On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

How negative emotions influence arithmetic problem-solving processes: An ERP study

We investigated the effects of negative emotions on arithmetic performance using ERPs. Participants were asked to verify complex multiplication problems that were either true (e.g., 3 × 23 = 69) or false (e.g., 5 × 98 = 485). Half the problems were five problems (e.g., 5 × 94 = 470) and half were non-five problems (e.g., 8 × 63 = 504). False five problems violated arithmetic rules (i.e., the five rule, e.g., 5 × 17 = 87, the parity-rule, e.g., 86 × 5 = 435, both the five- and parity-rules, e.g., 5 × 42 = 411) or no arithmetic rules (e.g., 13 × 5 = 45). Problems were displayed superimposed on emotionally neutral or negative pictures. Behavioral results showed that negative emotions (a) did not affect participants' performance on true five and non-five problems, (b) influenced arithmetic performance on false five problems, and (c) impaired performance on problems that violated both the five- and parity-rules but improved performance on false five problems violating no arithmetic rules. Electrophysiological data revealed that negative emotions led to (a) earlier P1 peak when participants verified true, non-five problems, (b) lower P300 and P600 amplitudes in central brain regions when participants verified false five problems that violated no-rule, (c) earlier N2 peak latencies in central brain regions and larger LPC amplitudes in right parietal regions while participants verified parity-rule violation problems, and (d) earlier N2 peak latencies in central brain regions and later N2 peak latencies in the right prefrontal brain regions while participants verified false, five problems violating both the five- and parity-rules. These findings demonstrate that negative emotions significantly alter key stages of arithmetic problem-solving by modulating neural activity related to encoding, detection of rule violations, and strategic execution, as evidenced by changes in the amplitude and latency of ERP components such as P1, N2, P300, P600, and LPC.

Event-related potentials, heart period, and brain-heart responses during a threat of shock oddball task: Replicability and 6-month-reliability

In a previous study (Gerpheide et al., 2024), we observed that unpredictable threat modulated event-related potentials (N1 and P2, but not P3) and heart responses during an oddball task as well as the communication between brain and heart as measured with cardio-electroencephalographic covariance tracing (CECT). Individual differences in brain, heart, and brain-heart responses to threat may provide biological markers for threat-related personality traits and psychopathology. However, to serve as psychophysiological markers the observed phenomena need to be replicable and individual differences in these phenomena must be reliably assessed and be temporally stable. To address this issue, N = 60 participants of our previous study completed the same auditory oddball paradigm with threat of shock vs. safe contexts 6 months after the initial study. With regard to replicability, all experimental effects that were observed during the first time were also significant 6-months later. With regard to reliability, amplitudes of original ERP waveforms, evoked HP changes and one CECT component showed substantial split-half and test-retest correlations. Moreover, difference scores (threat minus safe) for the P2 and N1 also showed substantial split-half (.55 Collapse

Key Words

• Anxiety
• Cortico-cardiac coupling
• ERP
• Stability
• Unpredictable threat

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Affiliation(s)

Kathrin Gerpheide Department of Psychology, University of Marburg, Marburg, Germany; Department of Psychology, Stanford University, California, United States of America.
Philipp Bierwirth Department of Psychology, University of Marburg, Marburg, Germany
Sarah-Louise Unterschemmann Department of Psychology, University of Marburg, Marburg, Germany
Christian Panitz Department of Psychology, University of Marburg, Marburg, Germany
James J Gross Department of Psychology, Stanford University, California, United States of America
Erik M Mueller Department of Psychology, University of Marburg, Marburg, Germany

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Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection

In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.

Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments

Estimating the location of a stimulus is a key function in sensory processing, and widely considered to result from the integration of prior information and sensory input according to Bayesian principles. A deviation of sensory input from the prior elicits surprisal, depending on the uncertainty of the prior. While this mechanism is increasingly understood in the visual domain, much less is known about its implementation in audition, especially regarding spatial localization. Here, we combined human EEG with computational modeling to study auditory spatial inference in a noisy, volatile environment and analyzed behavioral and neural patterns associated with prior uncertainty and surprisal. First, our results demonstrate that participants indeed used prior information during periods of stable environmental statistics, but showed evidence of surprisal and discarded prior information following environmental changes. Second, we observed distinct EEG activity patterns associated with prior uncertainty and surprisal in both the time- and time-frequency domain, which are in line with previous studies using visual tasks. Third, these EEG activity patterns were predictive of our participants' sound localization error, response uncertainty, and prior bias on a trial-by-trial basis. In summary, our work provides novel behavioral and neural evidence for Bayesian inference during dynamic auditory localization.

An event-related potential study of onset primacy in visual change detection

Onset primacy is a behavioural phenomenon whereby humans identify the appearance of an object (onset) with greater efficiency than other kinds of visual change, such as the disappearance of an object (offset). The default mode hypothesis explains this phenomenon by postulating that the attentional system is optimised for onset detection in its initial state. The present study extended this hypothesis by combining a change-detection task and measurement of the P300 event-related potential, which was thought to index the amount of processing resources available to detecting onsets and offsets. In an experiment, while brain activity was monitored by electroencephalography, participants indicated the locations of onsets and offsets under the condition in which they occurred equally often in the same locations across trials. Although there was no reason to prioritise detecting one type of change over the other, onsets were detected more quickly, and they evoked a larger P300 than offsets. These results suggest that processing resources are preferentially allocated to onset detection. This biased allocation may be a basis on which the attentional system defaults to the 'onset detection' mode.

The Interplay Between Multisensory Processing and Attention in Working Memory: Behavioral and Neural Indices of Audiovisual Object Storage

Although real-life events are multisensory, how audio-visual objects are stored in working memory is an open question. At a perceptual level, evidence shows that both top-down and bottom-up attentional processes can play a role in multisensory interactions. To understand how attention and multisensory processes interact in working memory, we designed an audiovisual delayed match-to-sample task in which participants were presented with one or two audiovisual memory items, followed by an audiovisual probe. In different blocks, participants were instructed to either (a) attend to the auditory features, (b) attend to the visual features, or (c) attend to both auditory and visual features. Participants were instructed to indicate whether the task-relevant features of the probe matched one of the task-relevant feature(s) or objects in working memory. Behavioral results showed interference from task-irrelevant features, suggesting bottom-up integration of audiovisual features and their automatic encoding into working memory, irrespective of task relevance. Yet, event-related potential analyses revealed no evidence for active maintenance of these task-irrelevant features, while they clearly taxed greater attentional resources during recall. Notably, alpha oscillatory activity revealed that linking information between auditory and visual modalities required more attentional demands at retrieval. Overall, these results offer critical insights into how and at which processing stage multisensory interactions occur in working memory.

Early-onset sleep alterations found in patients with amyotrophic lateral sclerosis are ameliorated by orexin antagonist in mouse models

Sleep alterations have been described in several neurodegenerative diseases yet are currently poorly characterized in amyotrophic lateral sclerosis (ALS). This study investigates sleep macroarchitecture and related hypothalamic signaling disruptions in ALS. Using polysomnography, we found that both patients with ALS as well as asymptomatic C9ORF72 and SOD1 mutation carriers exhibited increased wakefulness and reduced non-rapid eye movement sleep. Increased wakefulness correlated with diminished cognitive performance in both clinical cohorts. Similar changes in sleep macroarchitecture were observed in three ALS mouse models (Sod1G86R, FusΔNLS/+, and TDP43Q331K). A single oral administration of a dual-orexin receptor antagonist or intracerebroventricular delivery of melanin-concentrating hormone (MCH) through an osmotic pump over 15 days partially normalized sleep patterns in mouse models. MCH treatment did not extend the survival of Sod1G86R mice but did decrease the loss of lumbar motor neurons. These findings suggest MCH and orexin signaling as potential targets to treat sleep alterations that arise in early stages of the disease.

Preprocessing choices for P3 analyses with mobile EEG: A systematic literature review and interactive exploration

Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where complex processing is necessary to address motion artifacts. Specifically, we describe the preprocessing choices studies reported for analyzing the P3 event-related potential (ERP) during walking and standing. A systematic review of 258 studies of the P3 during walking, identified 27 studies meeting the inclusion criteria. Two independent coders extracted preprocessing choices reported in each study. Analysis of preprocessing choices revealed commonalities and differences, such as the widespread use of offline filters but limited application of line noise correction (3 of 27 studies). Notably, 59% of studies involved manual processing steps, and 56% omitted reporting critical parameters for at least one step. All studies employed unique preprocessing strategies. These findings align with stationary EEG preprocessing results, emphasizing the necessity for standardized reporting in mobile EEG research. We implemented an interactive visualization tool (Shiny app) to aid the exploration of the preprocessing landscape. The app allows users to structure the literature regarding different processing steps, enter planned processing methods, and compare them with the literature. The app could be utilized to examine how these choices impact P3 results and understand the robustness of various processing options. We hope to increase awareness regarding the potential influence of preprocessing decisions and advocate for comprehensive reporting standards to foster reproducibility in mobile EEG research.

Exploring Relevant Features for EEG-Based Investigation of Sound Perception in Naturalistic Soundscapes

A comprehensive analysis of everyday sound perception can be achieved using electroencephalography (EEG) with the concurrent acquisition of information about the environment. While extensive research has been dedicated to speech perception, the complexities of auditory perception within everyday environments, specifically the types of information and the key features to extract, remain less explored. Our study aims to systematically investigate the relevance of different feature categories: discrete sound-identity markers, general cognitive state information, and acoustic representations, including discrete sound onset, the envelope, and mel-spectrogram. Using continuous data analysis, we contrast different features in terms of their predictive power for unseen data and thus their distinct contributions to explaining neural data. For this, we analyze data from a complex audio-visual motor task using a naturalistic soundscape. The results demonstrated that the feature sets that explain the most neural variability were a combination of highly detailed acoustic features with a comprehensive description of specific sound onsets. Furthermore, it showed that established features can be applied to complex soundscapes. Crucially, the outcome hinged on excluding periods devoid of sound onsets in the analysis in the case of the discrete features. Our study highlights the importance to comprehensively describe the soundscape, using acoustic and non-acoustic aspects, to fully understand the dynamics of sound perception in complex situations. This approach can serve as a foundation for future studies aiming to investigate sound perception in natural settings.

Associations Among EEG Aperiodic Slope, Infant Temperament, and Maternal Anxiety/Depression Symptoms in Infancy

The aperiodic "slope" of the EEG power spectrum (i.e., aperiodic exponent, commonly represented as a slope in log-log space) is hypothesized to index the cortical excitatory-inhibitory balance. Slope has been associated with various neurodevelopmental outcomes in older children and adults, as well as with family history of ADHD in infants. Here, we investigate associations among EEG aperiodic slope, temperament, and maternal internalizing (anxiety and depression) symptoms in a large cohort of typically developing infants. A steeper slope was associated with higher scores on the temperament domains of orienting/regulation and surgency but was not associated with negative affectivity. Maternal symptoms did not appear to be directly associated with the slope, but the slope moderated the association between maternal symptoms and temperament. Specifically, a steeper slope was associated with a stronger negative association between maternal internalizing symptoms and infant orienting/regulation. These results demonstrate associations between slope and behavior as early as infancy, which may reflect early differences in the development of global inhibitory networks. Longitudinal research in early childhood is necessary to better understand the nature of these relations during development and their potential impact on later socioemotional outcomes.

Neural dynamics of personality trait perception and interaction preferences

According to recent research, self-reported Big Five personality traits are associated with preferences for faces that are representative of certain Big Five traits. Previous research has primarily focused on either preference for distinct prototypical personality faces or the accuracy of trait ratings for these faces. However, the underlying neural correlates involved in the processing of prototypical personality faces are unknown. In the present study, we aim to bridge this gap by investigating whether participants' Big Five personality traits predict preferences to interact with individuals represented by prototypical personality faces, as well as the neural processing of these facial features. Based on theoretical considerations and previous research, we focus on trait extraversion, agreeableness and neuroticism, and corresponding prototypical faces. Participants were asked to classify prototypical faces as above or below average representative of a certain trait, and then provide an interaction preference rating while face-sensitive event-related potentials (N170 and late positive potential) were measured. In line with our hypotheses, the results showed an interaction preference for faces that were perceived as high (vs. low) extraverted and agreeable and low (vs. high) neurotic. In addition, the preference for agreeable faces interacted with personality characteristics of the perceiver: The higher a persons' score on trait agreeableness, the higher the face preference ratings for both prototypical and perceived high agreeable faces. Analyses of ERP data showed that an increase in preference ratings for prototypical agreeable faces was paralleled by an increase of the late positive potential. Notably, the N170 did not show any neural signature of the hypothesized effects of personality faces. Together, these results highlight the importance of considering both perceiver characteristics as well as perceived features of an interaction partner when it comes to preference for social interaction.Protocol registration The stage 1 protocol for this Registered Report was accepted in principle on the 8th of May 2023. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/G8SCY .

Linking brain-heart interactions to emotional arousal in immersive virtual reality

The subjective experience of emotions is linked to the contextualized perception and appraisal of changes in bodily (e.g., heart) activity. Increased emotional arousal has been related to attenuated high-frequency heart rate variability (HF-HRV), lower EEG parieto-occipital alpha power, and higher heartbeat-evoked potential (HEP) amplitudes. We studied emotional arousal-related brain-heart interactions using immersive virtual reality (VR) for naturalistic yet controlled emotion induction. Twenty-nine healthy adults (13 women, age: 26 ± 3) completed a VR experience that included rollercoasters while EEG and ECG were recorded. Continuous emotional arousal ratings were collected during a video replay immediately after. We analyzed emotional arousal-related changes in HF-HRV as well as in BHIs using HEPs. Additionally, we used the oscillatory information in the ECG and the EEG to model the directional information flows between the brain and heart activity. We found that higher emotional arousal was associated with lower HEP amplitudes in a left fronto-central electrode cluster. While parasympathetic modulation of the heart (HF-HRV) and parieto-occipital EEG alpha power were reduced during higher emotional arousal, there was no evidence for the hypothesized emotional arousal-related changes in bidirectional information flow between them. Whole-brain exploratory analyses in additional EEG (delta, theta, alpha, beta and gamma) and HRV (low-frequency, LF, and HF) frequency bands revealed a temporo-occipital cluster, in which higher emotional arousal was linked to decreased brain-to-heart (i.e., gamma→HF-HRV) and increased heart-to-brain (i.e., LF-HRV → gamma) information flow. Our results confirm previous findings from less naturalistic experiments and suggest a link between emotional arousal and brain-heart interactions in temporo-occipital gamma power.

Cortical oscillations and event-related brain potentials during the preparation and execution of deceptive behavior

Deception often occurs in response to a preceding cue (e.g., a precarious question) alerting us about the need to subsequently lie. Here, we simulate this process by adapting a previously established paradigm of intentionally false responding, now instructing participants about the need for deception (vs. truthful responses) by means of a simple cue occurring before each response-relevant target. We analyzed event-related brain potentials (ERPs) as well as cortical oscillations recorded from the scalp. In an experimental study (N = 44), we show that a cue signaling the need for deception involves increased attentional selection (P2, P3a, P3b). Moreover, in the period following the cue and leading up to the target, ERP and oscillatory signatures of anticipation and preparation (Contingent Negative Variation, alpha suppression) were found to be increased during trials requiring a deceptive as compared to a truthful response. Additionally, we replicated earlier findings that target processing involves enhanced motivated attention toward words requiring a deceptive response (LPC). Moreover, a signature of integration effort and semantic inhibition (N400) was observed to be larger for words to which responses have to be intentionally false as compared to those to which responses must be truthful. Our findings support the view of the involvement of a series of basic cognitive processes (especially attention and cognitive control) when responses are deliberately wrong instead of right. Moreover, preceding cues signaling the subsequent need for lying already elicit attentional and preparatory mechanisms facilitating the cognitive operations necessary for later successful lying.

Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing

Introduction

The ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream.

Methods

Two approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire.

Results

Discriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller.

Discussion

Both four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users.

Multiple independent components contribute to event-related potential correlates of conscious vision

Research has revealed two major event-related potential (ERP) markers of visual awareness: the earlier Visual Awareness Negativity (VAN, around 150-250 ms after stimulus onset), and the following Late Positivity (LP, around 300-500 ms after stimulus onset). Understanding the neural sources that give rise to VAN and LP is important in order to understand what kind of neural processes underlie conscious visual perception. Although the ERPs afford high temporal resolution, their spatial resolution is limited because multiple separate neural sources sum up at the scalp level. In the present study, we sought to characterize the locations and time-courses of independent neural sources underlying the ERP correlates of visual awareness by means of Independent Component Analysis (ICA). ICA allows identifying and localizing the temporal dynamics of different neural sources that contribute to the ERP correlates of conscious perception. The present results show that the cortical sources of VAN are localized to posterior areas including occipital and temporal cortex, while LP reflects a combination of multiple sources distributed among frontal, parietal and occipito-temporal cortex. Our findings suggest that conscious vision correlates with dynamically changing neural sources, developing in part in "accumulative fashion": consciousness-related activity initially arises in few early sources and, subsequently, additional sources are engaged as a function of time. The results further suggest that even early latency neural sources that correlate with conscious perception may also associate with action-related processes.

Social conformity updates the neural representation of facial attractiveness

People readily change their behavior to comply with others. However, to which extent they will internalize the social influence remains elusive. In this preregistered electroencephalogram (EEG) study, we investigated how learning from one's in-group or out-group members about facial attractiveness would change explicit attractiveness ratings and spontaneous neural representations of facial attractiveness. Specifically, we quantified the neural representational similarities of learned faces with prototypical attractive faces during a face perception task without overt social influence and intentional evaluation. We found that participants changed their explicit attractiveness ratings to both in-group and out-group influences. Moreover, social conformity updated spontaneous neural representation of facial attractiveness, an effect particularly evident when participants learned from their in-group members and among those who perceived tighter social norms. These findings offer insights into how group affiliations and individual differences in perceived social norms modulate the internalization of social influence.

Differential Neural Mechanisms of Feedback Processing in Children with Developmental Language Disorder: An Examination of Midfrontal Theta Connectivity

BACKGROUND/OBJECTIVES

Previous research indicates that children with Developmental Language Disorder (DLD) face challenges learning from feedback, resulting in suboptimal performance and learning outcomes. Feedback processing, a key developing executive function, involves cognitive processes critical for goal-directed behavior. This study examined the neural mechanisms underlying feedback processing in school-age children with DLD compared to typically developing (TD) peers, focusing on midfrontal theta band (4-8 Hz) oscillations as an index of cognitive control and error monitoring.

METHODS

We measured midfrontal theta inter-trial coherence (ITC) and inter-site coherence (ISC) at midfrontal (FCz), lateral prefrontal (F3/F4), and lateral central (C3/C4) sites in children with and without DLD (n = 33, age 8-13 years) in response to feedback provision within a Wisconsin Card Sorting Test (WCST) in two time windows (200-400 ms, which is associated with the Feedback-Related Negativity, or FRN, and 400-600 ms, which is associated with the P3a).

RESULTS

Children with and without DLD showed elevated midfrontal theta oscillations in response to negative feedback that was followed by successful behavioral adjustments in the FRN time window. Activation in the P3a time window was only found in the TD group. Group differences were also noted in the inter-site coherence (ISC) associated with the effective processing of negative feedback. While in the TD group, effective processing of negative feedback was linked to high connectivity between midfrontal and right sensorimotor regions, in the DLD group, effective processing of negative feedback was associated with high connectivity between midfrontal and left sensorimotor sites.

CONCLUSIONS

Differential ISC patterns in children with DLD may indicate that they employ alternative or compensatory neural strategies, possibly due to atypical right sensorimotor engagement.

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.

The child the apple eats: processing of argument structure in Mandarin verb-final sentences

Mandarin Chinese is typologically unusual among the world's languages in having flexible word order despite a near absence of inflectional morphology. These features of Mandarin challenge conventional linguistic notions such as subject and object and the divide between syntax and semantics. In the present study, we tested monolingual processing of argument structure in Mandarin verb-final sentences, where word order alone is not a reliable cue. We collected participants' responses to a forced agent-assignment task while measuring their electroencephalography data to capture real-time processing throughout each sentence. We found that sentence interpretation was not informed by word order in the absence of other cues, and while the coverbs BA and BEI were strong signals for agent selection, comprehension was a result of multiple cues. These results challenge previous reports of a linear ranking of cue strength. Event-related potentials showed that BA and BEI impacted participants' processing even before the verb was read and that role reversal anomalies elicited an N400 effect without a subsequent semantic P600. This study demonstrates that Mandarin sentence comprehension requires online interaction among cues in a language-specific manner, consistent with models that predict crosslinguistic differences in core sentence processing mechanisms.

Attentional demands in the visual field modulate audiovisual interactions in the temporal domain

Attention and crossmodal interactions are closely linked through a complex interplay at different stages of sensory processing. Within the context of motion perception, previous research revealed that attentional demands alter audiovisual interactions in the temporal domain. In the present study, we aimed to understand the neurophysiological correlates of these attentional modulations. We utilized an audiovisual motion paradigm that elicits auditory time interval effects on perceived visual speed. The audiovisual interactions in the temporal domain were quantified by changes in perceived visual speed across different auditory time intervals. We manipulated attentional demands in the visual field by having a secondary task on a stationary object (i.e., single- vs. dual-task conditions). When the attentional demands were high (i.e., dual-task condition), there was a significant decrease in the effects of auditory time interval on perceived visual speed, suggesting a reduction in audiovisual interactions. Moreover, we found significant differences in both early and late neural activities elicited by visual stimuli across task conditions (single vs. dual), reflecting an overall increase in attentional demands in the visual field. Consistent with the changes in perceived visual speed, the audiovisual interactions in neural signals declined in the late positive component range. Compared with the findings from previous studies using different paradigms, our findings support the view that attentional modulations of crossmodal interactions are not unitary and depend on task-specific components. They also have important implications for motion processing and speed estimation in daily life situations where sensory relevance and attentional demands constantly change.

You're Beautiful When You Smile: Event-Related Brain Potential (ERP) Evidence of Early Opposite-Gender Bias in Happy Faces

Studies of social cognition have shown gender differences regarding human face processing. One interesting finding is the enhanced processing of opposite-gender faces at different time stages, as revealed by event-related brain potentials. Crucially, from an evolutionary perspective, such a bias might interact with the emotional expression of the face. To investigate this, 100 participants (50 female, 50 male) completed an expression-detection task while their EEG was recorded. In three blocks, fearful, happy and neutral faces (female and male) were randomly presented, with participants instructed to respond to only one predefined target expression level in each block. Using linear mixed models, we observed both faster reaction times as well as larger P1 and late positive potential (LPP) amplitudes for women compared to men, supporting a generally greater female interest in faces. Highly interestingly, the analysis revealed an opposite-gender bias at P1 for happy target faces. This suggests that participants' attentional templates may include more opposite-gender facial features when selectively attending to happy faces. While N170 was influenced by neither the face nor the participant gender, LPP was modulated by the face gender and specific combinations of the target status, face gender and expression, which is interpreted in the context of gender-emotion stereotypes. Future research should further investigate this expression and attention dependency of early opposite-gender biases.

Effects of Transcutaneous Auricular Vagus Nerve Stimulation on the P300: Do Stimulation Duration and Stimulation Type Matter?

Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has attracted increasing interest as a neurostimulation tool with potential applications in modulating cognitive processes such as attention and memory, possibly through the modulation of the locus-coeruleus noradrenaline system. Studies examining the P300 brain-related component as a correlate of noradrenergic activity, however, have yielded inconsistent findings, possibly due to differences in stimulation parameters, thus necessitating further investigation. In this event-related potential study involving 61 participants, therefore, we examined how changes in taVNS parameters, specifically stimulation type (interval vs. continuous stimulation) and duration, influence P300 amplitudes during a visual novelty oddball task. Although no effects of stimulation were found over the whole cluster and time window of the P300, cluster-based permutation tests revealed a distinct impact of taVNS on the P300 response for a small electrode cluster, characterized by larger amplitudes observed for easy targets (i.e., stimuli that are easily discernible from standards) following taVNS compared to sham stimulation. Notably, our findings suggested that the type of stimulation significantly modulated taVNS effects on the P300, with continuous stimulation showing larger P300 differences (taVNS vs. sham) for hard targets and standards compared to interval stimulation. We observed no interaction effects of stimulation duration on the target-related P300. While our findings align with previous research, further investigation is warranted to fully elucidate the influence of taVNS on the P300 component and its potential utility as a reliable marker for neuromodulation in this field.

Heartbeat-evoked potentials following voluntary hyperventilation in epilepsy patients: respiratory influences on cardiac interoception

Introduction

Current evidence indicates a modulating role of respiratory processes in cardiac interoception, yet whether altered breathing patterns influence heartbeat-evoked potentials (HEP) remains inconclusive.

Methods

Here, we examined the effects of voluntary hyperventilation (VH) as part of a clinical routine examination on scalp-recorded HEPs in epilepsy patients (N = 80).

Results

Using cluster-based permutation analyses, HEP amplitudes were compared across pre-VH and post-VH conditions within young and elderly subgroups, as well as for the total sample. No differences in the HEP were detected for younger participants or across the full sample, while an increased late HEP during pre-VH compared to post-VH was fond in the senior group, denoting decreased cardiac interoceptive processing after hyperventilation.

Discussion

The present study, thus, provides initial evidence of breathing-related HEP modulations in elderly epilepsy patients, emphasizing the potential of HEP as an interoceptive neural marker that could partially extend to the representation of pulmonary signaling. We speculate that aberrant CO2-chemosensing, coupled with disturbances in autonomic regulation, might constitute the underlying pathophysiological mechanism behind the obtained effect. Available databases involving patient records of routine VH assessment may constitute a valuable asset in disentangling the interplay of cardiac and ventilatory interoceptive information in various patient groups, providing thorough clinical data to parse, as well as increased statistical power and estimates of effects with higher precision through large-scale studies.

Power spectral density-based resting-state EEG classification of first-episode psychosis

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.

Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments

Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.

A Hybrid EEG-Based Stress State Classification Model Using Multi-Domain Transfer Entropy and PCANet

This paper proposes a new hybrid model for classifying stress states using EEG signals, combining multi-domain transfer entropy (TrEn) with a two-dimensional PCANet (2D-PCANet) approach. The aim is to create an automated system for identifying stress levels, which is crucial for early intervention and mental health management. A major challenge in this field lies in extracting meaningful emotional information from the complex patterns observed in EEG. Our model addresses this by initially applying independent component analysis (ICA) to purify the EEG signals, enhancing the clarity for further analysis. We then leverage the adaptability of the fractional Fourier transform (FrFT) to represent the EEG data in time, frequency, and time-frequency domains. This multi-domain representation allows for a more nuanced understanding of the brain's activity in response to stress. The subsequent stage involves the deployment of a two-layer 2D-PCANet network designed to autonomously distill EEG features associated with stress. These features are then classified by a support vector machine (SVM) to determine the stress state. Moreover, stress induction and data acquisition experiments are designed. We employed two distinct tasks known to trigger stress responses. Other stress-inducing elements that enhance the stress response were included in the experimental design, such as time limits and performance feedback. The EEG data collected from 15 participants were retained. The proposed algorithm achieves an average accuracy of over 92% on this self-collected dataset, enabling stress state detection under different task-induced conditions.

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.

Robust single-trial event-related potentials differentiate between distress and fear disorders

Recent evidence indicates that measures of brain functioning as indexed by event-related potentials (ERPs) on the electroencephalogram align more closely to transdiagnostic measures of psychopathology than to categorical taxonomies. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a transdiagnostic, dimensional framework aiming to solve issues of comorbidity, symptom heterogeneity, and arbitrary diagnostic boundaries. Based on shared features, the emotional disorders are allocated into subfactors Distress and Fear. Evidence indicates that disorders that are close in the HiTOP hierarchy share etiology, symptom profiles, and treatment outcomes. However, further studies testing the biological underpinnings of the HiTOP are called for. In this study, we assessed differences between Distress and Fear in a range of well-studied ERP components. In total, 50 patients with emotional disorders were divided into two groups (Distress, N = 25; Fear, N = 25) according to HiTOP criteria and compared against 37 healthy comparison (HC) subjects. Addressing issues in traditional ERP preprocessing and analysis methods, we applied robust single-trial analysis as implemented in the EEGLAB toolbox LIMO EEG. Several ERP components were found to differ between the groups. Surprisingly, we found no difference between Fear and HC for any of the ERPs. This suggests that some well-established results from the literature, e.g., increased error-related negativity in OCD, are not a shared neurobiological correlate of the Fear subfactor. Conversely, for Distress, we found reductions compared to Fear and HC in several ERP components across paradigms. Future studies could utilize HiTOP-validated psychopathology measures to more precisely define subfactor groups.

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods in minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

Induced error-related theta activity, not error-related negativity, predicts task performance as well as anxiety and worry during real-life stress in a youth sample

OBJECTIVE

The study examined differences between induced error-related theta activity (4-7 Hz) and error-related negativity (ERN) in youth and their unique associations with task performance as well as anxiety and worry during real-life stress a year later. We hypothesized that induced theta, but not the ERN, would predict task performance. We also hypothesized that induced theta would predict less anxiety and worries during situational stress a year later, while ERN would predict more anxiety and worries.

METHOD

Participants included 76 children aged 8-13 years who completed a flanker task while electroencephalogram (EEG) and behavioral data (t0 ) were collected. Approximately 1 year later (t1 ), during the first COVID-19 lockdown, 40 families from the original sample completed a battery of online questionnaires to assess the children's stress-related symptoms (anxiety, negative emotions and worries). We employed an analytical method that allowed us to differentiate between induced error-related theta and the evoked ERN.

RESULTS

Induced error-related theta, but not ERN, was associated with behavioral changes during the task, such as post-error speeding. Furthermore, induced error-related theta, but not ERN, was prospectively associated with less anxiety, worries, and fewer negative emotions a year later during COVID-19 lockdown.

CONCLUSIONS

Findings suggest ERN and error-related theta are dissociable processes reflecting error monitoring in youth. Specifically, induced error-related theta is more robustly associated with changes in behavior in the laboratory and with less anxiety and worries in real-world settings.

Predicting and coding sound into action translation in spinal cord injured people

Motor activation in response to perception of action-related stimuli may depend on a resonance mechanism subserving action understanding. The extent to which this mechanism is innate or learned from sensorimotor experience is still unclear. Here, we recorded EEG while people with paraplegia or tetraplegia consequent to spinal cord injury (SCI) and healthy control participants were presented with action sounds produced by body parts (mouth, hands or feet) that were or were not affected by SCI. Non-action sounds were used as further control. We observed reduced brain activation in subjects affected by SCI at both pre- and post-stimulus latencies specifically for those actions whose effector was disconnected by the spinal lesion (i.e., hand sound for tetraplegia and leg sound for both paraplegia and tetraplegia). Correlation analyses showed that these modulations were functionally linked with the chronicity of the lesion, indicating that the longer the time the lesion- EEG data acquisition interval and/or the more the lesion occurred at a young age, the weaker was the cortical activity in response to these action sounds. Tellingly, source estimations confirmed that these modulations originated from a deficit in the motor resonance mechanism, by showing diminished activity in premotor (during prediction and perception) and near the primary motor (during perception) areas. Such dissociation along the cortical hierarchy is consistent with both previous reports in healthy subjects and with hierarchical predictive coding accounts. Overall, these data expand on the notion that sensorimotor experience maintains the cortical representations relevant to anticipate and perceive action-related stimuli.

Somatosensory omissions reveal action-related predictive processing

The intricate relation between action and somatosensory perception has been studied extensively in the past decades. Generally, a forward model is thought to predict the somatosensory consequences of an action. These models propose that when an action is reliably coupled to a tactile stimulus, unexpected absence of the stimulus should elicit prediction error. Although such omission responses have been demonstrated in the auditory modality, it remains unknown whether this mechanism generalizes across modalities. This study therefore aimed to record action-induced somatosensory omission responses using EEG in humans. Self-paced button presses were coupled to somatosensory stimuli in 88% of trials, allowing a prediction, or in 50% of trials, not allowing a prediction. In the 88% condition, stimulus omission resulted in a neural response consisting of multiple components, as revealed by temporal principal component analysis. The oN1 response suggests similar sensory sources as stimulus-evoked activity, but an origin outside primary cortex. Subsequent oN2 and oP3 responses, as previously observed in the auditory domain, likely reflect modality-unspecific higher order processes. Together, findings straightforwardly demonstrate somatosensory predictions during action and provide evidence for a partially amodal mechanism of prediction error generation.

Amplitude Modulation Perception and Cortical Evoked Potentials in Children With Listening Difficulties and Their Typically Developing Peers

PURPOSE

Amplitude modulations (AMs) are important for speech intelligibility, and deficits in speech intelligibility are a leading source of impairment in childhood listening difficulties (LiD). The present study aimed to explore the relationships between AM perception and speech-in-noise (SiN) comprehension in children and to determine whether deficits in AM processing contribute to childhood LiD. Evoked responses were used to parse the neural origins of AM processing.

METHOD

Forty-one children with LiD and 44 typically developing children, ages 8-16 years, participated in the study. Behavioral AM depth thresholds were measured at 4 and 40 Hz. SiN tasks included the Listening in Spatialized Noise-Sentences Test (LiSN-S) and a coordinate response measure (CRM)-based task. Evoked responses were obtained during an AM change detection task using alternations between 4 and 40 Hz, including the N1 of the acoustic change complex, auditory steady-state response (ASSR), P300, and a late positive response (late potential [LP]). Maturational effects were explored via age correlations.

RESULTS

Age correlated with 4-Hz AM thresholds, CRM separated talker scores, and N1 amplitude. Age-normed LiSN-S scores obtained without spatial or talker cues correlated with age-corrected 4-Hz AM thresholds and area under the LP curve. CRM separated talker scores correlated with AM thresholds and area under the LP curve. Most behavioral measures of AM perception correlated with the signal-to-noise ratio and phase coherence of the 40-Hz ASSR. AM change response time also correlated with area under the LP curve. Children with LiD exhibited deficits with respect to 4-Hz thresholds, AM change accuracy, and area under the LP curve.

CONCLUSIONS

The observed relationships between AM perception and SiN performance extend the evidence that modulation perception is important for understanding SiN in childhood. In line with this finding, children with LiD demonstrated poorer performance on some measures of AM perception, but their evoked responses implicated a primarily cognitive deficit.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25009103.

Finding tau rhythms in EEG: An independent component analysis approach

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

Estimating person-specific neural correlates of mental rotation: A machine learning approach

Using neurophysiological measures to model how the brain performs complex cognitive tasks such as mental rotation is a promising way towards precise predictions of behavioural responses. The mental rotation task requires objects to be mentally rotated in space. It has been used to monitor progressive neurological disorders. Up until now, research on neural correlates of mental rotation have largely focused on group analyses yielding models with features common across individuals. Here, we propose an individually tailored machine learning approach to identify person-specific patterns of neural activity during mental rotation. We trained ridge regressions to predict the reaction time of correct responses in a mental rotation task using task-related, electroencephalographic (EEG) activity of the same person. When tested on independent data of the same person, the regression model predicted the reaction times significantly more accurately than when only the average reaction time was used for prediction (bootstrap mean difference of 0.02, 95% CI: 0.01-0.03, p < .001). When tested on another person's data, the predictions were significantly less accurate compared to within-person predictions. Further analyses revealed that considering person-specific reaction times and topographical activity patterns substantially improved a model's generalizability. Our results indicate that a more individualized approach towards neural correlates can improve their predictive performance of behavioural responses, particularly when combined with machine learning.

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

Response to experimental cold-induced pain discloses a resistant category among endurance athletes, with a distinct profile of pain-related behavior and GABAergic EEG markers: a case-control preliminary study

Pain is a major public health problem worldwide, with a high rate of treatment failure. Among promising non-pharmacological therapies, physical exercise is an attractive, cheap, accessible and innocuous method; beyond other health benefits. However, its highly variable therapeutic effect and incompletely understood underlying mechanisms (plausibly involving the GABAergic neurotransmission) require further research. This case-control study aimed to investigate the impact of long-lasting intensive endurance sport practice (≥7 h/week for the last 6 months at the time of the experiment) on the response to experimental cold-induced pain (as a suitable chronic pain model), assuming that highly trained individual would better resist to pain, develop advantageous pain-copying strategies and enhance their GABAergic signaling. For this purpose, clinical pain-related data, response to a cold-pressor test and high-density EEG high (Hβ) and low beta (Lβ) oscillations were documented. Among 27 athletes and 27 age-adjusted non-trained controls (right-handed males), a category of highly pain-resistant participants (mostly athletes, 48.1%) was identified, displaying lower fear of pain, compared to non-resistant non-athletes. Furthermore, they tolerated longer cold-water immersion and perceived lower maximal sensory pain. However, while having similar Hβ and Lβ powers at baseline, they exhibited a reduction between cold and pain perceptions and between pain threshold and tolerance (respectively -60% and - 6.6%; -179.5% and - 5.9%; normalized differences), in contrast to the increase noticed in non-resistant non-athletes (+21% and + 14%; +23.3% and + 13.6% respectively). Our results suggest a beneficial effect of long-lasting physical exercise on resistance to pain and pain-related behaviors, and a modification in brain GABAergic signaling. In light of the current knowledge, we propose that the GABAergic neurotransmission could display multifaceted changes to be differently interpreted, depending on the training profile and on the homeostatic setting (e.g., in pain-free versus chronic pain conditions). Despite limitations related to the sample size and to absence of direct observations under acute physical exercise, this precursory study brings into light the unique profile of resistant individuals (probably favored by training) allowing highly informative observation on physical exercise-induced analgesia and paving the way for future clinical translation. Further characterizing pain-resistant individuals would open avenues for a targeted and physiologically informed pain management.

Seeking the neural representation of statistical properties in print during implicit processing of visual words

Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.

Evaluating the effectiveness of artifact correction and rejection in event-related potential research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

The N400 is Elicited by Meaning Changes but not Synonym Substitutions: Evidence From Persian Phrasal Verbs

We tested two accounts of the cognitive process underlying the N400 event-related potential component: one that it reflects meaning-based processing and one that it reflects the processing of specific words. The experimental design utilized separable Persian phrasal verbs, which form a strongly probabilistic, long-distance dependency, ideal for the study of probabilistic processing. In sentences strongly constraining for a particular continuation, we show evidence that between two low-probability words, only the word that changed the expected meaning of the sentence increased N400 amplitude, while a synonym of the most probable target word did not. The findings support an account of the N400 in which its underlying process is driven by the processing of meaning rather than of specific words.

Declarative Learning Mechanisms Support Declarative but Not Probabilistic Feedback-Based Learning in Children with Developmental Language Disorder (DLD)

Declarative and probabilistic feedback-based learning was evaluated in 8-12-year-old school-age children with developmental language disorder (DLD; n = 14) and age-matched children with typical development (TD; n = 15). Children performed a visual two-choice word-learning task and a visual probabilistic classification task while their electroencephalogram (EEG) was recorded non-invasively from the scalp. Behavioral measures of accuracy and response to feedback, and electrophysiological responses to feedback were collected and compared between the two groups. While behavioral data indicated poorer performance by children with DLD in both learning paradigms, and similar response patterns to positive and negative feedback, electrophysiological data highlighted processing patterns in the DLD group that differed by task. More specifically, in this group, feedback processing in the context of declarative learning, which is known to be dominated by the medial temporal lobe (MTL), was associated with enhanced N170, an event-related brain potential (ERP) associated with MTL activation. The N170 amplitude was found to be correlated with declarative task performance in the DLD group. During probabilistic learning, known to be governed by the striatal-based learning system, the feedback-related negativity (FRN) ERP, which is the product of the cortico-striatal circuit dominated feedback processing. Within the context of probabilistic learning, enhanced N170 was associated with poor learning in the TD group, suggesting that MTL activation during probabilistic learning disrupts learning. These results are interpreted within the context of a proposed feedback parity hypothesis suggesting that in children with DLD, the system that dominates learning (i.e., MTL during declarative learning and the striatum during probabilistic learning) dominates and supports feedback processing.

Amplitude modulation perception and cortical evoked potentials in children with listening difficulties and their typically-developing peers

Purpose

Amplitude modulations (AM) are important for speech intelligibility, and deficits in speech intelligibility are a leading source of impairment in childhood listening difficulties (LiD). The present study aimed to explore the relationships between AM perception and speech-in-noise (SiN) comprehension in children and to determine whether deficits in AM processing contribute to childhood LiD. Evoked responses were used to parse the neural origin of AM processing.

Method

Forty-one children with LiD and forty-four typically-developing children, ages 8-16 y.o., participated in the study. Behavioral AM depth thresholds were measured at 4 and 40 Hz. SiN tasks included the LiSN-S and a Coordinate Response Measure (CRM)-based task. Evoked responses were obtained during an AM Change detection task using alternations between 4 and 40 Hz, including the N1 of the acoustic change complex, auditory steady-state response (ASSR), P300, and a late positive response (LP). Maturational effects were explored via age correlations.

Results

Age correlated with 4 Hz AM thresholds, CRM Separated Talker scores, and N1 amplitude. Age-normed LiSN-S scores obtained without spatial or talker cues correlated with age-corrected 4 Hz AM thresholds and area under the LP curve. CRM Separated Talker scores correlated with AM thresholds and area under the LP curve. Most behavioral measures of AM perception correlated with the SNR and phase coherence of the 40 Hz ASSR. AM Change RT also correlated with area under the LP curve. Children with LiD exhibited deficits with respect to 4 Hz thresholds, AM Change accuracy, and area under the LP curve.

Conclusions

The observed relationships between AM perception and SiN performance extend the evidence that modulation perception is important for understanding SiN in childhood. In line with this finding, children with LiD demonstrated poorer performance on some measures of AM perception, but their evoked responses implicated a primarily cognitive deficit.

The effects of trauma on feedback processing: an MEG study

The cognitive impact of psychological trauma can manifest as a range of post-traumatic stress symptoms that are often attributed to impairments in learning from positive and negative outcomes, aka reinforcement learning. Research on the impact of trauma on reinforcement learning has mainly been inconclusive. This study aimed to circumscribe the impact of psychological trauma on reinforcement learning in the context of neural response in time and frequency domains. Two groups of participants were tested - those who had experienced psychological trauma and a control group who had not - while they performed a probabilistic classification task that dissociates learning from positive and negative feedback during a magnetoencephalography (MEG) examination. While the exposure to trauma did not exhibit any effects on learning accuracy or response time for positive or negative feedback, MEG cortical activity was modulated in response to positive feedback. In particular, the medial and lateral orbitofrontal cortices (mOFC and lOFC) exhibited increased activity, while the insular and supramarginal cortices showed decreased activity during positive feedback presentation. Furthermore, when receiving negative feedback, the trauma group displayed higher activity in the medial portion of the superior frontal cortex. The timing of these activity changes occurred between 160 and 600 ms post feedback presentation. Analysis of the time-frequency domain revealed heightened activity in theta and alpha frequency bands (4-10 Hz) in the lOFC in the trauma group. Moreover, dividing the two groups according to their learning performance, the activity for the non-learner subgroup was found to be lower in lOFC and higher in the supramarginal cortex. These differences were found in the trauma group only. The results highlight the localization and neural dynamics of feedback processing that could be affected by exposure to psychological trauma. This approach and associated findings provide a novel framework for understanding the cognitive correlates of psychological trauma in relation to neural dynamics in the space, time, and frequency domains. Subsequent work will focus on the stratification of cognitive and neural correlates as a function of various symptoms of psychological trauma. Clinically, the study findings and approach open the possibility for neuromodulation interventions that synchronize cognitive and psychological constructs for individualized treatment.

A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings

Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant's voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.

Context and domain matter: the error-related negativity in peer presence predicts fear of negative evaluation, not global social anxiety, in adolescents

BACKGROUND

Social anxiety symptoms are most likely to emerge during adolescence, a developmental window marked by heightened concern over peer evaluation. However, the neurocognitive mechanism(s) underlying adolescent social anxiety remain unclear. Emerging work points to the error-related negativity (ERN) as a potential neural marker of exaggerated self/error-monitoring in social anxiety, particularly for errors committed in front of peers. However, social anxiety symptoms are marked by heterogeneity and it remains unclear exactly what domain(s) of social anxiety symptoms are associated with ERN variation in peer presence, particularly within the adolescent period.

METHODS

To advance and deepen the mechanistic understanding of the ERN's putative role as a neural marker for social anxiety in adolescence, we leveraged a social manipulation procedure and assessed a developmentally salient domain of social anxiety during adolescence - fear of negative evaluation (FNE). Adolescents residing in Hanzhong, a small city in the southwestern region of mainland China, had EEG recorded while performing a flanker task, twice (peer presence/absence); FNE, as well as global social anxiety symptoms, was assessed.

RESULTS

Overall ERN increases in peer presence. FNE specifically, but not global levels of social anxiety symptoms, predicted ERN in peer presence.

CONCLUSIONS

These data are the first demonstration that the ERN relates to a specific domain of social anxiety in adolescents, as well as the first evidence of such relations within a non-WEIRD (Western, Educated, Industrialized, Rich and Democratic) sample. Results have important implications for theory and research into adolescent social anxiety.

The developmental trajectory of functional excitation-inhibition balance relates to language abilities in autistic and allistic children

Autism is a neurodevelopmental condition that has been related to an overall imbalance between the brain's excitatory (E) and inhibitory (I) systems. Such an EI imbalance can lead to structural and functional cortical deviances and thus alter information processing in the brain, ultimately giving rise to autism traits. However, the developmental trajectory of EI imbalances across childhood and adolescence has not been investigated yet. Therefore, its relationship to autism traits is not well understood. In the present study, we determined a functional measure of the EI balance (f-EIB) from resting-state electrophysiological recordings for a final sample of 92 autistic children from 6 to 17 years of age and 100 allistic (i.e., non-autistic) children matched by age, sex, and nonverbal-IQ. We related the developmental trajectory of f-EIB to behavioral assessments of autism traits as well as language ability. Our results revealed differential EI trajectories for autistic compared to allistic children. Importantly, the developmental trajectory of f-EIB values related to individual language ability. In particular, elevated excitability in late childhood and early adolescence was linked to decreased listening comprehension. Our findings provide evidence against a general EI imbalance in autistic children when correcting for non-verbal IQ. Instead, we show that the developmental trajectory of EI balance shares variance with autism trait development at a specific age range. This is consistent with the proposal that the late development of inhibitory brain activity is a key substrate of autism traits.

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

P3-like signatures of temporal predictions: a computational EEG study

Many cognitive processes, ranging from perception to action, depend on the ability to predict the timing of forthcoming events. Yet, how the brain uses predictive models in the temporal domain is still an unsolved question. In previous work, we began to explore the neural correlates of temporal predictions by using a computational approach in which an ideal Bayesian observer learned the temporal probabilities of target onsets in a simple reaction time task. Because the task was specifically designed to disambiguate updating of predictive models and surprise, changes in temporal probabilities were explicitly cued. However, in the real world, we are usually incidentally exposed to changes in the statistics of the environment. Here, we thus aimed to further investigate the electroencephalographic (EEG) correlates of Bayesian belief updating and surprise associated with incidental learning of temporal probabilities. In line with our previous EEG study, results showed distinct P3-like modulations for updating and surprise. While surprise was indexed by an early fronto-central P3-like modulation, updating was associated with a later and more posterior P3 modulation. Moreover, updating was associated with a P2-like potential at centro-parietal electrodes, likely capturing integration processes between prior beliefs and likelihood of the observed event. These findings support previous evidence of trial-by-trial variability of P3 amplitudes as an index of dissociable inferential processes. Coupled with our previous findings, the present study strongly bolsters the view of the P3 as a key brain signature of temporal Bayesian inference. Data and scripts are shared on OSF: osf.io/sdy8j/.

Pre-attentive representation of prediction certainty in autism: A mismatch negativity (MMN) study

According to predictive processing theories of perception, the brain generates predictions to prepare for sensory input, and calibrates certainty of predictions based on their likelihood. When an input doesn't match the prediction, an error signal leads to updating of the predictive model. Prior research suggests altered prediction certainty in autism, but predictive processing occurs across the cortical hierarchy, and the stage(s) of processing where prediction certainty breaks down is unknown. We therefore tested the integrity of prediction certainty in autism at pre-attentive and relatively automatic processing stages using the pre-attentive Mismatch Negativity (MMN) brain response. The MMN occurs in response to a "deviant" presented in a stream of "standards" and is measured while the participant performs an orthogonal task. Most critically, MMN amplitude typically varies with the level of certainty associated with the prediction. We recorded high-density EEG while presenting adolescents and young adults with and without autism with repetitive tones every half second (the standard) interspersed with infrequent pitch and inter-stimulus-interval (ISI) deviants. Pitch and ISI deviant probabilities were manipulated at 4, 8, or 16% within a block of trials to test whether MMN amplitude varied in a typical manner with respect to probability. For both groups, Pitch-MMN amplitude increased as the probability of deviance decreased. Unexpectedly, ISI-MMN amplitude did not reliably vary by probability in either group. Our Pitch-MMN findings suggest intact neural representation of pre-attentive prediction certainty in autism, addressing a critical knowledge gap in autism research. The implications of these findings are considered.

LAY SUMMARY

Our brains are always trying to predict what will happen next. For example, when you open your utensil drawer, it would be surprising to see books because your brain expected to see utensils. In our study, we looked at whether the brains of autistic individuals automatically and accurately recognize when something unexpected happens. Results showed similar brain patterns in individuals with and without autism, suggesting that responses to prediction violations are generated in a typical manner during early cortical information processing.

Associations between EEG trajectories, family income, and cognitive abilities over the first two years of life

We sought to characterize developmental trajectories of EEG spectral power over the first 2 years after birth and examine whether family income or maternal education alter those trajectories. We analyzed EEGs (n = 161 infants, 534 EEGs) collected longitudinally between 2 and 24 months of age, and calculated frontal absolute power across 7 canonical frequency bands. For each frequency band, a piecewise growth curve model was fit, resulting in an estimated intercept and two slope parameters from 2 to 9 months and 9-24 months of age. Across 6/7 frequency bands, absolute power significantly increased over age, with steeper slopes in the 2-9 month period compared to 9-24 months. Increased family income, but not maternal education, was associated with higher intercept (2-3 month power) across delta-gamma bands (p range = 0.002-0.04), and reduced change in power between 2 and 9 months of age in lower frequency bands (delta-alpha, p range = 0.01-0.02). There was no significant effect of income on slope between 9 and 24 months. EEG intercept and slope measures did not mediate relationships between income and 24-month verbal and nonverbal development. These results add to growing literature concerning the role of socioeconomic factors in shaping brain trajectories.