Autoreject: Automated artifact rejection for MEG and EEG data

Resting-state EEG correlates of sustained attention in healthy ageing: Cross-sectional findings from the LEISURE study

While structural and biochemical brain changes are well-documented in ageing, functional neuronal network differences, as indicated by electrophysiological markers, are less clear. Moreover, age-related changes in sustained attention and their associated electrophysiological correlates are still poorly understood. To address this, we analysed cross-sectional baseline electroencephalography (EEG) and cognitive data from the Lifestyle Intervention Study for Dementia Risk Reduction (LEISURE). Participants were 96 healthy older adults, aged 50-84. We examined resting-state EEG periodic (individual alpha frequency [IAF], aperiodic-adjusted individual alpha power [aIAP]) and aperiodic (exponent and offset) activity, and their associations with age and sustained attention. Results showed associations between older age and slower IAF, but not aIAP or global aperiodic exponent and offset. Additionally, hierarchical linear regression revealed that after controlling for demographic variables, faster IAF was associated with better Sustained Attention to Response Task performance, and mediation analysis confirmed IAF as a mediator between age and sustained attention performance. These findings indicate that IAF may be an important marker of ageing, and a slower IAF may signal diminished cognitive processing capacity for sustained attention in older adults.

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Do smaller P300 amplitudes in schizophrenia result from larger variability in temporal processing?

The P3b is a prominent event-related potential (ERP) with maximal amplitude between 250 ms and 500 ms after the onset of a rare target stimulus within a sequence of standard non-target stimuli (oddball paradigm). Several studies found reduced P3b amplitudes in patients with schizophrenia compared to neurotypicals. Our work and the literature suggest that temporal imprecision may play a large pathophysiological role in schizophrenia. Here, we investigated whether reduced P3b amplitudes result from reduced neural activity (power) or temporal imprecision (inter-trial phase coherence; ITC) in delta and theta bands, using two EEG datasets from different studies with different oddball paradigms (Study 1: 19 patients with schizophrenia and 17 matched controls, Study 2: 26 patients and 26 controls). Both studies revealed typical P3b ERP components with smaller amplitudes in patients. Reduced ITC in patients was found in the delta band, which correlated with P3b peak amplitudes for all participant groups (ρ = 0.58-0.89). In Study 1, we also found significant differences between patients and controls in ITC in the theta band, which also correlated with P3b peak amplitudes (patients' ρ = 0.64, controls' ρ = 0.54). This was not found in Study 2. The results indicate that P3b amplitude reduction in patients with schizophrenia is linked to a reduction in temporal precision of neural activity. These results expand the notion of imprecision in temporal processing at phenomenological, psychological, and neurological levels that have been related to disturbances of the sense of self. They confirm that temporal imprecision may be more important than the reduction of neural activity itself.

Nonictal electroencephalographic measures for the diagnosis of functional seizures

OBJECTIVE

Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians.

METHODS

The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches.

RESULTS

The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier.

SIGNIFICANCE

Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.

Neurocognitive dynamics of preparatory and adaptive cognitive control: Insights from mass-univariate and multivariate pattern analysis of EEG data

Cognitive control refers to humans' ability to willingly align thoughts and actions with internally represented goals. Research indicates that cognitive control is not one-dimensional but rather integrates multiple sub-processes to cope with task demands successfully. In particular, the dynamic interplay between preparatory (i.e., prior to goal-relevant events) and adaptive (i.e., in response to unexpected demands) recruitment of neural resources is believed to facilitate successful behavioural performance. However, whether preparatory and adaptive processes draw from independent or shared neural resources, and how these align in the information processing stream, remains unclear. To address these issues, we recorded electroencephalographic data from 52 subjects while they performed a computerised task. Using a combination of mass-univariate and multivariate pattern analysis procedures, we found that different types of control triggered distinct sequences of brain activation patterns, and that the order and temporal extent of these patterns were dictated by the type of control used by the participants. Stimuli that fostered preparatory recruitment of control evoked a sequence of transient occipital-parietal, sustained central-parietal, and sustained fronto-central responses. In contrast, stimuli that indicated the need for quick behavioural adjustments triggered a sequence of transient occipital-parietal, fronto-central, and central parietal responses. There was also a considerable degree of overlap in the temporal evolution of these brain activation patterns, with behavioural performance being mainly related to the magnitude of the central-parietal and fronto-central responses. Our results demonstrate how different neurocognitive mechanisms, such as early attentional allocation and subsequent behavioural selection processes, are likely to contribute to cognitive control. Moreover, our findings extend prior work by showing that these mechanisms are engaged (at least partly) in parallel, rather than independently of each other.

EEG-based emotional valence and emotion regulation classification: a data-centric and explainable approach

Emotion classification using electroencephalographic (EEG) data is a challenging task in the field of Artificial Intelligence. While many researchers have focused on finding the best model or feature extraction technique to achieve optimal results, few have attempted to select the best methodological steps for working with the dataset. In this study, we applied two different theoretical approaches based on the noise of the dataset: curriculum learning and confident learning. Curriculum learning involves presenting training examples to the model in a specific order, starting with easier examples and gradually increasing in difficulty. This approach has been shown to improve model performance. Confident learning is a method for identifying and correcting label errors in datasets. By identifying and correcting these errors, confident learning can improve the performance of machine learning models trained on noisy datasets. We then applied the Integrated Gradient technique in order to assess the explainability of each model. Our aim was to explore the impact of different models and methods on emotion classification performance using EEG data. We collected and used an EEG dataset in which participants rated the emotional valence of positive and negative pictures while performing an emotion regulation (ER) task, comparing a control condition (Look) with two ER strategies: cognitive reappraisal and expressive suppression. We performed a multilabel classification to identify emotional neutrality or polarization of emotional valence (both positive and negative) rated by participants and the emotion regulation strategy adopted during the task. We compared the performance of models trained on three datasets selected based on label noise and evaluated their suitability for this task. Our results suggest different patterns based on the architecture used for feature importance, highlighting both advantages and criticisms.

DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG

This paper introduces the DERCo (Dublin EEG-based Reading Experiment Corpus), a language resource combining electroencephalography (EEG) and next-word prediction data obtained from participants reading narrative texts. The dataset comprises behavioral data collected from 500 participants recruited through the Amazon Mechanical Turk online crowd-sourcing platform, along with EEG recordings from 22 healthy adult native English speakers. The online experiment was designed to examine the context-based word prediction by a large sample of participants, while the EEG-based experiment was developed to extend the validation of behavioral next-word predictability. Online participants were instructed to predict upcoming words and complete entire stories. Cloze probabilities were then calculated for each word so that this predictability measure could be used to support various analyses pertaining to semantic context effects in the EEG recordings. EEG-based analyses revealed significant differences between high and low predictable words, demonstrating one important type of potential analysis that necessitates close integration of these two datasets. This material is a valuable resource for researchers in neurolinguistics due to the word-level EEG recordings in context.

A network analysis of affective and motivational individual differences and error monitoring in a non-clinical sample

Error monitoring, which plays a crucial role in shaping adaptive behavior, is influenced by a complex interplay of affective and motivational factors. Understanding these associations often proves challenging due to the intricate nature of these variables. With the aim of addressing previous inconsistencies and methodological gaps, in this study, we utilized network analysis to investigate the relationship between affective and motivational individual differences and error monitoring. We employed six Gaussian Graphical Models on a non-clinical population ($N$ = 236) to examine the conditional dependence between the amplitude of response-related potentials (error-related negativity; correct-related negativity) and 29 self-report measures related to anxiety, depression, obsessive thoughts, compulsive behavior, and motivation while adjusting for covariates: age, handedness, and latency of error-related negativity and correct-related negativity. We then validated our results on an independent sample of 107 participants. Our findings revealed unique associations between error-related negativity amplitudes and specific traits. Notably, more pronounced error-related negativity amplitudes were associated with increased rumination and obsessing, and decreased reward sensitivity. Importantly, in our non-clinical sample, error-related negativity was not directly associated with trait anxiety. These results underscore the nuanced effects of affective and motivational traits on error processing in healthy population.

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.

EEG based depression detection by machine learning: Does inner or overt speech condition provide better biomarkers when using emotion words as experimental cues?

BACKGROUND

Objective diagnostic approaches need to be tested to enhance the efficacy of depression detection. Non-invasive EEG-based identification represents a promising area.

AIMS

The present EEG study addresses two central questions: 1) whether inner or overt speech condition result in higher diagnositc accuracy of depression detection; and 2) does the affective nature of the presented emotion words count in such diagnostic approach.

METHODS

A matched case-control sample consisting of 10 depressed subjects and 10 healthy controls was assessed. An EEG headcap containing 64 electrodes measured neural responses to experimental cues presented in the form of 15 different words that belonged to three emotional categories: neutral, positive, and negative. 120 experimental cues was presented for every participant, each containing an "inner speech" and an "overt speech" segment. An EEGNet neural network was utilized.

RESULTS

The highest diagnostic accuracy of the EEGNet model was observed in the case of the overt speech condition (i.e. 69.5%), while a an overall subject-wise accuracy of 80% was achieved by the model. Only a negligible difference in diagnostic accuracy could be found between aggregated emotion word categories, with the highest accuracy (i.e. 70.2%) associated with the presentation of positive emotion words. Model decision was primarily influenced by electrodes representing the regions of the left parietal, the left temporal lobe and the middle frontal areas.

CONCLUSIONS

While the generalizability of our results is limited by the small sample size and potentially uncontrolled confounders, depression was associated with sensitive and presumably network-like aspects of these brain areas, potentially implying a higher level of emotion regulation that increases primarily in open communication.

RELAX-Jr: An Automated Pre-Processing Pipeline for Developmental EEG Recordings

Automated EEG pre-processing pipelines provide several key advantages over traditional manual data cleaning approaches; primarily, they are less time-intensive and remove potential experimenter error/bias. Automated pipelines also require fewer technical expertise as they remove the need for manual artefact identification. We recently developed the fully automated Reduction of Electroencephalographic Artefacts (RELAX) pipeline and demonstrated its performance in cleaning EEG data recorded from adult populations. Here, we introduce the RELAX-Jr pipeline, which was adapted from RELAX and designed specifically for pre-processing of data collected from children. RELAX-Jr implements multi-channel Wiener filtering (MWF) and/or wavelet-enhanced independent component analysis (wICA) combined with the adjusted-ADJUST automated independent component classification algorithm to identify and reduce all artefacts using algorithms adapted to optimally identify artefacts in EEG recordings taken from children. Using a dataset of resting-state EEG recordings (N = 136) from children spanning early-to-middle childhood (4-12 years), we assessed the cleaning performance of RELAX-Jr using a range of metrics including signal-to-error ratio, artefact-to-residue ratio, ability to reduce blink and muscle contamination, and differences in estimates of alpha power between eyes-open and eyes-closed recordings. We also compared the performance of RELAX-Jr against four publicly available automated cleaning pipelines. We demonstrate that RELAX-Jr provides strong cleaning performance across a range of metrics, supporting its use as an effective and fully automated cleaning pipeline for neurodevelopmental EEG data.

Sensitive infant care tunes a frontotemporal interbrain network in adolescence

Caregiving plays a critical role in children's cognitive, emotional, and psychological well-being. In the current longitudinal study, we investigated the enduring effects of early maternal behavior on processes of interbrain synchrony in adolescence. Mother-infant naturalistic interactions were filmed when infants were 3-4 months old and interactions were coded for maternal sensitivity and intrusiveness with the Coding Interactive Behavior Manual. In early adolescence (Mean = 12.30, SD = 1.25), mother-adolescent interbrain synchrony was measured using hyperscanning EEG during a naturalistic interaction of positive valence. Guided by previous hyperscanning studies, we focused on interbrain connections within the right frontotemporal interbrain network. Results indicate that maternal sensitivity in early infancy was longitudinally associated with neural synchrony in the right interbrain frontotemporal network. Post-hoc comparisons highlighted enhancement of mother-adolescent frontal-frontal connectivity, a connection that has been implicated in parent-child social communication. In contrast, maternal intrusiveness in infancy was linked with attenuation of interbrain synchrony in the right interbrain frontotemporal network. Sensitivity and intrusiveness are key maternal social orientations that have shown to be individually stable in the mother-child relationship from infancy to adulthood and foreshadow children's positive and negative social-emotional outcomes, respectively. Our findings are the first to demonstrate that these two maternal orientations play a role in enhancing or attenuating the child's interbrain frontotemporal network, which sustains social communication and affiliation. Results suggest that the reported long-term impact of maternal sensitivity and intrusiveness may relate, in part, to its effects on tuning the child's brain to sociality.

Interoceptive signals shape the earliest markers and neural pathway to awareness at the visual threshold

Variations in interoceptive signals from the baroreceptors (BRs) across the cardiac and respiratory cycle can modulate cortical excitability and so affect awareness. It remains debated at what stages of processing they affect awareness-related event-related potentials (ERPs) in different sensory modalities. We investigated the influence of the cardiac (systole/diastole) and the respiratory (inhalation/exhalation) phase on awareness-related ERPs. Subjects discriminated visual threshold stimuli while their electroencephalogram, electrocardiogram, and respiration were simultaneously recorded. We compared ERPs and their intracranial generators for stimuli classified correctly with and without awareness as a function of the cardiac and respiratory phase. Cyclic variations of interoceptive signals from the BRs modulated both the earliest electrophysiological markers and the trajectory of brain activity when subjects became aware of the stimuli: an early sensory component (P1) was the earliest marker of awareness for low (diastole/inhalation) and a perceptual component (visual awareness negativity) for high (systole/exhalation) BR activity, indicating that BR signals interfere with the sensory processing of the visual input. Likewise, activity spread from the primary visceral cortex (posterior insula) to posterior parietal cortices during high and from associative interoceptive centers (anterior insula) to the prefrontal cortex during low BR activity. Consciousness is thereby resolved in cognitive/associative regions when BR is low and in perceptual centers when it is high. Our results suggest that cyclic fluctuations of BR signaling affect both the earliest markers of awareness and the brain processes underlying conscious awareness.

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.

Perceptual awareness of near-threshold tones scales gradually with auditory cortex activity and pupil dilation

Negative-going responses in sensory cortex co-vary with perceptual awareness of sensory stimuli. Given that this awareness negativity has also been observed for undetected stimuli, some have challenged its role for perception. To address this question, we combined magnetoencephalography, electroencephalography, and pupillometry to study how sustained attention and response criterion affect the auditory awareness negativity. Participants first detected distractor sounds and denied hearing task-irrelevant near-threshold tones, which evoked neither awareness negativity nor pupil dilation. These same tones evoked both responses when task-relevant, stronger for hit but also present for miss trials. Participants then rated their perception on a six-point scale to test whether response criterion explains the presence of these responses for miss trials. Decreasing perception ratings were associated with gradually reduced evoked responses, consistent with signal detection theory. These results support the concept of an awareness negativity that is modulated by attention but does not require a non-linear threshold mechanism.

Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification

Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with the potential to reduce the clinical burden and improve patient care. However, further research is required before they can be used in clinical settings, particularly regarding the impact of the number of training samples and model parameters on their testing error. To address this, we present a comprehensive study of the empirical scaling behaviour of ConvNets for EEG pathology classification. We analysed the testing error with increasing the training samples and model size for four different ConvNet architectures. The focus of our experiments is width scaling, and we have increased the number of parameters to up to 1.8 million. Our evaluation was based on two publicly available datasets: the Temple University Hospital (TUH) Abnormal EEG Corpus and the TUH Abnormal Expansion Balanced EEG Corpus, which together contain 10,707 training samples. The results show that the testing error follows a saturating power-law with both model and dataset size. This pattern is consistent across different datasets and ConvNet architectures. Furthermore, empirically observed accuracies saturate at 85%-87%, which may be due to an imperfect inter-rater agreement on the clinical labels. The empirical scaling behaviour of the test performance with dataset and model size has significant implications for deep EEG pathology classification research and practice. Our findings highlight the potential of deep ConvNets for high-performance EEG pathology classification, and the identified scaling relationships provide valuable recommendations for the advancement of automated EEG diagnostics.

Machine learning of brain-specific biomarkers from EEG

BACKGROUND

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS).

METHODS

We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses.

FINDINGS

Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals.

INTERPRETATION

We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML.

FUNDING

All authors have been working for F. Hoffmann-La Roche Ltd.

Pharmacological Elevation of Catecholamine Levels Improves Perceptual Decisions, But Not Metacognitive Insight

Perceptual decisions are often accompanied by a feeling of decision confidence. Where the parietal cortex is known for its crucial role in shaping such perceptual decisions, metacognitive evaluations are thought to additionally rely on the (pre)frontal cortex. Because of this supposed neural differentiation between these processes, perceptual and metacognitive decisions may be divergently affected by changes in internal (e.g., attention, arousal) and external (e.g., task and environmental demands) factors. Although intriguing, causal evidence for this hypothesis remains scarce. Here, we investigated the causal effect of two neuromodulatory systems on behavioral and neural measures of perceptual and metacognitive decision-making. Specifically, we pharmacologically elevated levels of catecholamines (with atomoxetine) and acetylcholine (with donepezil) in healthy adult human participants performing a visual discrimination task in which we gauged decision confidence, while electroencephalography was measured. Where cholinergic effects were not robust, catecholaminergic enhancement improved perceptual sensitivity, while at the same time leaving metacognitive sensitivity unaffected. Neurally, catecholaminergic elevation did not affect sensory representations of task-relevant visual stimuli but instead enhanced well-known decision signals measured over the centroparietal cortex, reflecting the accumulation of sensory evidence over time. Crucially, catecholaminergic enhancement concurrently impoverished neural markers measured over the frontal cortex linked to the formation of metacognitive evaluations. Enhanced catecholaminergic neuromodulation thus improves perceptual but not metacognitive decision-making.

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.

Early Electrophysiological Correlates of Perceptual Consciousness Are Affected by Both Exogenous and Endogenous Attention

It has been proposed that visual awareness negativity (VAN), which is an early ERP component, constitutes a neural correlate of visual consciousness that is independent of perceptual and cognitive mechanisms. In the present study, we investigated whether VAN is indeed a specific marker of phenomenal awareness or rather reflects the involvement of attention. To this end, we reanalyzed data collected in a previously published EEG experiment in which awareness of visual stimuli and two aspects that define attentional involvement, namely, the inherent saliency and task relevance of a stimulus, were manipulated orthogonally. During the experimental procedure, participants (n = 41) were presented with images of faces that were backward-masked or unmasked, fearful or neutral, and defined as task-relevant targets or task-irrelevant distractors. Single-trial ERP analysis revealed that VAN was highly dependent on attentional manipulations in the early time window (140-200 msec), up to the point that the effect of awareness was not observed for attentionally irrelevant stimuli (i.e., neutral faces presented as distractors). In the late time window (200-350 msec), VAN was present in all attentional conditions, but its amplitude was significantly higher in response to fearful faces and task-relevant face images than in response to neutral ones and task-irrelevant ones, respectively. In conclusion, we demonstrate that the amplitude of VAN is highly dependent on both exogenous (stimulus saliency) and endogenous attention (task requirements). Our results challenge the view that VAN constitutes an attention-independent correlate of phenomenal awareness.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Patients with Traumatic Brain Injury

BACKGROUND

Resting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI). We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in patients with TBI.

METHODS

This is a retrospective study of comatose patients with TBI who were admitted to a trauma center (October 2013 to January 2022). Demographics, basic clinical data, imaging characteristics, and EEGs were collected. We calculated the following using 10-min rsEEGs: power spectral density, permutation entropy (complexity measure), weighted symbolic mutual information (wSMI, global information sharing measure), Kolmogorov complexity (Kolcom, complexity measure), and heart-evoked potentials (the averaged EEG signal relative to the corresponding QRS complex on electrocardiography). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, and rsEEG data via a support vector machine.

RESULTS

We studied 113 of 134 (84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50 years, p = 0.01). Patients who recovered also had higher Kolcom (U = 1688, p = 0.01), increased beta power (U = 1,652 p = 0.003) with higher variability across channels (U = 1534, p = 0.034) and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04), and higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = 0.026; U = 1639, p = 0.024) than those who did not recover. The area under the receiver operating characteristic curve for rsEEG was higher than that for clinical data (using age, motor response, pupil reactivity) and higher than that for the Marshall computed tomography classification (0.69 vs. 0.66 vs. 0.56, respectively; p < 0.001).

CONCLUSIONS

We describe the rsEEG signature in recovery of consciousness prior to discharge in comatose patients with TBI. rsEEG measures performed modestly better than the clinical and imaging data in predicting recovery.

Reactivation strength during cued recall is modulated by graph distance within cognitive maps

Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alternatively, replayed sequentially as temporally separate instances. In the current study, participants learned associations between images that were embedded in a directed graph network and retained this information over a brief 8 min consolidation period. During a subsequent cued recall session, participants retrieved the learned information while undergoing magnetoencephalographic recording. Using a trained stimulus decoder, we found evidence for clustered reactivation of learned material. Reactivation strength of individual items during clustered reactivation decreased as a function of increasing graph distance, an ordering present solely for successful retrieval but not for retrieval failure. In line with previous research, we found evidence that sequential replay was dependent on retrieval performance and was most evident in low performers. The results provide evidence for distinct performance-dependent retrieval mechanisms, with graded clustered reactivation emerging as a plausible mechanism to search within abstract cognitive maps.

Exploring empathic engagement in immersive media: An EEG study on mu rhythm suppression in VR

This study investigates the influence of immersive media, particularly Virtual Reality (VR), on empathic responses, in comparison to traditional television (TV), using electroencephalography (EEG). We employed mu rhythm suppression as a measurable neural marker to gauge empathic engagement, as its increase generally signifies heightened empathic responses. Our findings exhibit a greater mu rhythm suppression in VR conditions compared to TV conditions, suggesting a potential enhancement in empathic responses with VR. Furthermore, our results revealed that the strength of empathic responses was not confined to specific actions depicted in the video clips, underscoring the possibility of broader implications. This research contributes to the ongoing discourse on the effects of different media environments on empathic engagement, particularly emphasizing the unique role of immersive technologies such as VR. It invites further investigation into how such technologies can shape and potentially enhance the empathic experience.

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.

Infants' brain responses to social interaction predict future language growth

In face-to-face interactions with infants, human adults exhibit a species-specific communicative signal. Adults present a distinctive "social ensemble": they use infant-directed speech (parentese), respond contingently to infants' actions and vocalizations, and react positively through mutual eye-gaze and smiling. Studies suggest that this social ensemble is essential for initial language learning. Our hypothesis is that the social ensemble attracts attentional systems to speech and that sensorimotor systems prepare infants to respond vocally, both of which advance language learning. Using infant magnetoencephalography (MEG), we measure 5-month-old infants' neural responses during live verbal face-to-face (F2F) interaction with an adult (social condition) and during a control (nonsocial condition) in which the adult turns away from the infant to speak to another person. Using a longitudinal design, we tested whether infants' brain responses to these conditions at 5 months of age predicted their language growth at five future time points. Brain areas involved in attention (right hemisphere inferior frontal, right hemisphere superior temporal, and right hemisphere inferior parietal) show significantly higher theta activity in the social versus nonsocial condition. Critical to theory, we found that infants' neural activity in response to F2F interaction in attentional and sensorimotor regions significantly predicted future language development into the third year of life, more than 2 years after the initial measurements. We develop a view of early language acquisition that underscores the centrality of the social ensemble, and we offer new insight into the neurobiological components that link infants' language learning to their early brain functioning during social interaction.

Machine learning reveals differential effects of depression and anxiety on reward and punishment processing

Recent studies suggest that depression and anxiety are associated with unique aspects of EEG responses to reward and punishment, respectively; also, abnormal responses to punishment in depressed individuals are related to anxiety, the symptoms of which are comorbid with depression. In a non-clinical sample, we aimed to investigate the relationships between reward processing and anxiety, between punishment processing and anxiety, between reward processing and depression, and between punishment processing and depression. Towards this aim, we separated feedback-related brain activity into delta and theta bands to isolate activity that indexes functionally distinct processes. Based on the delta/theta frequency and feedback valence, we then used machine learning (ML) to classify individuals with high severity of depressive symptoms and individuals with high severity of anxiety symptoms versus controls. The significant difference between the depression and control groups was driven mainly by delta activity; there were no differences between reward- and punishment-theta activities. The high severity of anxiety symptoms was marginally more strongly associated with the punishment- than the reward-theta feedback processing. The findings provide new insights into the differences in the impacts of anxiety and depression on reward and punishment processing; our study shows the utility of ML in testing brain-behavior hypotheses and emphasizes the joint effect of theta-RewP/FRN and delta frequency on feedback-related brain activity.

Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

Distinct social behavior and inter-brain connectivity in Dyads with autistic individuals

Autism Spectrum Disorder (ASD) is defined by distinctive socio-cognitive behaviors that deviate from typical patterns. Notably, social imitation skills appear to be particularly impacted, manifesting early on in development. This paper compared the behavior and inter-brain dynamics of dyads made up of two typically developing (TD) participants with mixed dyads made up of ASD and TD participants during social imitation tasks. By combining kinematics and EEG-hyperscanning, we show that individuals with ASD exhibited a preference for the follower rather than the lead role in imitating scenarios. Moreover, the study revealed inter-brain synchrony differences, with low-alpha inter-brain synchrony differentiating control and mixed dyads. The study's findings suggest the importance of studying interpersonal phenomena in dynamic and ecological settings and using hyperscanning methods to capture inter-brain dynamics during actual social interactions.

Gamma power and beta envelope correlation are potential neural predictors of deep hypnosis

Hypnosis is a psychological intervention that is commonly used to enhance the effectiveness of therapeutic suggestions. Despite extensive fascination and study, the neural mechanisms behind hypnosis remain elusive. In the current study, we undertook a systematic exploration of these neural correlates. We first extracted well-studied neurophysiological features from EEG sensors and source-localized data using spectral analysis and two measures of functional connectivity: weighted phase lag index (wPLI) and power envelope correlation (PEC). Next, we developed classification models that predicted self-rated hypnotic experience based on the extracted feature sets. Our findings reveal that gamma power computed on sensor-level data and beta PEC computed between source-localized brain networks are the top predictors of hypnosis depth. Further, a SHapley Additive exPlanations (SHAP) analysis suggested reduced gamma power in the midline frontal area and increased beta PEC between interhemispheric Dorsal Attention Networks (DAN) contribute to the hypnotic experience. These results broaden our understanding of the neural correlates of deep hypnosis, highlighting potential targets for future research. Moreover, this study demonstrates the potential of using predictive models in understanding the neural underpinnings of self-reported hypnotic depth, offering a template for future investigations.

Investigating critical brain area for EEG-based binocular color fusion and rivalry with EEGNet

Introduction

Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color.

Methods

This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects.

Results

By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual.

Discussion

The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).

Magnetoencephalography (MEG) Data Processing in Epilepsy Patients with Implanted Responsive Neurostimulation (RNS) Devices

Drug-resistant epilepsy (DRE) is often treated with surgery or neuromodulation. Specifically, responsive neurostimulation (RNS) is a widely used therapy that is programmed to detect abnormal brain activity and intervene with tailored stimulation. Despite the success of RNS, some patients require further interventions. However, having an RNS device in situ is a hindrance to the performance of neuroimaging techniques. Magnetoencephalography (MEG), a non-invasive neurophysiologic and functional imaging technique, aids epilepsy assessment and surgery planning. MEG performed post-RNS is complicated by signal distortions. This study proposes an independent component analysis (ICA)-based approach to enhance MEG signal quality, facilitating improved assessment for epilepsy patients with implanted RNS devices. Three epilepsy patients, two with RNS implants and one without, underwent MEG scans. Preprocessing included temporal signal space separation (tSSS) and an automated ICA-based approach with MNE-Python. Power spectral density (PSD) and signal-to-noise ratio (SNR) were analyzed, and MEG dipole analysis was conducted using single equivalent current dipole (SECD) modeling. The ICA-based noise removal preprocessing method substantially improved the signal-to-noise ratio (SNR) for MEG data from epilepsy patients with implanted RNS devices. Qualitative assessment confirmed enhanced signal readability and improved MEG dipole analysis. ICA-based processing markedly enhanced MEG data quality in RNS patients, emphasizing its clinical relevance.

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Generation WhatsApp: inter-brain synchrony during face-to-face and texting communication

Texting has become one of the most prevalent ways to interact socially, particularly among youth; however, the effects of text messaging on social brain functioning are unknown. Guided by the biobehavioral synchrony frame, this pre-registered study utilized hyperscanning EEG to evaluate interbrain synchrony during face-to-face versus texting interactions. Participants included 65 mother-adolescent dyads observed during face-to-face conversation compared to texting from different rooms. Results indicate that both face-to-face and texting communication elicit significant neural synchrony compared to surrogate data, demonstrating for the first time brain-to-brain synchrony during texting. Direct comparison between the two interactions highlighted 8 fronto-temporal interbrain links that were significantly stronger in the face-to-face interaction compared to texting. Our findings suggest that partners co-create a fronto-temporal network of inter-brain connections during live social exchanges. The degree of improvement in the partners' right-frontal-right-frontal connectivity from texting to the live social interaction correlated with greater behavioral synchrony, suggesting that this well-researched neural connection may be specific to face-to-face communication. Our findings suggest that while technology-based communication allows humans to synchronize from afar, face-to-face interactions remain the superior mode of communication for interpersonal connection. We conclude by discussing the potential benefits and drawbacks of the pervasive use of texting, particularly among youth.

Reliable and fast automatic artifact rejection of Long-Term EEG recordings based on Isolation Forest

Long-term electroencephalogram (Long-Term EEG) has the capacity to monitor over a long period, making it a valuable tool in medical institutions. However, due to the large volume of patient data, selecting clean data segments from raw Long-Term EEG for further analysis is an extremely time-consuming and labor-intensive task. Furthermore, the various actions of patients during recording make it difficult to use algorithms to denoise part of the EEG data, and thus lead to the rejection of these data. Therefore, tools for the quick rejection of heavily corrupted epochs in Long-Term EEG records are highly beneficial. In this paper, a new reliable and fast automatic artifact rejection method for Long-Term EEG based on Isolation Forest (IF) is proposed. Specifically, the IF algorithm is repetitively applied to detect outliers in the EEG data, and the boundary of inliers is promptly adjusted by using a statistical indicator to make the algorithm proceed in an iterative manner. The iteration is terminated when the distance metric between clean epochs and artifact-corrupted epochs remains unchanged. Six statistical indicators (i.e., min, max, median, mean, kurtosis, and skewness) are evaluated by setting them as centroid to adjust the boundary during iteration, and the proposed method is compared with several state-of-the-art methods on a retrospectively collected dataset. The experimental results indicate that utilizing the min value of data as the centroid yields the most optimal performance, and the proposed method is highly efficacious and reliable in the automatic artifact rejection of Long-Term EEG, as it significantly improves the overall data quality. Furthermore, the proposed method surpasses compared methods on most data segments with poor data quality, demonstrating its superior capacity to enhance the data quality of the heavily corrupted data. Besides, owing to the linear time complexity of IF, the proposed method is much faster than other methods, thus providing an advantage when dealing with extensive datasets.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Traumatic Brain Injury Patients

Background Resting-state electroencephalogram (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI) patients. We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in comatose TBI patients. Methods This is a retrospective study of comatose TBI patients who were admitted to a level-1 trauma center (10/2013-1/2022). Demographics, basic clinical data, imaging characteristics, and EEG data were collected. We calculated using 10-minute rsEEGs: power spectral density (PSD), permutation entropy (PE - complexity measure), weighted symbolic-mutual-information (wSMI - global information sharing measure), Kolmogorov complexity (Kolcom - complexity measure), and heart-evoked potentials (HEP - the averaged EEG signal relative to the corresponding QRS complex on electrocardiogram). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, rsEEG data via Support Vector Machine with a linear kernel (SVM). Results We studied 113 (out of 134, 84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50, p .01). Patients who recovered consciousness had higher Kolcom (U = 1688, p = 0.01,), increased beta power (U = 1652 p = 0.003), with higher variability across channels ( U = 1534, p = 0.034), and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04) and showed higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = .026, U = 1639, p = 0.024) than those who didn't recover. The ROC-AUC improved from 0.66 (using age, motor response, pupils' reactivity, and CT Marshall classification) to 0.69 (p < 0.001) when adding rsEEG measures. Conclusion We describe the rsEEG EEG signature in recovery of consciousness prior to discharge in comatose TBI patients. Resting-state EEG measures improved prediction beyond the clinical and imaging data.

Beta bursts question the ruling power for brain-computer interfaces

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is considered to be the borderline of cognitive changes associated with aging and very early dementia. Cognitive functions in MCI can improve, remain stable or progress to clinically probable AD. Quantitative electroencephalography (qEEG) can become a useful tool for using the analytical techniques to quantify EEG patterns indicating cognitive impairment.

OBJECTIVE

The aim of our study was to assess spectral and connectivity analysis of the EEG resting state activity in amnestic MCI (aMCI) patients in comparison with healthy control group (CogN).

METHODS

30 aMCI patients and 23 CogN group, matched by age and education, underwent equal neuropsychological assessment and EEG recording, according to the same protocol.

RESULTS

qEEG spectral analysis revealed decrease of global relative beta band power and increase of global relative theta and delta power in aMCI patients. Whereas, decreased coherence in centroparietal right area considered to be an early qEEG biomarker of functional disconnection of the brain network in aMCI patients. In conclusion, the demonstrated changes in qEEG, especially, the coherence patterns are specific biomarkers of cognitive impairment in aMCI.

CONCLUSIONS

Therefore, qEEG measurements appears to be a useful tool that complements neuropsychological diagnostics, assessing the risk of progression and provides a basis for possible interventions designed to improve cognitive functions or even inhibit the progression of the disease.

A role for retro-splenial cortex in the task-related P3 network

OBJECTIVE

The P3 is an event-related response observed in relation to task-relevant sensory events. Despite its ubiquitous presence, the neural generators of the P3 are controversial and not well identified.

METHODS

We compared source analysis of combined magneto- and electroencephalography (M/EEG) data with functional magnetic resonance imaging (fMRI) and simulation studies to better understand the sources of the P3 in an auditory oddball paradigm.

RESULTS

Our results suggest that the dominant source of the classical, postero-central P3 lies in the retro-splenial cortex of the ventral cingulate gyrus. A second P3 source in the anterior insular cortex contributes little to the postero-central maximum. Multiple other sources in the auditory, somatosensory, and anterior midcingulate cortex are active in an overlapping time window but can be functionally dissociated based on their activation time courses.

CONCLUSIONS

The retro-splenial cortex is a dominant source of the parietal P3 maximum in EEG.

SIGNIFICANCE

These results provide a new perspective for the interpretation of the extensive research based on the P3 response.

Pupil-linked arousal correlates with neural activity prior to sensorimotor decisions

Objective.Sensorimotor decisions require the brain to process external information and combine it with relevant knowledge prior to actions. In this study, we explore the neural predictors of motor actions in a novel, realistic driving task designed to study decisions while driving.Approach.Through a spatiospectral assessment of functional connectivity during the premotor period, we identified the organization of visual cortex regions of interest into a distinct scene processing network. Additionally, we identified a motor action selection network characterized by coherence between the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC).Main results.We show that steering behavior can be predicted from oscillatory power in the visual cortex, DLPFC, and ACC. Power during the premotor periods (specific to the theta and beta bands) correlates with pupil-linked arousal and saccade duration.Significance.We interpret our findings in the context of network-level correlations with saccade-related behavior and show that the DLPFC is a key node in arousal circuitry and in sensorimotor decisions.

Temporal differences and commonalities between hand and tool neural processing

Object recognition is a complex cognitive process that relies on how the brain organizes object-related information. While spatial principles have been extensively studied, less studied temporal dynamics may also offer valuable insights into this process, particularly when neural processing overlaps for different categories, as it is the case of the categories of hands and tools. Here we focus on the differences and/or similarities between the time-courses of hand and tool processing under electroencephalography (EEG). Using multivariate pattern analysis, we compared, for different time points, classification accuracy for images of hands or tools when compared to images of animals. We show that for particular time intervals (~ 136-156 ms and ~ 252-328 ms), classification accuracy for hands and for tools differs. Furthermore, we show that classifiers trained to differentiate between tools and animals generalize their learning to classification of hand stimuli between ~ 260-320 ms and ~ 376-500 ms after stimulus onset. Classifiers trained to distinguish between hands and animals, on the other hand, were able to extend their learning to the classification of tools at ~ 150 ms. These findings suggest variations in semantic features and domain-specific differences between the two categories, with later-stage similarities potentially related to shared action processing for hands and tools.

Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing

The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.

Effects of pupil size as manipulated through ipRGC activation on visual processing

The size of the eyes' pupils determines how much light enters the eye and also how well this light is focused. Through this route, pupil size shapes the earliest stages of visual processing. Yet causal effects of pupil size on vision are poorly understood and rarely studied. Here we introduce a new way to manipulate pupil size, which relies on activation of intrinsically photosensitive retinal ganglion cells (ipRGCs) to induce sustained pupil constriction. We report the effects of both experimentally induced and spontaneous changes in pupil size on visual processing as measured through EEG. We compare these to the effects of stimulus intensity and covert visual attention, because previous studies have shown that these factors all have comparable effects on some common measures of early visual processing, such as detection performance and steady-state visual evoked potentials; yet it is still unclear whether these are superficial similarities, or rather whether they reflect similar underlying processes. Using a mix of neural-network decoding, ERP analyses, and time-frequency analyses, we find that induced pupil size, spontaneous pupil size, stimulus intensity, and covert visual attention all affect EEG responses, mainly over occipital and parietal electrodes, but-crucially-that they do so in qualitatively different ways. Induced and spontaneous pupil-size changes mainly modulate activity patterns (but not overall power or intertrial coherence) in the high-frequency beta range; this may reflect an effect of pupil size on oculomotor activity and/ or visual processing. In addition, spontaneous (but not induced) pupil size tends to correlate positively with intertrial coherence in the alpha band; this may reflect a non-causal relationship, mediated by arousal. Taken together, our findings suggest that pupil size has qualitatively different effects on visual processing from stimulus intensity and covert visual attention. This shows that pupil size as manipulated through ipRGC activation strongly affects visual processing, and provides concrete starting points for further study of this important yet understudied earliest stage of visual processing.

Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence

GOAL AND AIMS

To evaluate an automatic sleep scoring algorithm against manual polysomnography sleep scoring.

FOCUS METHOD/TECHNOLOGY

Yet Another Spindle Algorithm automatic sleep staging algorithm.

REFERENCE METHOD/TECHNOLOGY

Manual sleep scoring.

SAMPLE

327 nights (151 healthy adolescents), from the NCANDA study.

DESIGN

Participants underwent one-to-three overnight polysomnography recordings, one consisting of an event-related-potential paradigm.

CORE ANALYTICS

Epoch by Epoch and discrepancy analyses (Bland Altman plots) were conducted on the overall sample.

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Epoch by Epoch and discrepancy analysis were repeated separately on standard polysomnography nights and event-related potential nights. Regression models were estimated on age, sex, scorer, and site of recording, separately on standard polysomnography nights and event-related potential nights.

CORE OUTCOMES

The Yet Another Spindle Algorithm sleep scoring algorithm's average sensitivity of 93.04% for Wake, 87.67% for N2, 84.46% for N3, 86.02% for rapid-eye-movement, and 40.39% for N1. Specificity was 96.75% for Wake, 97.31% for N1, 88.87% for N2, 97.99% for N3, and 97.70% for rapid-eye-movement. The Matthews Correlation Coefficient was highest in rapid-eye-movement sleep (0.85) while lowest in N1 (0.39). Cohen's Kappa mirrored Matthews Correlation Coefficient results. In Bland-Altman plots, the bias between Yet Another Spindle Algorithm and human scoring showed proportionality to the manual scoring measurement size.

IMPORTANT ADDITIONAL OUTCOMES

Yet Another Spindle Algorithm performance was reduced in event-related-potential/polysomnography nights for N3 and rapid-eye-movement. According to the Matthews Correlation Coefficient, the Yet Another Spindle Algorithm performance was affected by younger age, male sex, recording sites, and scorers.

CORE CONCLUSION

Results support the use of Yet Another Spindle Algorithm to score adolescents' polysomnography sleep records, possibly with classification outcomes supervised by an expert scorer.

Catecholaminergic neuromodulation and selective attention jointly shape perceptual decision-making

Perceptual decisions about sensory input are influenced by fluctuations in ongoing neural activity, most prominently driven by attention and neuromodulator systems. It is currently unknown if neuromodulator activity and attention differentially modulate perceptual decision-making and/or whether neuromodulatory systems in fact control attentional processes. To investigate the effects of two distinct neuromodulatory systems and spatial attention on perceptual decisions, we pharmacologically elevated cholinergic (through donepezil) and catecholaminergic (through atomoxetine) levels in humans performing a visuo-spatial attention task, while we measured electroencephalography (EEG). Both attention and catecholaminergic enhancement improved decision-making at the behavioral and algorithmic level, as reflected in increased perceptual sensitivity and the modulation of the drift rate parameter derived from drift diffusion modeling. Univariate analyses of EEG data time-locked to the attentional cue, the target stimulus, and the motor response further revealed that attention and catecholaminergic enhancement both modulated pre-stimulus cortical excitability, cue- and stimulus-evoked sensory activity, as well as parietal evidence accumulation signals. Interestingly, we observed both similar, unique, and interactive effects of attention and catecholaminergic neuromodulation on these behavioral, algorithmic, and neural markers of the decision-making process. Thereby, this study reveals an intricate relationship between attentional and catecholaminergic systems and advances our understanding about how these systems jointly shape various stages of perceptual decision-making.

Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG

Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and alpha oscillations (8-12 Hz) can be linked with the baseline-shift mechanism. This mechanism states that oscillations generate evoked responses if oscillations have a non-zero mean and their amplitude is modulated by the stimulus. Therefore, the following predictions should hold: (1) the temporal evolution of P300 and alpha amplitude is similar, (2) spatial localisations of the P300 and alpha amplitude modulation overlap, (3) oscillations are non-zero mean, (4) P300 and alpha amplitude correlate with cognitive scores in a similar fashion. To validate these predictions, we analysed the data set of elderly participants (N=2230, 60-82 years old), using (a) resting-state EEG recordings to quantify the mean of oscillations, (b) the event-related data, to extract parameters of P300 and alpha rhythm amplitude envelope. We showed that P300 is indeed linked to alpha rhythm, according to all four predictions. Our results provide an unifying view on the interdependency of evoked responses and neuronal oscillations and suggest that P300, at least partly, is generated by the modulation of alpha oscillations.

Temporal Dynamics of Brain Activity Predicting Sense of Agency over Muscle Movements

Our muscles are the primary means through which we affect the external world, and the sense of agency (SoA) over the action through those muscles is fundamental to our self-awareness. However, SoA research to date has focused almost exclusively on agency over action outcomes rather than over the musculature itself, as it was believed that SoA over the musculature could not be manipulated directly. Drawing on methods from human-computer interaction and adaptive experimentation, we use human-in-the-loop Bayesian optimization to tune the timing of electrical muscle stimulation so as to robustly elicit a SoA over electrically actuated muscle movements in male and female human subjects. We use time-resolved decoding of subjects' EEG to estimate the time course of neural activity which predicts reported agency on a trial-by-trial basis. Like paradigms which assess SoA over action consequences, we found that the late (post-conscious) neural activity predicts SoA. Unlike typical paradigms, however, we also find patterns of early (sensorimotor) activity with distinct temporal dynamics predicts agency over muscle movements, suggesting that the "neural correlates of agency" may depend on the level of abstraction (i.e., direct sensorimotor feedback versus downstream consequences) most relevant to a given agency judgment. Moreover, fractal analysis of the EEG suggests that SoA-contingent dynamics of neural activity may modulate the sensitivity of the motor system to external input.SIGNIFICANCE STATEMENT The sense of agency, the feeling of "I did that," when directing one's own musculature is a core feature of human experience. We show that we can robustly manipulate the sense of agency over electrically actuated muscle movements, and we investigate the time course of neural activity that predicts the sense of agency over these actuated movements. We find evidence of two distinct neural processes: a transient sequence of patterns that begins in the early sensorineural response to muscle stimulation and a later, sustained signature of agency. These results shed light on the neural mechanisms by which we experience our movements as volitional.

Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of the external world during sleep

Sleep has long been considered as a state of behavioral disconnection from the environment, without reactivity to external stimuli. Here we questioned this 'sleep disconnection' dogma by directly investigating behavioral responsiveness in 49 napping participants (27 with narcolepsy and 22 healthy volunteers) engaged in a lexical decision task. Participants were instructed to frown or smile depending on the stimulus type. We found accurate behavioral responses, visible via contractions of the corrugator or zygomatic muscles, in most sleep stages in both groups (except slow-wave sleep in healthy volunteers). Across sleep stages, responses occurred more frequently when stimuli were presented during high cognitive states than during low cognitive states, as indexed by prestimulus electroencephalography. Our findings suggest that transient windows of reactivity to external stimuli exist during bona fide sleep, even in healthy individuals. Such windows of reactivity could pave the way for real-time communication with sleepers to probe sleep-related mental and cognitive processes.

Brain-imaging evidence for compression of binary sound sequences in human memory

According to the language-of-thought hypothesis, regular sequences are compressed in human memory using recursive loops akin to a mental program that predicts future items. We tested this theory by probing memory for 16-item sequences made of two sounds. We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened to a hierarchy of sequences of variable complexity, whose minimal description required transition probabilities, chunking, or nested structures. Occasional deviant sounds probed the participants' knowledge of the sequence. We predicted that task difficulty and brain activity would be proportional to the complexity derived from the minimal description length in our formal language. Furthermore, activity should increase with complexity for learned sequences, and decrease with complexity for deviants. These predictions were upheld in both fMRI and MEG, indicating that sequence predictions are highly dependent on sequence structure and become weaker and delayed as complexity increases. The proposed language recruited bilateral superior temporal, precentral, anterior intraparietal, and cerebellar cortices. These regions overlapped extensively with a localizer for mathematical calculation, and much less with spoken or written language processing. We propose that these areas collectively encode regular sequences as repetitions with variations and their recursive composition into nested structures.

Visual short-term memory-related EEG components in a virtual reality setup

Virtual reality (VR) offers a powerful tool for investigating cognitive processes, as it allows researchers to gauge behaviors and mental states in complex, yet highly controlled, scenarios. The use of VR head-mounted displays in combination with physiological measures such as EEG presents new challenges and raises the question whether established findings also generalize to a VR setup. Here, we used a VR headset to assess the spatial constraints underlying two well-established EEG correlates of visual short-term memory: the amplitude of the contralateral delay activity (CDA) and the lateralization of induced alpha power during memory retention. We tested observers' visual memory in a change detection task with bilateral stimulus arrays of either two or four items while varying the horizontal eccentricity of the memory arrays (4, 9, or 14 degrees of visual angle). The CDA amplitude differed between high and low memory load at the two smaller eccentricities, but not at the largest eccentricity. Neither memory load nor eccentricity significantly influenced the observed alpha lateralization. We further fitted time-resolved spatial filters to decode memory load from the event-related potential as well as from its time-frequency decomposition. Classification performance during the retention interval was above-chance level for both approaches and did not vary significantly across eccentricities. We conclude that commercial VR hardware can be utilized to study the CDA and lateralized alpha power, and we provide caveats for future studies targeting these EEG markers of visual memory in a VR setup.