MNE software for processing MEG and EEG data

Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States

Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5 min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3 min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.

Examining the lateralization of electrophysiological correlates of auditory awareness

The neurological basis for perceptual awareness remains unclear, and theories disagree as to whether sensory cortices per se generate awareness. Critically, neural activity in the sensory cortices is only a neural correlate of consciousness (NCC) if it closely matches the contents of perceptual awareness. Research in vision and touch suggest that contralateral activity in sensory cortices is an NCC. Similarly, research in hearing with two sound sources (left and right) presented over headphones also suggests that a candidate NCC called the auditory awareness negativity (AAN) matches perceived location of sound. The current study used 13 different sound sources presented over loudspeakers for natural localization cues and measured event-related potentials to a threshold stimulus in a sound localization task. Preregistered Bayesian mixed models provided moderate evidence against an overall AAN and very strong evidence against its lateralization. Because of issues regarding data quantity and quality, exploratory analyses with aggregated data from multiple loudspeakers were conducted. Results provided moderate evidence for an overall AAN and strong evidence against its lateralization. Nonetheless, the interpretations of these results remain inconclusive. Therefore, future research should reduce the number of conditions and/or test over several sessions to procure a sufficient amount of data. Taken at face value, the results may suggest issues with AAN as an NCC of auditory awareness, as it does not laterally map onto experiences in a free-field auditory environment, in contrast to the NCCs of vision and touch.

Causal role of frontocentral beta oscillation in comprehending linguistic communicative functions

Linguistic communication is often considered as an action serving the function of conveying the speaker's goal to the addressee. Although neuroimaging studies have suggested a role of the motor system in comprehending communicative functions, the underlying mechanism is yet to be specified. Here, by two EEG experiments and a tACS experiment, we demonstrate that the frontocentral beta oscillation, which represents action states, plays a crucial part in linguistic communication understanding. Participants read scripts involving two interlocutors and rated the interlocutors' attitudes. Each script included a critical sentence said by the speaker expressing a context-dependent function of either promise, request, or reply to the addressee's query. These functions were behaviorally discriminated, with higher addressee's will rating for the promise than for the reply and higher speaker's will rating for the request than for the reply. EEG multivariate analyses showed that different communicative functions were represented by different patterns of the frontocentral beta activity but not by patterns of alpha activity. Further tACS results showed that, relative to alpha tACS and sham stimulation, beta tACS improved the predictability of communicative functions of request or reply, as measured by the speaker's will rating. These results convergently suggest a causal role of the frontocentral beta activities in comprehending linguistic communications.

Non-invasive brain-machine interface control with artificial intelligence copilots

Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3 × in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.

Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping

Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.

Machine learning explains response variability of deep brain stimulation on Parkinson's disease quality of life

Improving health-related quality of life (QoL) is crucial for managing Parkinson's disease. However, QoL outcomes after deep brain stimulation (DBS) of the subthalamic nucleus (STN) vary considerably. Current approaches lack integration of demographic, patient-reported, neuroimaging, and neurophysiological data to understand this variability. This study used explainable machine learning to analyze multimodal factors affecting QoL changes, measured by the Parkinson's Disease Questionnaire (PDQ-39) in 63 patients, and quantified each variable's contribution. Results showed that preoperative PDQ-39 scores and upper beta band activity (>20 Hz) in the left STN were key predictors of QoL changes. Lower initial QoL burden predicted worsening, while improvement was associated with higher beta activity. Additionally, electrode positions along the superior-inferior axis, especially relative to the z = -7 coordinate in standard space, influenced outcomes, with improved and worsened QoL above and below this marker. This study emphasizes a tailored, data-informed approach to optimize DBS treatment and improve patient QoL.

The association between social rewards and anxiety: Links from neurophysiological analysis in virtual reality and social interaction game

Individuals' affective experience can be intricate, influenced by various factors including monetary rewards and social factors during social interaction. However, within this array of factors, divergent evidence has been considered as potential contributors to social anxiety. To gain a better understanding of the specific factors associated with anxiety during social interaction, we combined a social interaction task with neurophysiological recordings obtained through an anxiety-elicitation task conducted in a Virtual Reality (VR) environment. Employing inter-subject representational similarity analysis (ISRSA), we explored the potential linkage between individuals' anxiety neural patterns and their affective experiences during social interaction. Our findings suggest that, after controlling for other factors, the influence of the partner's emotional cues on individuals' affective experiences is specifically linked to their neural pattern of anxiety. This indicates that the emergence of anxiety during social interaction may be particularly associated with the emotional cues provided by the social partner, rather than individuals' own reward or prediction errors during social interaction. These results provide further support for the cognitive theory of social anxiety and extend the application of VR in future cognitive and affective studies.

XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Action Planning Renders Objects in Working Memory More Attentionally Salient

A rapidly growing body of work suggests that visual working memory (VWM) is fundamentally action oriented. Consistent with this, we recently showed that attention is more strongly biased by VWM representations of objects when we plan to act on those objects in the future. Using EEG and eye tracking, here, we investigated neurophysiological correlates of the interactions between VWM and action. Participants (n = 36) memorized a shape for a subsequent VWM test. At test, a probe was presented along with a secondary object. In the action condition, participants gripped the actual probe if it matched the memorized shape, whereas in the control condition, they gripped the secondary object. Crucially, during the VWM delay, participants engaged in a visual selection task, in which they located a target as fast as possible. The memorized shape could either encircle the target (congruent trials) or a distractor (incongruent trials). Replicating previous findings, we found that eye gaze was biased toward the VWM-matching shape and, importantly, more so when the shape was directly associated with an action plan. Moreover, the ERP results revealed that during the selection task, future action-relevant VWM-matching shapes elicited (1) a stronger Ppc (posterior positivity contralateral), signaling greater attentional saliency; (2) an earlier PD (distractor positivity) component, suggesting faster suppression; (3) a larger inverse (i.e., positive) sustained posterior contralateral negativity in incongruent trials, consistent with stronger suppression of action-associated distractors; and (4) an enhanced response-locked positivity over left motor regions, possibly indicating enhanced inhibition of the response associated with the memorized item during the interim task. Overall, these results suggest that action planning renders objects in VWM more attentionally salient, supporting the notion of selection-for-action in working memory.

Impaired motor-to-sensory transformation mediates auditory hallucinations

Distinguishing reality from hallucinations requires efficient monitoring of agency. It has been hypothesized that a copy of motor signals, termed efference copy (EC) or corollary discharge (CD), suppresses sensory responses to yield a sense of agency; impairment of the inhibitory function leads to hallucinations. However, how can the sole absence of inhibition yield positive symptoms of hallucinations? We hypothesize that selective impairments in functionally distinct signals of CD and EC during motor-to-sensory transformation cause the positive symptoms of hallucinations. In an electroencephalography (EEG) experiment with a delayed articulation paradigm in schizophrenic patients with (AVHs) and without auditory verbal hallucinations (non-AVHs), we found that preparing to speak without knowing the contents (general preparation) did not suppress auditory responses in both patient groups, suggesting the absent of inhibitory function of CD. Whereas, preparing to speak a syllable (specific preparation) enhanced the auditory responses to the prepared syllable in non-AVHs, whereas AVHs showed enhancement in responses to unprepared syllables, opposite to the observations in the normal population, suggesting that the enhancement function of EC is not precise in AVHs. A computational model with a virtual lesion of an inhibitory inter-neuron and disproportional sensitization of auditory cortices fitted the empirical data and further quantified the distinct impairments in motor-to-sensory transformation in AVHs. These results suggest that "broken" CD plus "noisy" EC causes erroneous monitoring of the imprecise generation of internal auditory representation and yields auditory hallucinations. Specific impairments in functional granularity of motor-to-sensory transformation mediate positivity symptoms of agency abnormality in mental disorders.

Alterations in aperiodic and periodic EEG activity in young children with Down syndrome

Down syndrome (DS) is the most common cause of intellectual disability, yet little is known about the neurobiological pathways leading to cognitive impairments. Electroencephalographic (EEG) measures are commonly used to study neurodevelopmental disorders, but few studies have focused on young children with DS. Here we assess resting state EEG data collected from toddlers/preschoolers with DS (n = 29, age 13-48 months old) and compare their aperiodic and periodic EEG features with both age-matched (n = 29) and developmental-matched (n = 58) comparison groups. DS participants exhibited significantly reduced aperiodic slope, increased periodic theta power, and decreased alpha peak amplitude. A majority of DS participants displayed a prominent peak in the theta range, whereas a theta peak was not present in age-matched participants. Overall, similar findings were also observed when comparing DS and developmental-matched groups, suggesting that EEG differences are not explained by delayed cognitive ability.

Neural mechanisms underlying competition-induced optimal decisions in individuals with high entrepreneurial intention

In a rapidly changing and uncertain business environment, individuals with high entrepreneurial intention (HEI) inevitably need to compete or cooperate with others to maximize their gains. However, the effects of competition and cooperation on the risky decision-making and neural mechanisms of individuals with HEI are not clear. By combining the modified Devil Task and electroencephalogram (EEG) technology, the current study showed that a competition context is more likely to motivate optimal decisions and enhance the total decision gains for individuals with HEI than a cooperation context. A positive relationship between the frequency of optimal decisions and the total gains of decision-making for individuals with HEI was also found, and this relationship was mediated by the degree of entrepreneurial intention. The EEG results showed that individuals with HEI made decisions in the competition context with greater P2 amplitude of frontal regions than in the cooperation context, and source localization analyses revealed that this difference in brain activity was manifested in the medial prefrontal cortex. Finally, the results revealed a positive relationship between the P2 amplitude and the degree of entrepreneurial intention of individuals with HEI. Overall, the study suggests that competition is an effective way to motivate individuals with HEI to make optimal decisions and, thus, maximize their profits, providing new perspectives on ways to promote successful entrepreneurship.

Significance of EEG-electrode combinations while calculating filters with common spatial patterns

Objective

Common spatial pattern (CSP) is a common filter technique used for pre-processing of electroencephalography (EEG) signals for imaginary movement classification tasks. It is crucial to reduce the amount of features especially in cases where few data is available. Therefore, different approaches to reduce the amount of electrodes used for CSP calculation are tried in this research.

Methods

Freely available EEG datasets are used for the evaluation. To evaluate the approaches a simple classification pipeline consisting mainly of the CSP calculation and linear discriminant analysis for classification is used. A baseline over all electrodes is calculated and compared against the results of the approaches.

Results

The most promising approach is to use the ability of CSP to provide information about the origin of the created filter. An algorithm that extracts the important electrodes from the CSP utilizing these information is proposed.The results show that using subject specific electrode positions has a positive impact on accuracy for the classification task. Further, it is shown that good performing electrode combinations in one session are not necessarily good performing electrodes in another session of the same subject. In addition to the combinations calculated using the developed algorithm, 26 additional electrode combinations are proposed. These can be taken into account when selecting well-performing electrode combinations. In this research we could achieve an accuracy improvement of over 10%.

Conclusions

Carefully selecting the correct electrode combination can improve accuracy for classifying an imaginary movement task.

Long-term impact of maternal prenatal smoking on EEG brain activity and internalizing/externalizing problem symptoms in young adults

OBJECTIVE

The objective of this study was to investigate the impact of smoking during pregnancy on the development of the child. While previous research has established its detrimental effects during early childhood, understanding potential long-term consequences into adulthood remains limited. This study specifically aimed to explore the influence of prenatal smoking exposure on brain activity and whether internalizing and externalizing symptoms are influenced by prenatal smoking exposure in a cohort of young adults.

METHODS

Utilizing data from 176 participants (mean age M = 24.68, SD = 0.49) and their mothers enrolled in a longitudinal risk study (MARS), we employed Generalized Additive Mixed Models (GAMMs) to analyze electroencephalography (EEG) power at rest and behavioral outcomes derived from the Young Adult-Self-Report (YASR) scales. Both covariate-unadjusted and -adjusted models were used, taking into account participant variables such as sex and age, as well as maternal factors like psychopathology and alcohol consumption, in addition to smoking and alcohol intake by the participants themselves.

RESULTS

The study revealed a significant impact of prenatal smoking on delta and theta band power, indicating decreased slower brain activity in prenatally exposed individuals compared to unexposed counterparts. Additionally, individuals exposed to prenatal smoking exhibited significantly higher levels of externalizing behavior. While this association was strongly influenced by maternal psychopathology, the child's gender, and the child's own substance use, the effect on delta power band remained after adjusting for covariates.

CONCLUSION

The findings suggest that prenatal smoking exposure may have enduring effects on brain activity patterns in young adulthood. Conversely, the influence on externalizing behaviors depended on familial factors (maternal psychopathology) and the lifestyle of the individual (substance use).

Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings

Objective.Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance.Approach.We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships.Main results.The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU.Significance.The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.

Validating a novel paradigm for simultaneously assessing mismatch response and frequency-following response to speech sounds

BACKGROUND

Speech sounds are processed in the human brain through intricate and interconnected cortical and subcortical structures. Two neural signatures, one largely from cortical sources (mismatch response, MMR) and one largely from subcortical sources (frequency-following response, FFR) are critical for assessing speech processing as they both show sensitivity to high-level linguistic information. However, there are distinct prerequisites for recording MMR and FFR, making them difficult to acquire simultaneously NEW METHOD: Using a new paradigm, our study aims to concurrently capture both signals and test them against the following criteria: (1) replicating the effect that the MMR to a native speech contrast significantly differs from the MMR to a nonnative speech contrast, and (2) demonstrating that FFRs to three speech sounds can be reliably differentiated.

RESULTS

Using EEG from 18 adults, we observed a decoding accuracy of 72.2 % between the MMR to native vs. nonnative speech contrasts. A significantly larger native MMR was shown in the expected time window. Similarly, a significant decoding accuracy of 79.6 % was found for FFR. A high stimulus-to-response cross-correlation with a 9 ms lag suggested that FFR closely tracks speech sounds.

COMPARISON WITH EXISTING METHOD(S)

These findings demonstrate that our paradigm reliably captures both MMR and FFR concurrently, replicating and extending past research with much fewer trials (MMR: 50 trials; FFR: 200 trials) and shorter experiment time (12 minutes).

CONCLUSIONS

This study paves the way to understanding cortical-subcortical interactions for speech and language processing, with the ultimate goal of developing an assessment tool specific to early development.

Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing

Electroencephalography (EEG) is a medical engineering technique aimed at recording the electric activity of the human brain. Brain signals derived from an EEG device can be processed and analyzed through computers by using digital signal processing, computational statistics, and machine learning techniques, that can lead to scientifically-relevant results and outcomes about how the brain works. In the last decades, the spread of EEG devices and the higher availability of EEG data, of computational resources, and of software packages for electroencephalography analysis has made EEG signal processing easier and faster to perform for any researcher worldwide. This increased ease to carry out computational analyses of EEG data, however, has made it easier to make mistakes, as well. And these mistakes, if unnoticed or treated wrongly, can in turn lead to wrong results or misleading outcomes, with worrisome consequences for patients and for the advancements of the knowledge about human brain. To tackle this problem, we present here our ten quick tips to perform electroencephalography signal processing analyses avoiding common mistakes: a short list of guidelines designed for beginners on what to do, how to do it, and what not to do when analyzing EEG data with a computer. We believe that following our quick recommendations can lead to better, more reliable and more robust results and outcome in clinical neuroscientific research.

No Evidence of Musical Training Influencing the Cortical Contribution to the Speech-Frequency-Following Response and Its Modulation through Selective Attention

Musicians can have better abilities to understand speech in adverse condition such as background noise than non-musicians. However, the neural mechanisms behind such enhanced behavioral performances remain largely unclear. Studies have found that the subcortical frequency-following response to the fundamental frequency of speech and its higher harmonics (speech-FFR) may be involved since it is larger in people with musical training than in those without. Recent research has shown that the speech-FFR consists of a cortical contribution in addition to the subcortical sources. Both the subcortical and the cortical contribution are modulated by selective attention to one of two competing speakers. However, it is unknown whether the strength of the cortical contribution to the speech-FFR, or its attention modulation, is influenced by musical training. Here we investigate these issues through magnetoencephalographic (MEG) recordings of 52 subjects (18 musicians, 25 non-musicians, and 9 neutral participants) listening to two competing male speakers while selectively attending one of them. The speech-in-noise comprehension abilities of the participants were not assessed. We find that musicians and non-musicians display comparable cortical speech-FFRs and additionally exhibit similar subject-to-subject variability in the response. Furthermore, we also do not observe a difference in the modulation of the neural response through selective attention between musicians and non-musicians. Moreover, when assessing whether the cortical speech-FFRs are influenced by particular aspects of musical training, no significant effects emerged. Taken together, we did not find any effect of musical training on the cortical speech-FFR.

Aperiodic activity differences in individuals with high and low temporal processing efficiency

It is known that Temporal Information Processing (TIP) underpins our cognitive functioning. Previous research has focused on the relationship between TIP efficiency and oscillatory brain activity, especially the gamma rhythm; however, non-oscillatory (aperiodic or 1/f) brain activity has often been missed. Recent studies have identified the 1/f component as being important for the functioning of the brain. Therefore, the current study aimed to verify whether TIP efficiency is associated with specific EEG resting state cortical activity patterns, including oscillatory and non-oscillatory (aperiodic) brain activities. To measure individual TIP efficiency, we used two behavioral tasks in which the participant judges the order of two sounds separated by millisecond intervals. Based on the above procedure, participants were classified into two groups with high and low TIP efficiency. Using cluster-based permutation analyses, we examined between-group differences in oscillatory and non-oscillatory (aperiodic) components across the 1-90 Hz range. The results revealed that the groups differed in the aperiodic component across the 30-80 Hz range in fronto-central topography. In other words, participants with low TIP efficiency exhibited higher levels of aperiodic activity, and thus a flatter frequency spectrum compared to those with high TIP efficiency. We conclude that participants with low TIP efficiency display higher levels of 'neural noise', which is associated with poorer quality and speed of neural processing.

Long-term night-shift work is associated with accelerates brain aging and worsens N3 sleep in female nurses

BACKGROUND

Shift work disrupts circadian rhythms and alters sleep patterns, resulting in various health problems. To quantitatively assess the impact of shift work on brain health, we evaluated the brain age index (BAI) derived from sleep electroencephalography (EEG) results in night-shift workers and compared it with that in daytime workers.

METHODS

We studied 45 female night shift nurses (mean age: 28.2 ± 3.3 years) and 44 female daytime workers (30.5 ± 4.7 years). Sleep EEG data were analyzed to calculate BAI. The BAI of night shift workers who were asleep during the daytime with those of daytime workers who were asleep at night were statistically compared to explore associations between BAI, duration of shift work, and sleep quality.

RESULTS

Night-shift workers exhibited significantly higher BAI (2.14 ± 6.04 vs. 0 ± 5.35), suggesting accelerated brain aging and altered sleep architecture, including reduced delta and sigma wave frequency activity during non-rapid eye movement sleep than daytime workers. Furthermore, poor deep sleep quality, indicated by a higher percentage of N1, lower percentage of N3, and higher arousal index, was associated with increased BAI among shift workers. Additionally, a longer duration of night-shift work was correlated with increased BAI, particularly in older shift workers.

CONCLUSION

Night-shift work, especially over extended periods, may be associated with accelerated brain aging, as indicated by higher BAI and alterations in sleep architecture. Interventions are necessary to mitigate the health impacts of shift work. Further research on the long-term effects and potential strategies for sleep improvement and mitigating brain aging in shift workers is warranted.

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BACKGROUND

Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined.

NEW METHOD

We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared.

RESULTS

Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters.

COMPARISON WITH EXISTING METHODS

Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation.

CONCLUSIONS

Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

Neural indices of heritable impulsivity: Impact of the COMT Val158Met polymorphism on frontal beta power during early motor preparation

Studies of COMT Val158Met suggest that the neural circuitry subserving inhibitory control may be modulated by this functional polymorphism altering cortical dopamine availability, thus giving rise to heritable differences in behaviors. Using an anatomically-constrained magnetoencephalography method and stratifying the sample by COMT genotype, from a larger sample of 153 subjects, we examined the spatial and temporal dynamics of beta oscillations during motor execution and inhibition in 21 healthy Met158/Met158 (high dopamine) or 21 Val158/Val158 (low dopamine) genotype individuals during a Go/NoGo paradigm. While task performance was unaffected, Met158 homozygotes demonstrated an overall increase in beta power across regions essential for inhibitory control during early motor preparation (∼100 ms latency), suggestive of a global motor "pause" on behavior. This increase was especially evident on Go trials with slow response speed and was absent during inhibition failures. Such a pause could underlie the tendency of Met158 allele carriers to be more cautious and inhibited. In contrast, Val158 homozygotes exhibited a beta drop during early motor preparation, indicative of high response readiness. This decrease was associated with measures of behavioral disinhibition and consistent with greater extraversion and impulsivity observed in Val homozygotes. These results provide mechanistic insight into genetically-determined interindividual differences of inhibitory control with higher cortical dopamine associated with momentary response hesitation, and lower dopamine leading to motor impulsivity.

A survey on integral equations for bioelectric modeling

Bioelectric modeling problems, such as electroencephalography, magnetoencephalography, transcranial electrical stimulation, deep brain stimulation, and transcranial magnetic stimulation, among others, can be approached through the formulation and resolution of integral equations of theboundary element method(BEM). Recently, it has been realized that thecharge-based formulationof the BEM is naturally well-suited for the application of thefast multipole method(FMM). The FMM is a powerful algorithm for the computation of many-body interactions and is widely applied in electromagnetic modeling problems. With the introduction of BEM-FMM in the context of bioelectromagnetism, the BEM can now compete with thefinite element method(FEM) in a number of application cases. This survey has two goals: first, to give a modern account of the main BEM formulations in the literature and their integration with FMM, directed to general researchers involved in development of BEM software for bioelectromagnetic applications. Second, to survey different techniques and available software, and to contrast different BEM and FEM approaches. As a new contribution, we showcase that the charge-based formulation is dual to the more common surface potential formulation.

Comorbidity of concussion and depression alters brain functional connectivity in collegiate student-athletes

Depression and concussion are highly prevalent neuropsychological disorders that often occur simultaneously. However, due to the high degree of symptom overlap between the two events, including but not limited to headache, sleep disturbances, appetite changes, fatigue, and difficulty concentrating, they may be treated in isolation. Thus, clinical awareness of additive symptom load may be missed. This study measures neuropsychological and electroencephalography (EEG) alpha band coherence differences in collegiate student-athletes with history of comorbid depression and concussion, in comparison to those with a single morbidity and healthy controls (HC). 35 collegiate athletes completed neuropsychological screenings and EEG measures. Participants were grouped by concussion and depression history. Differences in alpha band coherence were calculated using two-way ANOVA with post hoc correction for multiple comparisons. Comorbid participants scored significantly worse on neuropsychological screening, BDI-FS, and PCSS than those with a single morbidity and HC. Two-way ANOVA by group revealed significant main effects of alpha band coherence for concussion, depression, and their interaction term. Post-hoc analysis showed that comorbid participants had more abnormal alpha band coherence than single morbidity, when compared to HC. Comorbidity of concussion and depression increased symptom reporting and revealed more altered alpha band coherence than single morbidity, compared to HC. The abnormalities of the comorbid group exclusively showed decreased alpha band coherence in comparison to healthy controls. The comorbidity of depression and SRC has a compounding effect on depression symptoms, post-concussion symptoms, and brain functional connectivity. This research demonstrates a promising objective measure in comorbid individuals, previously only measured via subjective symptom reporting.

An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain-computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Dissociating prosodic from syntactic delta activity during natural speech comprehension

Decoding human speech requires the brain to segment the incoming acoustic signal into meaningful linguistic units, ranging from syllables and words to phrases. Integrating these linguistic constituents into a coherent percept sets the root of compositional meaning and hence understanding. One important cue for segmentation in natural speech is prosodic cues, such as pauses, but their interplay with higher-level linguistic processing is still unknown. Here, we dissociate the neural tracking of prosodic pauses from the segmentation of multi-word chunks using magnetoencephalography (MEG). We find that manipulating the regularity of pauses disrupts slow speech-brain tracking bilaterally in auditory areas (below 2 Hz) and in turn increases left-lateralized coherence of higher-frequency auditory activity at speech onsets (around 25-45 Hz). Critically, we also find that multi-word chunks-defined as short, coherent bundles of inter-word dependencies-are processed through the rhythmic fluctuations of low-frequency activity (below 2 Hz) bilaterally and independently of prosodic cues. Importantly, low-frequency alignment at chunk onsets increases the accuracy of an encoding model in bilateral auditory and frontal areas while controlling for the effect of acoustics. Our findings provide novel insights into the neural basis of speech perception, demonstrating that both acoustic features (prosodic cues) and abstract linguistic processing at the multi-word timescale are underpinned independently by low-frequency electrophysiological brain activity in the delta frequency range.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

INTRODUCTION

High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets.

FINDINGS

Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software.

CONCLUSIONS

NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention

Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective attention can be decoded from a single trial of electroencephalography (EEG) data. In this study, leveraging the source-reconstructed and anatomically-resolved EEG data as inputs, we sought to employ CNN as an interpretable model to uncover task-specific interactions between brain regions, rather than simply to utilize it as a black box decoder. To this end, our CNN model was specifically designed to learn pairwise interaction representations for 10 cortical regions from five-second inputs. By exclusively utilizing these features for decoding, our model was able to attain a median accuracy of 77.56% for within-participant and 65.14% for cross-participant classification. Through ablation analysis together with dissecting the features of the models and applying cluster analysis, we were able to discern the presence of alpha-band-dominated inter-hemisphere interactions, as well as alpha- and beta-band dominant interactions that were either hemisphere-specific or were characterized by a contrasting pattern between the right and left hemispheres. These interactions were more pronounced in parietal and central regions for within-participant decoding, but in parietal, central, and partly frontal regions for cross-participant decoding. These findings demonstrate that our CNN model can effectively utilize features known to be important in auditory attention tasks and suggest that the application of domain knowledge inspired CNNs on source-reconstructed EEG data can offer a novel computational framework for studying task-relevant brain interactions.

Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals

BACKGROUND AND OBJECTIVE

Multiple sclerosis (MS) is a neurodegenerative autoimmune disease affecting the central nervous system, leading to various neurological symptoms. Early detection is paramount to prevent enduring damage during MS episodes. Although magnetic resonance imaging (MRI) is a common diagnostic tool, this study aims to explore the feasibility of using electroencephalography (EEG) signals for MS detection, considering their accessibility and ease of application compared to MRI.

METHODS

The study involved the analysis of EEG signals during rest from 17 MS patients and 27 healthy volunteers to investigate MS-healthy patterns. Power spectral density features (PSD) were extracted from the 32-channel EEG signals. The study employed Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Classification and Regression Trees (CART), and k-Nearest Neighbor (kNN) classifiers to identify channels with the highest accuracy. Notably, the study achieved 100% accuracy in MS detection using the "Fp1" and "Pz" channels with the LDA classifier. A statistical analysis, utilizing the independent sample t-test, was conducted to explore whether PSD features of these channels differed significantly between healthy individuals and those with MS.

RESULTS

The results of the study demonstrate that effective detection of MS can be achieved using PSD features from only two channels of the EEG signal. Specifically, the "Fp1" and "Pz" channels exhibited 100% accuracy in MS detection with the LDA classifier. The statistical analysis further explored and confirmed the significant differences in PSD features between healthy individuals and MS patients.

CONCLUSION

The study concludes that the proposed method, utilizing PSD features from specific EEG channels, offers a straightforward and efficient diagnostic approach for the effective detection of MS. The findings suggest the potential utility of EEG signals as a non-invasive and accessible alternative for MS detection, highlighting the importance of further research in this direction.

A Novel Time-Frequency Parameterization Method for Oscillations in Specific Frequency Bands and Its Application on OPM-MEG

Time-frequency parameterization for oscillations in specific frequency bands reflects the dynamic changes in the brain. It is related to cognitive behavior and diseases and has received significant attention in neuroscience. However, many studies do not consider the impact of the aperiodic noise and neural activity, including their time-varying fluctuations. Some studies are limited by the low resolution of the time-frequency spectrum and parameter-solved operation. Therefore, this paper proposes super-resolution time-frequency periodic parameterization of (transient) oscillation (STPPTO). STPPTO obtains a super-resolution time-frequency spectrum with Superlet transform. Then, the time-frequency representation of oscillations is obtained by removing the aperiodic component fitted in a time-resolved way. Finally, the definition of transient events is used to parameterize oscillations. The performance of this method is validated on simulated data and its reliability is demonstrated on magnetoencephalography. We show how it can be used to explore and analyze oscillatory activity under rhythmic stimulation.

Speech and music recruit frequency-specific distributed and overlapping cortical networks

To what extent does speech and music processing rely on domain-specific and domain-general neural networks? Using whole-brain intracranial EEG recordings in 18 epilepsy patients listening to natural, continuous speech or music, we investigated the presence of frequency-specific and network-level brain activity. We combined it with a statistical approach in which a clear operational distinction is made between shared, preferred, and domain-selective neural responses. We show that the majority of focal and network-level neural activity is shared between speech and music processing. Our data also reveal an absence of anatomical regional selectivity. Instead, domain-selective neural responses are restricted to distributed and frequency-specific coherent oscillations, typical of spectral fingerprints. Our work highlights the importance of considering natural stimuli and brain dynamics in their full complexity to map cognitive and brain functions.

Emotion brain network topology in healthy subjects following passive listening to different auditory stimuli

A large body of research establishes the efficacy of musical intervention in many aspects of physical, cognitive, communication, social, and emotional rehabilitation. However, the underlying neural mechanisms for musical therapy remain elusive. This study aimed to investigate the potential neural correlates of musical therapy, focusing on the changes in the topology of emotion brain network. To this end, a Bayesian statistical approach and a cross-over experimental design were employed together with two resting-state magnetoencephalography (MEG) as controls. MEG recordings of 30 healthy subjects were acquired while listening to five auditory stimuli in random order. Two resting-state MEG recordings of each subject were obtained, one prior to the first stimulus (pre) and one after the final stimulus (post). Time series at the level of brain regions were estimated using depth-weighted minimum norm estimation (wMNE) source reconstruction method and the functional connectivity between these regions were computed. The resultant connectivity matrices were used to derive two topological network measures: transitivity and global efficiency which are important in gauging the functional segregation and integration of brain network respectively. The differences in these measures between pre- and post-stimuli resting MEG were set as the equivalence regions. We found that the network measures under all auditory stimuli were equivalent to the resting state network measures in all frequency bands, indicating that the topology of the functional brain network associated with emotional regulation in healthy subjects remains unchanged following these auditory stimuli. This suggests that changes in the emotion network topology may not be the underlying neural mechanism of musical therapy. Nonetheless, further studies are required to explore the neural mechanisms of musical interventions especially in the populations with neuropsychiatric disorders.

Behavioral, Functional Imaging, and Neurophysiological Outcomes of Transcranial Direct Current Stimulation and Speech-Language Therapy in an Individual with Aphasia

Speech-language therapy (SLT) is the most effective technique to improve language performance in persons with aphasia. However, residual language impairments remain even after intensive SLT. Recent studies suggest that combining transcranial direct current stimulation (tDCS) with SLT may improve language performance in persons with aphasia. However, our understanding of how tDCS and SLT impact brain and behavioral relation in aphasia is poorly understood. We investigated the impact of tDCS and SLT on a behavioral measure of scripted conversation and on functional connectivity assessed with multiple methods, both resting-state functional magnetic resonance imaging (rs-fMRI) and resting-state electroencephalography (rs-EEG). An individual with aphasia received 15 sessions of 20-min cathodal tDCS to the right angular gyrus concurrent with 40 min of SLT. Performance during scripted conversation was measured three times at baseline, twice immediately post-treatment, and at 4- and 8-weeks post-treatment. rs-fMRI was measured pre-and post-3-weeks of treatment. rs-EEG was measured on treatment days 1, 5, 10, and 15. Results show that both communication performance and left hemisphere functional connectivity may improve after concurrent tDCS and SLT. Results are in line with aphasia models of language recovery that posit a beneficial role of left hemisphere perilesional areas in language recovery.

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG

The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity between EEG and MEG in measuring radial and tangential cortical sources, combined EEG/MEG is considered beneficial in improving the reconstruction performance of ESI algorithms. Traditional algorithms mainly emphasize incorporating predesigned neurophysiological priors to solve the ESI problem. Deep learning frameworks aim to directly learn the mapping from scalp EEG/MEG measurements to the underlying brain source activities in a data-driven manner, demonstrating superior performance compared to traditional methods. However, most of the existing deep learning approaches for the ESI problem are performed on a single modality of EEG or MEG, meaning the complementarity of these two modalities has not been fully utilized. How to fuse the EEG and MEG in a more principled manner under the deep learning paradigm remains a challenging question. This study develops a Multi-Modal Deep Fusion (MMDF) framework using Attention Neural Networks (ANN) to fully leverage the complementary information between EEG and MEG for solving the ESI inverse problem, which is termed as MMDF-ANN. Specifically, our proposed brain source imaging approach consists of four phases, including feature extraction, weight generation, deep feature fusion, and source mapping. Our experimental results on both synthetic dataset and real dataset demonstrated that using a fusion of EEG and MEG can significantly improve the source localization accuracy compared to using a single-modality of EEG or MEG. Compared to the benchmark algorithms, MMDF-ANN demonstrated good stability when reconstructing sources with extended activation areas and situations of EEG/MEG measurements with a low signal-to-noise ratio.

Shared neural codes of recognition memory

Recognition memory research has identified several electrophysiological indicators of successful memory retrieval, known as old-new effects. These effects have been observed in different sensory domains using various stimulus types, but little attention has been given to their similarity or distinctiveness and the underlying processes they may share. Here, a data-driven approach was taken to investigate the temporal evolution of shared information content between different memory conditions using openly available EEG data from healthy human participants of both sexes, taken from six experiments. A test dataset involving personally highly familiar and unfamiliar faces was used. The results show that neural signals of recognition memory for face stimuli were highly generalized starting from around 200 ms following stimulus onset. When training was performed on non-face datasets, an early (around 200-300 ms) to late (post-400 ms) differentiation was observed over most regions of interest. Successful cross-classification for non-face stimuli (music and object/scene associations) was most pronounced in late period. Additionally, a striking dissociation was observed between familiar and remembered objects, with shared signals present only in the late window for correctly remembered objects, while cross-classification for familiar objects was successful in the early period as well. These findings suggest that late neural signals of memory retrieval generalize across sensory modalities and stimulus types, and the dissociation between familiar and remembered objects may provide insight into the underlying processes.

Investigating the impact of schizophrenia traits on attention: the role of the theta band in a modified Posner cueing paradigm

Joint attention is an indispensable tool for daily communication. Abnormalities in joint attention may be a key reason underlying social impairment in schizophrenia spectrum disorders. In this study, we aimed to explore the attentional orientation mechanism related to schizotypal traits in a social situation. Here, we employed a Posner cueing paradigm with social attentional cues. Subjects needed to detect the location of a target that is cued by gaze and head orientation. The power in the theta frequency band was used to examine the attentional process in the schizophrenia spectrum. There were four main findings. First, a significant association was found between schizotypal traits and attention orientation in response to invalid gaze cues. Second, individuals with schizotypal traits exhibited significant activation of neural oscillations and synchrony in the theta band, which correlated with their schizotypal tendencies. Third, neural oscillations and synchrony demonstrated a synergistic effect during social tasks, particularly when processing gaze cues. Finally, the relationship between schizotypal traits and attention orientation was mediated by neural oscillations and synchrony in the theta frequency band. These findings deepen our understanding of the impact of theta activity in schizotypal traits on joint attention and offer new insights for future intervention strategies.

Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes

OBJECTIVE

Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD).

METHODS

Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns.

RESULTS

PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity.

CONCLUSIONS

Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology.

SIGNIFICANCE

Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.

Spectrotemporal cortical dynamics and semantic control during sentence completion

OBJECTIVE

To investigate cortical oscillations during a sentence completion task (SC) using magnetoencephalography (MEG), focusing on the semantic control network (SCN), its leftward asymmetry, and the effects of semantic control load.

METHODS

Twenty right-handed adults underwent MEG while performing SC, consisting of low cloze (LC: multiple responses) and high cloze (HC: single response) stimuli. Spectrotemporal power modulations as event-related synchronizations (ERS) and desynchronizations (ERD) were analyzed: first, at the whole-brain level; second, in key SCN regions, posterior middle/inferior temporal gyri (pMTG/ITG) and inferior frontal gyri (IFG), under different semantic control loads.

RESULTS

Three cortical response patterns emerged: early (0-200 ms) theta-band occipital ERS; intermediate (200-700 ms) semantic network alpha/beta-band ERD; late (700-3000 ms) dorsal language stream alpha/beta/gamma-band ERD. Under high semantic control load (LC), pMTG/ITG showed prolonged left-sided engagement (ERD) and right-sided inhibition (ERS). Left IFG exhibited heightened late (2500-2550 ms) beta-band ERD with increased semantic control load (LC vs. HC).

CONCLUSIONS

SC involves distinct cortical responses and depends on the left IFG and asymmetric engagement of the pMTG/ITG for semantic control.

SIGNIFICANCE

Future use of SC in neuromagnetic preoperative language mapping and for understanding the pathophysiology of language disorders in neurological conditions.

Working memory processes and intrinsic motivation: An EEG study

Processes typically encompassed by working memory (WM) include encoding, retention, and retrieval of information. Previous research has demonstrated that motivation can influence WM performance, although the specific WM processes affected by motivation are not yet fully understood. In this study, we investigated the effects of motivation on different WM processes, examining how task difficulty modulates these effects. We hypothesized that motivation level and personality traits of the participants (N = 48, 32 females; mean age = 21) would modulate the parietal alpha and frontal theta electroencephalography (EEG) correlates of WM encoding, retention, and retrieval phases of the Sternberg task. This effect was expected to be more pronounced under conditions of very high task difficulty. We found that increasing difficulty led to reduced accuracy and increased response time, but no significant relationship was found between motivation and accuracy. However, EEG data revealed that motivation influenced WM processes, as indicated by changes in alpha and theta oscillations. Specifically, higher levels of the Resilience trait-associated with mental toughness, hardiness, self-efficacy, achievement motivation, and low anxiety-were related to increased alpha desynchronization during encoding and retrieval. Increased scores of Subjective Motivation to perform well in the task were related to enhanced frontal midline theta during retention. Additionally, these effects were significantly stronger under conditions of high difficulty. These findings provide insights into the specific WM processes that are influenced by motivation, and underscore the importance of considering both task difficulty and intrinsic motivation in WM research.

Reading instruction causes changes in category-selective visual cortex

Education sculpts specialized neural circuits for skills like reading that are critical to success in modern society but were not anticipated by the selective pressures of evolution. Does the emergence of brain regions that selectively process novel visual stimuli like words occur at the expense of cortical representations of other stimuli like faces and objects? "Neuronal Recycling" predicts that learning to read should enhance the response to words in ventral occipitotemporal cortex (VOTC) and decrease the response to other visual categories such as faces and objects. To test this hypothesis, and more broadly to understand the changes that are induced by the early stages of literacy instruction, we conducted a randomized controlled trial with pre-school children (five years of age). Children were randomly assigned to intervention programs focused on either reading skills or oral language skills and magnetoencephalography (MEG) data collected before and after the intervention was used to measure visual responses to images of text, faces, and objects. We found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of visual cortex, but that there was not a clear tradeoff between the response to words versus other categories. Within a predefined region of VOTC corresponding to the visual word form area (VWFA) we found that the relative amplitude of responses to text, faces, and objects changed, but increases in the response to words were not linked to decreases in the response to faces or objects. How these changes play out over a longer timescale is still unknown but, based on these data, we can surmise that high-level visual cortex undergoes rapid changes as children enter school and begin establishing new skills like literacy.

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals

Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention

A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion.

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100's to 1000's. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post-hoc manner from univariate analyses, or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgement in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as Oscillation Component Analysis (OCA). These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Fast and Accurate EEG/MEG BEM-Based Forward Problem Solution for High-Resolution Head Models

A BEM (boundary element method) based approach is developed to accurately solve an EEG/MEG forward problem for a modern high-resolution head model in approximately 60 seconds using a common workstation. The method utilizes a charge-based BEM with fast multipole acceleration (BEM-FMM) and a "smart" mesh pre-refinement (called b-refinement) close to the singular source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models in approximately 60 seconds after initial model assembly. The method is verified both theoretically and experimentally.

Multimodal fusion for anticipating human decision performance

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features. Additionally, our approach effectively utilizes a combination of EEG signals and image features to streamline the feature set required for the Random Forest Classifier (RFC) while maintaining high accuracy. The findings of this research hold substantial potential for developing advanced fault alert systems, particularly in critical decision-making environments such as the medical and defence sectors.

Reactive Inhibitory Control Precedes Overt Stuttering Events

Research points to neurofunctional differences underlying fluent speech between stutterers and non-stutterers. Considerably less work has focused on processes that underlie stuttered vs. fluent speech. Additionally, most of this research has focused on speech motor processes despite contributions from cognitive processes prior to the onset of stuttered speech. We used MEG to test the hypothesis that reactive inhibitory control is triggered prior to stuttered speech. Twenty-nine stutterers completed a delayed-response task that featured a cue (prior to a go cue) signaling the imminent requirement to produce a word that was either stuttered or fluent. Consistent with our hypothesis, we observed increased beta power likely emanating from the right pre-supplementary motor area (R-preSMA)-an area implicated in reactive inhibitory control-in response to the cue preceding stuttered vs. fluent productions. Beta power differences between stuttered and fluent trials correlated with stuttering severity and participants' percentage of trials stuttered increased exponentially with beta power in the R-preSMA. Trial-by-trial beta power modulations in the R-preSMA following the cue predicted whether a trial would be stuttered or fluent. Stuttered trials were also associated with delayed speech onset suggesting an overall slowing or freezing of the speech motor system that may be a consequence of inhibitory control. Post-hoc analyses revealed that independently generated anticipated words were associated with greater beta power and more stuttering than researcher-assisted anticipated words, pointing to a relationship between self-perceived likelihood of stuttering (i.e., anticipation) and inhibitory control. This work offers a neurocognitive account of stuttering by characterizing cognitive processes that precede overt stuttering events.

P3b correlates of inspection time

Both P3b and the inspection time (IT) are related with intelligence, yet the P3b correlates of IT are not well understood. This event-related potential study addressed this question by asking participants (N = 28) to perform an IT task. There were three IT conditions with different levels of discriminative stimulus duration, i.e., 33 ms, 67 ms, and 100 ms, and a control condition with no target presentation (0 ms condition). We also measured participants' processing speed with four Elementary Cognitive Tests (ECTs), including a Simple Reaction Time task (SRT), two Choice Reaction Time tasks (CRTs), and a Pattern Discrimination task (PD). Results revealed that an increase in P3b latency with longer duration of the discriminative stimulus. Moreover, the P3b latency was negatively correlated with the accuracy of the IT task in the 33 ms condition, but not evident in the 67 and 100 ms conditions. Furthermore, the P3b latency of the 33 ms condition was positively correlated with the RT of the SRT, but not related with the RTs of CRTs or PD. A significant main effect of duration on the amplitude of P1 was also found. We conclude that the present study provides the neurophysiological correlates of the IT task, and those who are able to accurately perceive and process very briefly presented stimuli have a higher speed of information process, reflected by the P3b latency, yet this relationship is more obvious in the most difficult condition. Combined, our results suggest that P3b is related with the closure of a perceptual epoch to form the neural representation of a stimulus, in support of the "context closure" hypothesis.