a decreased theta oscillation power (4-8 Hz) and a smaller amplitude in the auditory cortex. After implantation, however, the auditory-cortex response gradually increased and developed a stronger intra-modal connectivity. Nevertheless, task efficiency and activation in the visual cortex was significantly modulated in both groups by focusing attention on the visual as compared to the auditory speech signal, with the NH listeners additionally showing an attention-dependent decrease in beta oscillation power (13-30 Hz). In sum, these results suggest remarkable deprivation effects on audiovisual speech processing in the auditory cortex, which partially reverse after implantation. Although even experienced CI users still show distinct audiovisual speech processing compared to NH listeners, pronounced effects of (top-down) direction of attention on (bottom-up) audiovisual processing can be observed in both groups. However, NH listeners but not CI users appear to show enhanced allocation of cognitive resources in visually as compared to auditory attended audiovisual speech conditions, which supports our behavioural observations of poorer lipreading abilities and reduced visual influence on audition in NH listeners as compared to CI users.

Effect of short-term exposure to Raag Bilawal of North Indian classical music on young Indian adults: a high-density electroencephalogram microstate study

Introduction

the objective of the study was to find out the microstate map topographies and their parameters generated during the resting state and during listening to North Indian classical Music Raag 'the Raag Bilawal'. It was hypothesized that in the resting state and during listening to music conditions, there would be a difference in microstate parameters i.e. mean duration, global explained variance (GEV), and time coverage.

Methods

a 128-channel electroencephalogram (EEG) was recorded for 12 Indian subjects (average age 26.1+1.4 years) while resting and listening to music using the EEG microstate investigation. Investigation and comparison of the microstate parameters were the mean duration, global explained variance (GEV), and time coverage between both conditions were performed.

Results

seven microstate maps were found to represent the resting state and listening to music condition, four canonical and three novel maps. No statistically significant difference was found between the two conditions for time coverage and mean duration. The statistical significance levels of the map-1, map-2, map-3, map-4, map-5, map-6, and map-7 for the mean duration were 0.4, 0.6, 0.97, 0.34, 0.32, 0.69, and 0.29 respectively; and for time coverage were 0.92, 0.92, 0.96, 0.64, 0.78, 0.38, and 0.76 respectively. Map-1, map-4, and map-7 were the three novel maps we found in our study.

Conclusion

similarities regarding stability and predominance of maps with small vulnerability exist in both conditions indicating that phonological, visual, and dorsal attention networks may be activated in both resting state and listening to music condition.

The neural representation of an auditory spatial cue in the primate cortex

Humans make use of small differences in the timing of sounds at the two ears-interaural time differences (ITDs)-to locate their sources. Despite extensive investigation, however, the neural representation of ITDs in the human brain is contentious, particularly the range of ITDs explicitly represented by dedicated neural detectors. Here, using magneto- and electro-encephalography (MEG and EEG), we demonstrate evidence of a sparse neural representation of ITDs in the human cortex. The magnitude of cortical activity to sounds presented via insert earphones oscillated as a function of increasing ITD-within and beyond auditory cortical regions-and listeners rated the perceptual quality of these sounds according to the same oscillating pattern. This pattern was accurately described by a population of model neurons with preferred ITDs constrained to the narrow, sound-frequency-dependent range evident in other mammalian species. When scaled for head size, the distribution of ITD detectors in the human cortex is remarkably like that recorded in vivo from the cortex of rhesus monkeys, another large primate that uses ITDs for source localization. The data solve a long-standing issue concerning the neural representation of ITDs in humans and suggest a representation that scales for head size and sound frequency in an optimal manner.

Enhancing touch sensibility with sensory electrical stimulation and sensory retraining

A large proportion of stroke survivors suffer from sensory loss, negatively impacting their independence, quality of life, and neurorehabilitation prognosis. Despite the high prevalence of somatosensory impairments, our understanding of somatosensory interventions such as sensory electrical stimulation (SES) in neurorehabilitation is limited. We aimed to study the effectiveness of SES combined with a sensory discrimination task in a well-controlled virtual environment in healthy participants, setting a foundation for its potential application in stroke rehabilitation. We employed electroencephalography (EEG) to gain a better understanding of the underlying neural mechanisms and dynamics associated with sensory training and SES. We conducted a single-session experiment with 26 healthy participants who explored a set of three visually identical virtual textures-haptically rendered by a robotic device and that differed in their spatial period-while physically guided by the robot to identify the odd texture. The experiment consisted of three phases: pre-intervention, intervention, and post-intervention. Half the participants received subthreshold whole-hand SES during the intervention, while the other half received sham stimulation. We evaluated changes in task performance-assessed by the probability of correct responses-before and after intervention and between groups. We also evaluated differences in the exploration behavior, e.g., scanning speed. EEG was employed to examine the effects of the intervention on brain activity, particularly in the alpha frequency band (8-13 Hz) associated with sensory processing. We found that participants in the SES group improved their task performance after intervention and their scanning speed during and after intervention, while the sham group did not improve their task performance. However, the differences in task performance improvements between groups only approached significance. Furthermore, we found that alpha power was sensitive to the effects of SES; participants in the stimulation group exhibited enhanced brain signals associated with improved touch sensitivity likely due to the effects of SES on the central nervous system, while the increase in alpha power for the sham group was less pronounced. Our findings suggest that SES enhances texture discrimination after training and has a positive effect on sensory-related brain areas. Further research involving brain-injured patients is needed to confirm the potential benefit of our solution in neurorehabilitation.

A simulation study: comparing independent component analysis and signal-space projection - source-informed reconstruction for rejecting muscle artifacts evoked by transcranial magnetic stimulation

Introduction

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection-source-informed reconstruction approach (SSP-SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP-SIR.

Methods

We simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS-EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP-SIR, and subsequent comparisons were made with the ground truth data.

Results

ICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP-SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP-SIR being better when difference between topographies was larger. Overall, SSP-SIR performed better than ICA across the tested conditions. Based on these simulations, SSP-SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials.

Discussion

Selecting between ICA and SSP-SIR should be guided by the characteristics of the artifacts. SSP-SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP-SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.

Directed physiological networks in the human prefrontal cortex at rest and post transcranial photobiomodulation

Cerebral infra-slow oscillation (ISO) is a source of vasomotion in endogenic (E; 0.005-0.02 Hz), neurogenic (N; 0.02-0.04 Hz), and myogenic (M; 0.04-0.2 Hz) frequency bands. In this study, we quantified changes in prefrontal concentrations of oxygenated hemoglobin (Δ[HbO]) and redox-state cytochrome c oxidase (Δ[CCO]) as hemodynamic and metabolic activity metrics, and electroencephalogram (EEG) powers as electrophysiological activity, using concurrent measurements of 2-channel broadband near-infrared spectroscopy and EEG on the forehead of 22 healthy participants at rest. After preprocessing, the multi-modality signals were analyzed using generalized partial directed coherence to construct unilateral neurophysiological networks among the three neurophysiological metrics (with simplified symbols of HbO, CCO, and EEG) in each E/N/M frequency band. The links in these networks represent neurovascular, neurometabolic, and metabolicvascular coupling (NVC, NMC, and MVC). The results illustrate that the demand for oxygen by neuronal activity and metabolism (EEG and CCO) drives the hemodynamic supply (HbO) in all E/N/M bands in the resting prefrontal cortex. Furthermore, to investigate the effect of transcranial photobiomodulation (tPBM), we performed a sham-controlled study by delivering an 800-nm laser beam to the left and right prefrontal cortex of the same participants. After performing the same data processing and statistical analysis, we obtained novel and important findings: tPBM delivered on either side of the prefrontal cortex triggered the alteration or reversal of directed network couplings among the three neurophysiological entities (i.e., HbO, CCO, and EEG frequency-specific powers) in the physiological network in the E and N bands, demonstrating that during the post-tPBM period, both metabolism and hemodynamic supply drive electrophysiological activity in directed network coupling of the prefrontal cortex (PFC). Overall, this study revealed that tPBM facilitates significant modulation of the directionality of neurophysiological networks in electrophysiological, metabolic, and hemodynamic activities.

Analysis of EEG microstates as biomarkers in neuropsychological processes - Review

Microstate analysis is a spatiotemporal method where instantaneous scalp potential topography represents the current state of the brain. The temporal evolution of these scalp topographies gives an understanding of quasi-stable periods of long-range coherence between distant electrodes, reflecting functional coordination within large-scale cortical networks. It has been proven potential in identification and characterization of neurophysiological indicators associated with neuropsychiatric conditions. Changes in microstates connected to symptoms and cognitive impairments of neuropsychiatric conditions. It is useful in the study of cognitive processes and disorders related to memory. Researchers may probe into the relationships between microstates and other cognitive processes, such as memory retrieval and encoding. This is a tool for clinicians to enhance the precision of diagnosis and inform possibilities for treatment by acquiring information regarding individual diversity in microstates could lead to tailored medical methods. Customizing treatment according to a patient's microstate patterns could improve the efficacy of treatment. The papers selected for the review span a broad-spectrum including memory related disorders, psychiatry and neurological disorders. A section in the review article has been dedicated to source localization of EEG microstates. The selection of review papers shed light on the importance and huge potential of application of EEG microstate analysis in various neuropsychological processes. The review concludes with the need for standardization of microstate analysis. It suggests the incorporation of widely accepted machine learning techniques for increasing the accuracy, reliability and acceptability of microstate analysis as reliable biomarkers for neurological conditions in the future.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

All-night spectral and microstate EEG analysis in patients with recurrent isolated sleep paralysis

The pathophysiology of recurrent isolated sleep paralysis (RISP) has yet to be fully clarified. Very little research has been performed on electroencephalographic (EEG) signatures outside RISP episodes. This study aimed to investigate whether sleep is disturbed even without the occurrence of a RISP episode and in a stage different than conventional REM sleep. 17 RISP patients and 17 control subjects underwent two consecutive full-night video-polysomnography recordings. Spectral analysis was performed on all sleep stages in the delta, theta, and alpha band. EEG microstate (MS) analysis was performed on the NREM 3 phase due to the overall high correlation of subject template maps with canonical templates. Spectral analysis showed a significantly higher power of theta band activity in REM and NREM 2 sleep stages in RISP patients. The observed rise was also apparent in other sleep stages. Conversely, alpha power showed a downward trend in RISP patients' deep sleep. MS maps similar to canonical topographies were obtained indicating the preservation of prototypical EEG generators in RISP patients. RISP patients showed significant differences in the temporal dynamics of MS, expressed by different transitions between MS C and D and between MS A and B. Both spectral analysis and MS characteristics showed abnormalities in the sleep of non-episodic RISP subjects. Our findings suggest that in order to understand the neurobiological background of RISP, there is a need to extend the analyzes beyond REM-related processes and highlight the value of EEG microstate dynamics as promising functional biomarkers of RISP.

EEG microstate co-specificity in schizophrenia and obsessive-compulsive disorder

The past 20 years of research on EEG microstates has yielded the hypothesis that the imbalance pattern in the temporal dynamics of microstates C (increased) and D (decreased) is specific to schizophrenia. A similar microstate imbalance has been recently found in obsessive-compulsive disorder (OCD). The aim of the present high-density EEG study was to examine whether this pathological microstate pattern is co-specific to schizophrenia and OCD. We compared microstate temporal dynamics using Bayesian analyses, transition probabilities analyses and the Topographic Electrophysiological State Source-Imaging method for source reconstruction in 24 OCD patients and 28 schizophrenia patients, respectively, free of comorbid psychotic and OCD symptoms, and 27 healthy controls. OCD and schizophrenia patients exhibited the same increased contribution of microstate C, decreased duration and contribution of microstate D and greater D → C transition probabilities, compared with controls. A Bayes factor of 4.424 for the contribution of microstate C, 4.600 and 3.824, respectively, for the duration and contribution of microstate D demonstrated that there was no difference in microstate patterns between the two disorders. Source reconstruction further showed undistinguishable dysregulations between the Salience Network (SN), associated with microstate C, and the Executive Control Network (ECN), associated with microstate D, and between the ECN and cognitive cortico-striato-thalamo-cortical (CSTC) loop in the two disorders. The ECN/CSTC loop dysconnectivity was slightly worsened in schizophrenia. Our findings provide substantial evidence for a common aetiological pathway in schizophrenia and OCD, i.e. microstate co-specificity, and same anomalies in salience and external attention processing, leading to co-expression of symptoms.

Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network

Mild cognitive impairment (MCI) is the initial phase of Alzheimer's disease (AD). The cognitive decline is linked to abnormal connectivity between different regions of the brain. Most brain network studies fail to consider the changes in brain patterns and do not reflect the dynamic pathological characteristics of patients. Therefore, this paper proposes a method for constructing brain networks based on microstate sequences. It also analyzes the microstate temporal parameters and introduces a new feature, the brain homeostasis coefficient (Bhc), to quantify the stability of patient brain connections. The results showed that microstate class B parameters were higher in the MCI than in the HC group. Additionally, the Bhc values in most channels of the MCI and AD groups were lower than those of the HC group, with the most significant differences observed in the right frontal lobe. These differences were statistically significant (P < 0.05). The findings indicate that connectivity in the right frontal lobe may be most severely disrupted in patients with cognitive impairment. Furthermore, the Montreal Cognitive Assessment score showed a strong positive correlation with Bhc. This suggests that Bhc could be a novel biomarker for evaluating cognitive function in patients with cognitive impairment.

Dynamic Neural Patterns of Human Emotions in Virtual Reality: Insights from EEG Microstate Analysis

Emotions play a crucial role in human life and affect mental health. Understanding the neural patterns associated with emotions is essential. Previous studies carried out some exploration of the neural features of emotions, but most have designed experiments in two-dimensional (2D) environments, which differs from real-life scenarios. To create a more real environment, this study investigated emotion-related brain activity using electroencephalography (EEG) microstate analysis in a virtual reality (VR) environment. We recruited 42 healthy volunteers to participate in our study. We explored the dynamic features of different emotions, and four characteristic microstates were analyzed. In the alpha band, microstate A exhibited a higher occurrence in both negative and positive emotions than in neutral emotions. Microstate C exhibited a prolonged duration of negative emotions compared to positive emotions, and a higher occurrence was observed in both microstates C and D during positive emotions. Notably, a unique transition pair was observed between microstates B and C during positive emotions, whereas a unique transition pair was observed between microstates A and D during negative emotions. This study emphasizes the potential of integrating virtual reality (VR) and EEG to facilitate experimental design. Furthermore, this study enhances our comprehension of neural activities during various emotional states.

Electroencephalography microstates as novel functional biomarkers for insomnia disorder

Background

Insomnia disorder (ID) is one of the most common mental disorders. Research on ID focuses on exploring its mechanism of disease, novel treatments and treatment outcome prediction. An emerging technique in this field is the use of electroencephalography (EEG) microstates, which offer a new method of EEG feature extraction that incorporates information from both temporal and spatial dimensions.

Aims

To explore the electrophysiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) for ID treatment and use baseline microstate metrics for the prediction of its efficacy.

Methods

This study included 60 patients with ID and 40 age-matched and gender-matched good sleep controls (GSC). Their resting-state EEG microstates were analysed, and the Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were collected to assess sleep quality. The 60 patients with ID were equally divided into active and sham groups to receive rTMS for 20 days to test whether rTMS had a moderating effect on abnormal microstates in patients with ID. Furthermore, in an independent group of 90 patients with ID who received rTMS treatment, patients were divided into optimal and suboptimal groups based on their median PSQI reduction rate. Baseline EEG microstates were used to build a machine-learning predictive model for the effects of rTMS treatment.

Results

The class D microstate was less frequent and contribute in patients with ID, and these abnormalities were associated with sleep onset latency as measured by PSG. Additionally, the abnormalities were partially reversed to the levels observed in the GSC group following rTMS treatment. The baseline microstate characteristics could predict the therapeutic effect of ID after 20 days of rTMS, with an accuracy of 80.13%.

Conclusions

Our study highlights the value of EEG microstates as functional biomarkers of ID and provides a new perspective for studying the neurophysiological mechanisms of ID. In addition, we predicted the therapeutic effect of rTMS on ID based on the baseline microstates of patients with ID. This finding carries great practical significance for the selection of therapeutic options for patients with ID.

EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.

A comprehensive characterization of cognitive performance, clinical symptoms, and cortical activity following mild traumatic brain injury (mTBI)

OBJECTIVE

The objective of this study was to investigate clinical symptoms, cognitive performance and cortical activity following mild traumatic brain injury (mTBI).

METHODS

We recruited 30 individuals in the sub-acute phase post mTBI and 28 healthy controls with no history of head injury and compared these groups on clinical, cognitive and cortical activity measures. Measures of cortical activity included; resting state electroencephalography (EEG), task related EEG and combined transcranial magnetic stimulation with electroencephalography (TMS-EEG). Primary analyses investigated clinical, cognitive and cortical activity differences between groups. Exploratory analyses investigated the relationships between these measures.

RESULTS

At 4 weeks' post injury, mTBI participants exhibited significantly greater post concussive and clinical symptoms compared to controls; as well as reduced cognitive performance on verbal learning and working memory measures. mTBI participants demonstrated alterations in cortical activity while at rest and in response to stimulation with TMS.

CONCLUSIONS

The present study comprehensively characterized the multidimensional effect of mTBI in the sub-acute phase post injury, showing a broad range of differences compared to non-mTBI participants. Further research is needed to explore the relationship between these pathophysiologies and clinical/cognitive symptoms in mTBI.

A representation of abstract linguistic categories in the visual system underlies successful lipreading

There is considerable debate over how visual speech is processed in the absence of sound and whether neural activity supporting lipreading occurs in visual brain areas. Much of the ambiguity stems from a lack of behavioral grounding and neurophysiological analyses that cannot disentangle high-level linguistic and phonetic/energetic contributions from visual speech. To address this, we recorded EEG from human observers as they watched silent videos, half of which were novel and half of which were previously rehearsed with the accompanying audio. We modeled how the EEG responses to novel and rehearsed silent speech reflected the processing of low-level visual features (motion, lip movements) and a higher-level categorical representation of linguistic units, known as visemes. The ability of these visemes to account for the EEG - beyond the motion and lip movements - was significantly enhanced for rehearsed videos in a way that correlated with participants' trial-by-trial ability to lipread that speech. Source localization of viseme processing showed clear contributions from visual cortex, with no strong evidence for the involvement of auditory areas. We interpret this as support for the idea that the visual system produces its own specialized representation of speech that is (1) well-described by categorical linguistic features, (2) dissociable from lip movements, and (3) predictive of lipreading ability. We also suggest a reinterpretation of previous findings of auditory cortical activation during silent speech that is consistent with hierarchical accounts of visual and audiovisual speech perception.

Towards a dynamical understanding of microstate analysis of M/EEG data

One of the interesting aspects of EEG data is the presence of temporally stable and spatially coherent patterns of activity, known as microstates, which have been linked to various cognitive and clinical phenomena. However, there is still no general agreement on the interpretation of microstate analysis. Various clustering algorithms have been used for microstate computation, and multiple studies suggest that the microstate time series may provide insight into the neural activity of the brain in the resting state. This study addresses two gaps in the literature. Firstly, by applying several state-of-the-art microstate algorithms to a large dataset of EEG recordings, we aim to characterise and describe various microstate algorithms. We demonstrate and discuss why the three "classically" used algorithms ((T)AAHC and modified K-Means) yield virtually the same results, while HMM algorithm generates the most dissimilar results. Secondly, we aim to test the hypothesis that dynamical microstate properties might be, to a large extent, determined by the linear characteristics of the underlying EEG signal, in particular, by the cross-covariance and autocorrelation structure of the EEG data. To this end, we generated a Fourier transform surrogate of the EEG signal to compare microstate properties. Here, we found that these are largely similar, thus hinting that microstate properties depend to a very high degree on the linear covariance and autocorrelation structure of the underlying EEG data. Finally, we treated the EEG data as a vector autoregression process, estimated its parameters, and generated surrogate stationary and linear data from fitted VAR. We observed that such a linear model generates microstates highly comparable to those estimated from real EEG data, supporting the conclusion that a linear EEG model can help with the methodological and clinical interpretation of both static and dynamic human brain microstate properties.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Temporal and spectral analyses of EEG microstate reveals neural effects of transcranial photobiomodulation on the resting brain

Introduction

The quantification of electroencephalography (EEG) microstates is an effective method for analyzing synchronous neural firing and assessing the temporal dynamics of the resting state of the human brain. Transcranial photobiomodulation (tPBM) is a safe and effective modality to improve human cognition. However, it is unclear how prefrontal tPBM neuromodulates EEG microstates both temporally and spectrally.

Methods

64-channel EEG was recorded from 45 healthy subjects in both 8-min active and sham tPBM sessions, using a 1064-nm laser applied to the right forehead of the subjects. After EEG data preprocessing, time-domain EEG microstate analysis was performed to obtain four microstate classes for both tPBM and sham sessions throughout the pre-, during-, and post-stimulation periods, followed by extraction of the respective microstate parameters. Moreover, frequency-domain analysis was performed by combining multivariate empirical mode decomposition with the Hilbert-Huang transform.

Results

Statistical analyses revealed that tPBM resulted in (1) a significant increase in the occurrence of microstates A and D and a significant decrease in the contribution of microstate C, (2) a substantial increase in the transition probabilities between microstates A and D, and (3) a substantial increase in the alpha power of microstate D.

Discussion

These findings confirm the neurophysiological effects of tPBM on EEG microstates of the resting brain, particularly in class D, which represents brain activation across the frontal and parietal regions. This study helps to better understand tPBM-induced dynamic alterations in EEG microstates that may be linked to the tPBM mechanism of action for the enhancement of human cognition.

Directed physiological networks in the human prefrontal cortex at rest and post transcranial photobiomodulation

Cerebral infra-slow oscillation (ISO) is a source of vasomotion in endogenic (E; 0.005-0.02 Hz), neurogenic (N; 0.02-0.04 Hz), and myogenic (M; 0.04-0.2 Hz) frequency bands. In this study, we quantified changes in prefrontal concentrations of oxygenated hemoglobin ( Δ [ H b O ] ) and redox-state cytochrome c oxidase ( Δ [ C C O ] ) as hemodynamic and metabolic activity metrics, and electroencephalogram (EEG) powers as electrophysiological activity, using concurrent measurements of 2-channel broadband near-infrared spectroscopy and EEG on the forehead of 22 healthy participants at rest. After preprocessing, the multi-modality signals were analyzed using generalized partial directed coherence to construct unilateral neurophysiological networks among the three neurophysiological metrics (with simplified symbols of HbO, CCO, and EEG) in each E/N/M frequency band. The links in these networks represent neurovascular, neurometabolic, and metabolicvascular coupling (NVC, NMC, and MVC). The results illustrate that the demand for oxygen by neuronal activity and metabolism (EEG and CCO) drives the hemodynamic supply (HbO) in all E/N/M bands in the resting prefrontal cortex. Furthermore, to investigate the effect of transcranial photobiomodulation (tPBM), we performed a sham-controlled study by delivering an 800-nm laser beam to the left and right prefrontal cortex of the same participants. After performing the same data processing and statistical analysis, we obtained novel and important findings: tPBM delivered on either side of the prefrontal cortex triggered the alteration or reversal of directed network couplings among the three neurophysiological entities (i.e., HbO, CCO, and EEG frequency-specific powers) in the physiological network in the E and N bands, demonstrating that during the post-tPBM period, both metabolism and hemodynamic supply drive electrophysiological activity in directed network coupling of the PFC. Overall, this study revealed that tPBM facilitates significant modulation of the directionality of neurophysiological networks in electrophysiological, metabolic, and hemodynamic activities.

Social high performers under stress behave more prosocially and detect happy emotions better in a male sample

Psychosocial stress is increasing in society, impacting our lives in all social domains. However, the conditions under which stress facilitates ("tend-and-befriend") or hinders ("fight-or-flight") social approach remain elusive. We tested whether previous heterogeneous findings might be resolved by accounting for individual differences in social performance under stress. For that purpose, we introduce the novel Trier Social Stress Test (TSST) social performance index that was aggregated across ratings from two independent observers. Moreover, we apply an innovative setup enabling electroencephalographic (EEG) data to be measured inside an electrically-shielded cabin during stress, namely the TSST-EEG. Relying on a sample of 59 healthy male participants, we collected behavioral (i.e., sharing resources with others) and cognitive (i.e., detecting facial emotional expressions) approach patterns while participants experienced either acute psychosocial stress (n = 31) or no stress (control condition; n = 28) and while EEG was being recorded. During stress exposure, high-performing participants behaved more prosocially, and differentiated better between happy and neutral emotions on both behavioral and neurophysiological levels (revealed by intensity differences in a N170-like response). Overall, our findings demonstrate the added value of both the novel TSST social performance index and the novel TSST-EEG setup. By showing that high social performance during the TSST is associated with behavioral, cognitive, and neurophysiological approach patterns, our study offers valuable insights into adaptive or maladaptive psychobiological mechanisms in coping with psychosocial stress. Future stress research should address the role of social performance differences during stress in social interaction to better understand the behavioral consequences of psychosocial stress in humans.

Identifying Neurophysiological Markers of Intermittent Theta Burst Stimulation in Treatment-Resistant Depression Using Transcranial Magnetic Stimulation-Electroencephalography

BACKGROUND

Intermittent theta burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex is effective for treatment-resistant depression, but the effects of iTBS on neurophysiological markers remain unclear. Here, we indexed transcranial magnetic stimulation-electroencephalography (TMS-EEG) markers, specifically, the N45 and N100 amplitudes, at baseline and post-iTBS, comparing separated and contiguous iTBS schedules. TMS-EEG markers were also compared between iTBS responders and nonresponders.

METHODS

TMS-EEG was analyzed from a triple-blind 1:1 randomized trial for treatment-resistant depression, comparing a separated (54-minute interval) and contiguous (0-minute interval) schedule of 2 × 600-pulse iTBS for 30 treatments. Participants underwent TMS-EEG over the left dorsolateral prefrontal cortex at baseline and posttreatment. One hundred fourteen participants had usable TMS-EEG at baseline, and 98 at posttreatment. TMS-evoked potential components (N45, N100) were examined via global mean field analysis.

RESULTS

The N100 amplitude decreased from baseline to posttreatment, regardless of the treatment group (F1,106 = 5.20, p = .02). There were no changes in N45 amplitude in either treatment group. In responders, the N100 amplitude decreased after iTBS (F1,102 = 11.30, p = .001, pcorrected = .0004). Responders showed higher posttreatment N45 amplitude than nonresponders (F1,94 = 4.11, p = .045, pcorrected = .016). Higher baseline N100 amplitude predicted lower post-iTBS depression scores (F4,106 = 6.28, p = .00014).

CONCLUSIONS

These results provide further evidence for an association between the neurophysiological effects of iTBS and treatment efficacy in treatment-resistant depression. Future studies are needed to test the predictive potential for clinical applications of TMS-EEG markers.

Auditory cortical responses to abrupt lateralization shifts do not reflect the activity of hemifield-specific units involved in opponent coding of auditory space

Recent studies show that the classical model based on axonal delay-lines may not explain interaural time difference (ITD) based spatial coding in humans. Instead, a population-code model called "opponent channels model" (OCM) has been suggested. This model comprises two competing channels respectively for the two auditory hemifields, each with a sigmoidal tuning curve. Event-related potentials (ERPs) to ITD-changes are used in some studies to test the predictions of this model by considering the sounds before and after the change as adaptor and probe stimuli, respectively. It is assumed in these studies that the former stimulus causes adaptation of the neurons selective to its side, and that the ERP N1-P2 response to the ITD-change is the specific response of the neurons with selectivity to the side of probe sound. However, these ERP components are known as a global, non-specific acoustic change complex of cortical origin evoked by any change in the auditory environment. It probably does not genuinely reflect the activity of some stimulus-specific neuronal units that have escaped the refractory effect of the preceding adaptor, which means a violation of the crucial assumption in an adaptor-probe paradigm. To assess this viewpoint, we conducted two experiments. In the first one, we recorded ERPs to abrupt lateralization shifts of click trains having various pre- and post-shift ITDs within the physiological range of -600μs to +600μs. Magnitudes of the ERP components P1, N1, and P2 to these ITD-shifts did not comply with the additive behavior of partial probe responses presumed for an adaptor-probe paradigm, casting doubt on the accuracy of testing sensory coding models by using ERPs to abrupt lateralization changes. Findings of the second experiment, involving ERPs to conjoint outwards/transverse shift stimuli also supported this conclusion.

Spontaneous brain microstates correlate with impaired inhibitory control in internet addiction disorder

The prevalence of the Internet addiction disorder (IAD) has been on the rise, making it increasingly imperative to explore the neurophysiological markers of it. Using the whole-brain imaging approach of EEG microstate analysis, which treats multichannel EEG recordings as a series of quasi-steady states, similar as the resting-state networks found by fMRI, the present study aimed to investigate the specificity of the IAD in class C of the four canonical microstates. The existing EEG data of 40 participants (N = 20 for each group) was used, and correlation between the time parameters of microstate C and the performance of the Go/NoGo task was analyzed. Results suggested that the duration and coverage of class C were significantly reduced in the IAD group as compared to the healthy control (HC) group. Furthermore, the duration of class C had a significant inverse correlation with Go RTs in the IAD group. These results implied that class C might serve as a neurophysiological marker of IAD, helping to understand the underlying neural mechanism of inhibitory control in IAD.

Prior Knowledge Uses Prestimulus Alpha Band Oscillations and Persistent Poststimulus Neural Templates for Conscious Perception

Prior knowledge has a profound impact on the way we perceive the world. However, it remains unclear how the prior knowledge is maintained in our brains and thereby influences the subsequent conscious perception. The Dalmatian dog illusion is a perfect tool to study prior knowledge, where the picture is initially perceived as noise. Once the prior knowledge was introduced, a Dalmatian dog could be consciously seen, and the picture immediately became meaningful. Using pictures with hidden objects as standard stimuli and similar pictures without hidden objects as deviant stimuli, we investigated the neural representation of prior knowledge and its impact on conscious perception in an oddball paradigm using electroencephalogram (EEG) in both male and female human subjects. We found that the neural patterns between the prestimulus alpha band oscillations and poststimulus EEG activity were significantly more similar for the standard stimuli than for the deviant stimuli after prior knowledge was provided. Furthermore, decoding analysis revealed that persistent neural templates were evoked after the introduction of prior knowledge, similar to that evoked in the early stages of visual processing. In conclusion, the current study suggests that prior knowledge uses alpha band oscillations in a multivariate manner in the prestimulus period and induces specific persistent neural templates in the poststimulus period, enabling the conscious perception of the hidden objects.SIGNIFICANCE STATEMENT The visual world we live in is not always optimal. In dark or noisy environments, prior knowledge can help us interpret imperfect sensory signals and enable us to consciously perceive hidden objects. However, we still know very little about how prior knowledge works at the neural level. Using the Dalmatian dog illusion and multivariate methods, we found that prior knowledge uses prestimulus alpha band oscillations to carry information about the hidden object and exerts a persistent influence in the poststimulus period by inducing specific neural templates. Our findings provide a window into the neural underpinnings of prior knowledge and offer new insights into the role of alpha band oscillations and neural templates associated with conscious perception.

Effects of simultaneous transcutaneous auricular vagus nerve stimulation and high-definition transcranial direct current stimulation on disorders of consciousness: a study protocol

Background

Non-invasive brain stimulation (NIBS) techniques are now widely used in patients with disorders of consciousness (DOC) for accelerating their recovery of consciousness, especially minimally conscious state (MCS). However, the effectiveness of single NIBS techniques for consciousness rehabilitation needs further improvement. In this regard, we propose to enhance from bottom to top the thalamic-cortical connection by using transcutaneous auricular vagus nerve stimulation (taVNS) and increase from top to bottom cortical-cortical connections using simultaneous high-definition transcranial direct current stimulation (HD-tDCS) to reproduce the network of consciousness.

Methods/design

The study will investigate the effect and safety of simultaneous joint stimulation (SJS) of taVNS and HD-tDCS for the recovery of consciousness. We will enroll 84 MCS patients and randomize them into two groups: a single stimulation group (taVNS and HD-tDCS) and a combined stimulation group (SJS and sham stimulation). All patients will undergo a 4-week treatment. The primary outcome will be assessed using the coma recovery scale-revised (CRS-R) at four time points to quantify the effect of treatment: before treatment (T0), after 1 week of treatment (T1), after 2 weeks of treatment (T2), and after 4 weeks of treatment (T3). At the same time, nociception coma scale-revised (NCS-R) and adverse effects (AEs) will be collected to verify the safety of the treatment. The secondary outcome will involve an analysis of electroencephalogram (EEG) microstates to assess the response mechanisms of dynamic brain networks to SJS. Additionally, CRS-R and AEs will continue to be obtained for a 3-month follow-up (T4) after the end of the treatment.

Discussion

This study protocol aims to innovatively develop a full-time and multi-brain region combined neuromodulation paradigm based on the mesocircuit model to steadily promote consciousness recovery by restoring thalamocortical and cortical-cortical interconnections.

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer's disease

Aims

Our aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.

Methods

A total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.

Results

(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.

Conclusion

Integrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.