Analysis and visualization of single-trial event-related potentials

Repetition suppression between monetary loss and social pain

The relationship between monetary loss and pain has been a recent research focus. Prior studies found similarities in the network representation patterns of monetary loss and pain, particularly social pain. However, the neural level evidence was lacking. To address this, we conducted an ERP experiment to investigate whether there is a repetitive suppression effect of monetary loss on the neural activity of social pain, aiming to understand if they engage overlapping neuronal populations. The results revealed that FRN amplitudes showed repetitive suppression effects of monetary loss on the neural activity of social pain. Our study suggests that monetary loss and social pain share common neural bases, indicating that they might involve shared neural modules related to cognitive conflict and affective appraisal.

A Neural Decision Signal during Internal Sampling from Working Memory in Humans

How humans transform sensory information into decisions that steer purposeful behavior is a central question in psychology and neuroscience that is traditionally investigated during the sampling of external environmental signals. The decision-making framework of gradual information sampling toward a decision has also been proposed to apply when sampling internal sensory evidence from working memory. However, neural evidence for this proposal remains scarce. Here we show (using scalp EEG in male and female human volunteers) that sampling internal visual representations from working memory elicits a scalp EEG potential associated with gradual evidence accumulation-the central parietal positivity. Consistent with an evolving decision process, we show how this signal (1) scales with the time participants require to reach a decision about the cued memory content and (2) is amplified when having to decide among multiple contents in working memory. These results bring the electrophysiology of decision-making into the domain of working memory and suggest that variability in memory-guided behavior may be driven (at least in part) by variations in the sampling of our inner mental contents.

Subjectively salient faces differ from emotional faces: ERP evidence

The self-face is processed differently than emotional faces. A question arises whether other highly familiar and subjectively significant non-self faces (e.g. partner's face) are also differentiated from emotional faces. The aim of this event-related potential (ERP) study was to investigate the neural correlates of personally-relevant faces (the self and a close-other's) as well as emotionally positive (happy) and neutral faces. Participants were tasked with the simple detection of faces. Amplitudes of N170 were more negative in the right than in the left hemisphere and were not modulated by type of face. A similar pattern of N2 and P3 results for the self-face and close-other's face was observed: they were associated with decreased N2 and increased P3 relative to happy and neutral faces. However, the self-face was preferentially processed also when compared to a close-other's face as revealed by lower N2 and higher P3 amplitudes. Nonparametric cluster-based permutation tests showed an analogous pattern of results: significant clusters for the self-face compared with all other faces (close-other's, happy, neutral) and for close-other's face compared to happy and neutral faces. In summary, the self-face prioritization was observed, as indicated by significant differences between one's own face and all other faces. Crucially, both types of personally-relevant faces differed from happy faces. These findings point to the pivotal role of subjective evaluation of the saliency factor.

The effect of top-down attention on empathy fatigue

Prolonged exposure to others' suffering can lead to empathy fatigue, especially when individuals struggle to effectively regulate their empathic capacity. Shifting active attention away from emotional components toward cognitive components of others' suffering is an effective strategy for mitigating empathy fatigue. This research investigated how top-down attentional manipulation modulates empathy fatigue in both auditory (Study 1) and visual (Study 2) modalities. Participants completed two tasks in both studies: (i) the attention to cognitive empathy task (A-C task) and (ii) the attention to emotional empathy task (A-E task). Each task included three blocks (Time Block 1, Time Block 2, and Time Block 3) designed to induce empathy fatigue. Study 1 revealed that the A-C task reduced empathy fatigue and N1 amplitudes than the A-E task in Time Block 3, indicating that attention to cognitive empathy might decrease auditory empathy fatigue. Study 2 indicates that the A-C task caused a longer N2 latency than the A-E task, signifying a decelerated emotional empathic response when attention was on cognitive empathy in the visual modality. Overall, prioritizing cognitive empathy seems to conserve mental resources and reduce empathy fatigue. This research documented the relationship between top-down attention and empathy fatigue and the possible neural mechanism.

Second Order Blind Identification of Event Related Potentials Sources

Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

The influence of aggressive exercise on responses to self-perceived and others' pain

Previous studies have reported relationships between exercise and pain. However, little is known about how aggressive exercise modulates individuals' responses to their own and others' pain. This present study addresses this question by conducting 2 studies employing event-related potential (ERP). Study 1 included 38 participants whose self-perceived pain was assessed after intervention with aggressive or nonaggressive exercises. Study 2 recruited 36 participants whose responses to others' pain were assessed after intervention with aggressive or nonaggressive exercise. Study 1's results showed that P2 amplitudes were smaller, reaction times were longer, and participants' judgments were less accurate in response to self-perceived pain stimuli, especially to high-pain stimuli, after intervention with aggressive exercise compared to nonaggressive exercise. Results of study 2 showed that both P3 and LPP amplitudes to others' pain were larger after intervention with aggressive exercise than with nonaggressive exercise. These results suggest that aggressive exercise decreases individuals' self-perceived pain and increases their empathic responses to others' pain.

Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making

Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

Neural Differences in Relation to Risk Preferences during Reward Processing: An Event-Related Potential Study

Inter-individual variability in risk preferences can be reflected in reward processing differences, making people risk-seekers or risk-averse. However, the neural correlates of reward processing in individuals with risk preferences remain unknown. Consequently, this event-related potential (ERP) study examined and compared electrophysiological correlates associated with different stages of reward processing in risk-seeking and risk-averse groups. Individuals scoring in the bottom and top 20% on the Balloon Analogue Risk Task (BART) were deemed risk-averse and risk-seeking, respectively. Participants engaged in a gambling task while their electroencephalogram (EEG) was recorded. Risk-seekers tended to choose high-risk options significantly more frequently than low-risk options, whereas risk-averse individuals chose low-risk options significantly more frequently than high-risk ones. All participants selected the low-risk alternative more slowly than the high-risk option. During the anticipation stage, the low-risk option elicited a relatively attenuated stimulus-preceding negativity (SPN) response from risk-seekers compared to risk-averse participants. During the outcome stage, feedback-related negativity (FRN) increased in risk-seekers responding to greater losses but not in risk-averse participants. These results indicate that ERP components can detect differences in reward processing during risky situations. In addition, these results suggest that motivation and cognitive control, along with their associated neural processes, may play a central role in differences in reward-based behavior between the two groups.

Event-related variability is modulated by task and development

In carefully designed experimental paradigms, cognitive scientists interpret the mean event-related potentials (ERP) in terms of cognitive operations. However, the huge signal variability from one trial to the next, questions the representability of such mean events. We explored here whether this variability is an unwanted noise, or an informative part of the neural response. We took advantage of the rapid changes in the visual system during human infancy and analyzed the variability of visual responses to central and lateralized faces in 2-to 6-month-old infants compared to adults using high-density electroencephalography (EEG). We observed that neural trajectories of individual trials always remain very far from ERP components, only moderately bending their direction with a substantial temporal jitter across trials. However, single trial trajectories displayed characteristic patterns of acceleration and deceleration when approaching ERP components, as if they were under the active influence of steering forces causing transient attraction and stabilization. These dynamic events could only partly be accounted for by induced microstate transitions or phase reset phenomena. Importantly, these structured modulations of response variability, both between and within trials, had a rich sequential organization, which in infants, was modulated by the task difficulty and age. Our approaches to characterize Event Related Variability (ERV) expand on classic ERP analyses and provide the first evidence for the functional role of ongoing neural variability in human infants.

Aging effect of cross-modal interactions during audiovisual detection and discrimination by behavior and ERPs

Introduction

Numerous studies have shown that aging greatly affects audiovisual integration; however, it is still unclear when the aging effect occurs, and its neural mechanism has yet to be fully elucidated.

Methods

We assessed the audiovisual integration (AVI) of older (n = 40) and younger (n = 45) adults using simple meaningless stimulus detection and discrimination tasks. The results showed that the response was significantly faster and more accurate for younger adults than for older adults in both the detection and discrimination tasks. The AVI was comparable for older and younger adults during stimulus detection (9.37% vs. 9.43%); however, the AVI was lower for older than for younger adults during stimulus discrimination (9.48% vs. 13.08%) behaviorally. The electroencephalography (EEG) analysis showed that comparable AVI amplitude was found at 220-240 ms for both groups during stimulus detection and discrimination, but there was no significant difference between brain regions for older adults but a higher AVI amplitude in the right posterior for younger adults. Additionally, a significant AVI was found for younger adults in 290-310 ms but was absent for older adults during stimulus discrimination. Furthermore, significant AVI was found in the left anterior and right anterior at 290-310 ms for older adults but in the central, right posterior and left posterior for younger adults.

Discussion

These results suggested that the aging effect of AVI occurred in multiple stages, but the attenuated AVI mainly occurred in the later discriminating stage attributed to attention deficit.

Understanding the Effects of Constraint and Predictability in ERP

Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.

Scarcity mindset reduces empathic responses to others' pain: the behavioral and neural evidence

Resource scarcity pervades our life. A scarcity mindset triggered by perceiving insufficient resources has been proven to influence our cognition and behaviors, yet it remains unknown whether this mindset specifically influences empathy. The present study induced feelings of scarcity or abundance in separate groups of participants through experimental manipulation and examined the effects of both mindsets on the behavioral and neural responses to others' pain. Behaviorally, pain intensity ratings of others' pain were lower in the scarcity group than in the abundance group. The analysis of event-related potentials revealed that N1 amplitudes for painful and nonpainful stimuli were comparable in the scarcity group but differed significantly in the abundance group. Additionally, while both groups showed larger late positive potential amplitudes for painful stimuli than for nonpainful stimuli, this amplitude differential was significantly smaller in the scarcity group than in the abundance group. Thus, behavioral and neural evidence suggests that inducing a scarcity mindset significantly dampens the ability to empathize with others' pain during both the early and late stages of empathic processing. These findings shed light on our understanding of how a scarcity mindset may influence social emotions and behaviors.

Testosterone administration enhances the expectation and perception of painful and non-painful somatosensory stimuli

The influence of testosterone on pain perception remains inconsistent in the literature. This randomized, placebo-controlled, double-blind, crossover study investigated the effect of testosterone administration on perception and expectation of electrocutaneous stimulus. Thirty healthy male participants received a single dose of testosterone in one session and a placebo in the other session. For each session, they completed a pain-rating task in which a predictability cue was inserted before a painful or non-painful electocutaneous stimulus delivery, while neural activity was simultaneously recorded by a 64-channel electroencephalographic (EEG) system. Expected and perceived pain ratings, as well as event-related potentials (ERPs) to electocutaneous stimuli and prestimulus EEG oscillatory activities while expecting upcoming electocutaneous stimuli were comprehensively compared between testosterone and placebo sessions. Compared with the placebo session, participants in the testosterone session reported greater pain rating and exhibited greater amplitude of N1 component on ERPs when perceiving both painful and non-painful electrocutaneous stimuli. Mediation analysis revealed that testosterone enhanced the pain-intensity ratings via the N1 response to the electrocutaneous stimulus. Upon viewing the predictability cues after testosterone administration, expected pain intensity increased and spontaneous low-frequency α-oscillation power in the frontal region decreased. These results provide evidence that testosterone enhanced perception and expectation of somatosensory events, and that this was a general effect rather than pain-specific. A plausible explanation for these findings is that testosterone acts to increase vigilance and sustained attention levels, as evidenced by the decreased α-oscillation power. Thus, our findings support a causal role for testosterone in heightening the biological salience of incoming somatosensory information.

Distinct neural dynamics underlying risk and ambiguity during valued-based decision making

Uncertainty can be fractioned into risk and ambiguity psychologically and neurobiologically. However, whether and how risk and ambiguity are dissociated in terms of neural dynamics during value-based decision making remain elusive. The present event-related potential (ERP) study addressed these issues by asking participants to perform a wheel-of-fortune task either during a risky context (Experiment 1; N = 30) where outcome probability was known or during an ambiguous context (Experiment 2; N = 30) where outcome probability was unknown. Results revealed that the cue-P3 was more enhanced for risk versus ambiguity during the anticipatory phase, whereas the RewP was more increased for ambiguity than risk during the consummatory phase. Moreover, the SPN and the fb-P3 components were further modulated by the levels of risk and ambiguity, respectively. These findings demonstrate a neural dissociation between risk and ambiguity, which unfolds from the anticipatory phase to the consummatory phase.

The self and a close-other: differences between processing of faces and newly acquired information

Prioritization of self-related information (e.g. self-face) may be driven by its extreme familiarity. Nevertheless, the findings of numerous behavioral studies reported a self-preference for initially unfamiliar information, arbitrarily associated with the self. In the current study, we investigated the neural underpinnings of extremely familiar stimuli (self-face, close-other's face) and stimuli newly assigned to one's own person and to a close-other (abstract shapes). Control conditions consisted of unknown faces and unknown abstract shapes. Reaction times (RTs) to the self-face were shorter than to close-other's and unknown faces, whereas no RTs differences were observed for shapes. P3 amplitude to the self-face was larger than to close-other's and unknown faces. Nonparametric cluster-based permutation tests showed significant clusters for the self-face vs. other (close-other's, unknown) faces. However, in the case of shapes P3 amplitudes to the self-assigned shape and to the shape assigned to a close-other were similar, and both were larger than P3 to unknown shapes. No cluster was detected for the self-assigned shape when compared with the shape assigned to the close-other. Thus, our findings revealed preferential attentional processing of the self-face and the similar allocation of attentional resources to shapes assigned to the self and a close-other.

Removing ocular artifacts from magnetoencephalographic data on naturalistic reading of continuous texts

Naturalistic reading paradigms and stimuli consisting of long continuous texts are essential for characterizing the cortical basis of reading. Due to the highly dynamic nature of the reading process, electrophysiological brain imaging methods with high spatial and temporal resolution, such as magnetoencephalography (MEG), are ideal for tracking them. However, as electrophysiological recordings are sensitive to electromagnetic artifacts, data recorded during naturalistic reading is confounded by ocular artifacts. In this study, we evaluate two different pipelines for removing ocular artifacts from MEG data collected during continuous, naturalistic reading, with the focus on saccades and blinks. Both pipeline alternatives are based on blind source separation methods but differ fundamentally in their approach. The first alternative is a multi-part process, in which saccades are first extracted by applying Second-Order Blind Identification (SOBI) and, subsequently, FastICA is used to extract blinks. The other alternative uses a single powerful method, Adaptive Mixture ICA (AMICA), to remove all artifact types at once. The pipelines were tested, and their effects compared on MEG data recorded from 13 subjects in a naturalistic reading task where the subjects read texts with the length of multiple pages. Both pipelines performed well, extracting the artifacts in a single component per artifact type in most subjects. Signal power was reduced across the whole cortex in all studied frequency bands from 1 to 90 Hz, but especially in the frontal cortex and temporal pole. The results were largely similar for the two pipelines, with the exception that SOBI-FastICA reduced signal in the right frontal cortex in all studied frequency bands more than AMICA. However, there was considerable interindividual variation in the effects of the pipelines. As a holistic conclusion, we choose to recommend AMICA for removing artifacts from MEG data on naturalistic reading but note that the SOBI-FastICA pipeline has also various favorable characteristics.

Attentional capture by angry faces in girls who self-injure: Evidence from steady state visual evoked potentials

BACKGROUND

Non-suicidal self-injury (NSSI) is a significant public health concern, not only because of the personal and social cost of the behavior itself, but also because it increases risk for future self-injurious behaviors, including suicide attempts. NSSI is increasingly prevalent during adolescence, which highlights the need for research aimed at identifying modifiable risk factors that can be targeted to reduce future risk. Building from theoretical models that highlight interpersonal processes, this study examined whether adolescents with an NSSI history exhibit greater difficulty inhibiting attention to emotionally salient interpersonal stimuli (face), indexed via steady state visual evoked potentials (SSVEPs), which provide a direct neural index of the ability to inhibit attention to task-irrelevant stimuli.

METHODS

Adolescent girls aged 13-17 with (n = 26) and without (n = 28) an NSSI history completed a change-detection computer task during which frequency-tagged SSVEPs were used to assess adolescents' ability to inhibit attention to affectively salient stimuli from spatially superimposed targets.

RESULTS

Compared with adolescents with no NSSI history, adolescents with NSSI demonstrated difficulty inhibiting attention to angry adult faces.

CONCLUSIONS

These findings underscore specific deficits in attentional filtering among girls with an NSSI history, which, if replicated and extended, could be a promising intervention target for reducing risk for future NSSI.

Capsaicin-induced pain increases neural responses to low-calorie non-spicy food cues: An ERP study

Capsaicin, the main spicy ingredient in chili, can activate pain receptors on the human tongue and skin. Although some studies have determined that pain influenced preference for high-calorie foods, little is known whether pain can modulate the individuals' preference for spicy foods and its neural mechanisms. After 30 participants underwent painful (topical capsaicin cream) and control (hand cream) treatments, an event-related potential (ERP) study was conducted to investigate the modulation of capsaicin-induced pain on food preference with food images. Results showed that both P3 and late positive potential (LPP) amplitudes during the painful treatment were significantly larger than those during the control treatment for low-calorie non-spicy food cues. However, for the other three categories of food cues, there were no significant differences between the two treatments. The present study suggests that capsaicin-induced pain increases individuals' neural processing of low-calorie non-spicy food cues, which provides empirical evidence on the relationship between pain and neural responses to food cues to help optimize dietary interventions for patients experiencing pain.

Auditory attentional load attenuates age-related audiovisual integration: An EEG study

Studies have revealed that visual attentional load modulated audiovisual integration (AVI) greatly; however, auditory and visual attentional resources are separate to some degree, and task-irrelevant auditory information could arouse much faster and larger attentional alerting effects than visible information. Here, we aimed to explore how auditory attentional load influences AVI and how aging could have an effect. Thirty older and 30 younger adults participated in an AV discrimination task with an additional auditory distractor competing for attentional resources. The race model analysis revealed highest AVI in the low auditory attentional load condition (low > no > medium > high, pairwise comparison, all p ≤ 0.047) for younger adults and a higher AVI under the no auditory attentional-load condition (p = 0.008), but there was a lower AVI under the low (p = 0.019), medium (p < 0.001), and high (p = 0.021) auditory attentional-load conditions for older adults than for younger adults. The time-frequency analysis revealed higher theta- and alpha-band AVI oscillation under no and low auditory attentional-load conditions than under medium and high auditory attentional-load conditions for both older (all p ≤ 0.011) and younger (all p ≤ 0.024) adults. Additionally, Weighted Phase lag index (WPLI) analysis revealed higher theta-band and lower alpha-band global functional connectivity for older adults during AV stimuli processing (all p ≤ 0.031). These results suggested that the AVI was higher in the low attentional-load condition than in the no attentional-load condition but decreased inversely with increasing of attentional load and that there was a significant aging effect in older adults. In addition, the strengthened theta-band global functional connectivity in older adults during AV stimuli processing might be an adaptive phenomenon for age-related perceptual decline.

On the Comparison Between the Nc/CRN and the Ne/ERN

After the Error Negativity (Ne or ERN) has been described on full-blown errors and on partial error, a smaller Error Negativity-like wave (CRN or Nc) has also been evidenced on correct trials, first in patients with schizophrenia and, later on, in healthy subjects. The functional significance of the Nc as compared to the Ne is of critical importance since most models accounting for the genesis of the Ne on errors and partial errors cannot account for the existence of the Nc if this Nc simply corresponds to a small Ne. On the contrary, if the Nc and the Ne are two completely distinct components, then the existence of a Nc poses no constraint to the existing models. To this end, we examine in the present review the similarities and the differences existing between the Ne and the Nc regarding their functional properties and their anatomical origin.

Self- and Situation-Focused Reappraisal are not homogeneous: Evidence from behavioral and brain networks

Reappraisal is an effective emotion regulation strategy which can be divided into self- and situation-focused subtypes. Previous studies have produced inconsistent findings on the moderating effects and neural mechanisms of reappraisal; thus, further research is necessary to clarify these inconsistencies. In this study, a total of 44 participants were recruited and randomly assigned to two groups. 23 participants were assigned to the self-focused group, while 21 participants were assigned to the situation-focused group. The participants' resting EEG data were collected for 6 minutes before the experiment began, followed by an emotional regulation task. During this task, participants were asked to view emotion-provoking images under four emotion regulation conditions (View, Watch, Increase, and Decrease). Late positive potential (LPP) was obtained when these emotional images were observed. LPP is an effective physiological indicator of emotion regulation, enabling this study to explore emotion regulation under different reappraisal strategies, as well as the functional connectivity and node efficiency within the brain. It was found that, in terms of the effect on emotion regulation, situation-focused reappraisal was significantly better than self-focused reappraisal at enhancing the valence of negative emotion, while self-focused reappraisal was significantly better than situation-focused reappraisal at increasing the arousal of negative emotion. In terms of neural mechanisms, multiple brain regions such as the anterior cingulate cortex, the frontal lobe, the parahippocampal gyrus, parts of the temporal lobe, and parts of the parietal lobe were involved in both reappraisal processes. In addition, there were some differences in brain regions associated with different forms of cognitive reappraisal. Self-focused reappraisal was associated with the posterior cingulate gyrus, fusiform gyrus, and lingual gyrus, and situation-focused reappraisal was associated with the parietal lobule, anterior central gyrus, and angular gyrus. In conclusion, this research demonstrates that self- and situation-focused reappraisal are not homogenous in terms of their effects and neural mechanisms and clarifies the uncertainties over their regulatory effects. Different types of reappraisal activate different brain regions when used, and the functional connectivity or node efficiency of these brain regions seems to be a suitable indicator for assessing the effects of different types of reappraisal.

Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases

Software such as EEGLab has enabled the treatment and visualization of the tracing and cortical topography of the electroencephalography (EEG) signals. In particular, the topography of the cortical electrical activity is represented by colors, which make it possible to identify functional differences between cortical areas and to associate them with various diseases. The use of cortical topography with EEG origin in the investigation of diseases is often not used due to the representation of colors making it difficult to classify the disease. Thus, the analyses have been carried out, mainly, based on the EEG tracings. Therefore, a computer system that recognizes disease patterns through cortical topography can be a solution to the diagnostic aid. In view of this, this study compared five models of Convolutional Neural Networks (CNNs), namely: Inception v3, SqueezeNet, LeNet, VGG-16 and VGG-19, in order to know the patterns in cortical topography images obtained with EEG, in Parkinson's disease, Depression and Bipolar Disorder. SqueezeNet performed better in the 3 diseases analyzed, with Parkinson's disease being better evaluated for Accuracy (88.89%), Precison (86.36%), Recall (91.94%) and F1 Score (89.06%), the other CNNs had less performance. In the analysis of the values of the Area under ROC Curve (AUC), SqueezeNet reached (93.90%) for Parkinson's disease, (75.70%) for Depression and (72.10%) for Bipolar Disorder. We understand that there is the possibility of classifying neurological diseases from cortical topographies with the use of CNNs and, thus, creating a computational basis for the implementation of software for screening and possible diagnostic assistance.

The role of sex and emotion on emotion perception in artificial faces: An ERP study

Sex has a significant impact on the perception of emotional expressions. However, it remains unclear whether sex influences the perception of emotions in artificial faces, which are becoming popular in emotion research. We used an emotion recognition task with FaceGen faces portraying six basic emotions aiming to investigate the effect of sex and emotion on behavioural and electrophysiological parameters. 71 participants performed the task while EEG was recorded. The recognition of sadness was the poorest, however, females recognized sadness better than males. ERP results indicated that fear, disgust, and anger evoked higher amplitudes of late positive potential over the left parietal region compared to neutral expression. Females demonstrated higher values of global field power as compared to males. The interaction between sex and emotion on ERPs was not significant. The results of our study may be valuable for future therapies and research, as it emphasizes possibly distinct processing of emotions and potential sex differences in the recognition of emotional expressions in FaceGen faces.

Altered Processing of Social Emotions in Individuals With Autistic Traits

Social impairment is a defining phenotypic feature of autism. The present study investigated whether individuals with autistic traits exhibit altered perceptions of social emotions. Two groups of participants (High-AQ and Low-AQ) were recruited based on their scores on the autism-spectrum quotient (AQ). Their behavioral responses and event-related potentials (ERPs) elicited by social and non-social stimuli with positive, negative, and neutral emotional valence were compared in two experiments. In Experiment 1, participants were instructed to view social-emotional and non-social emotional pictures. In Experiment 2, participants were instructed to listen to social-emotional and non-social emotional audio recordings. More negative emotional reactions and smaller amplitudes of late ERP components (the late positive potential in Experiment 1 and the late negative component in Experiment 2) were found in the High-AQ group than in the Low-AQ group in response to the social-negative stimuli. In addition, amplitudes of these late ERP components in both experiments elicited in response to social-negative stimuli were correlated with the AQ scores of the High-AQ group. These results suggest that individuals with autistic traits have altered emotional processing of social-negative emotions.

Weighted Blind Source Separation Can Decompose the Frequency Mismatch Response by Deviant Concatenation: An MEG Study

The mismatch response (MMR) is thought to be a neurophysiological measure of novel auditory detection that could serve as a translational biomarker of various neurological diseases. When recorded with electroencephalography (EEG) or magnetoencephalography (MEG), the MMR is traditionally extracted by subtracting the event-related potential/field (ERP/ERF) elicited in response to “deviant” sounds that occur randomly within a train of repetitive “standard” sounds. However, there are several problems with such a subtraction, which include increased noise and the neural adaptation problem. On the basis of the original theory underlying MMR (i.e., the memory-comparison process), the MMR should be present only in deviant epochs. Therefore, we proposed a novel method called weighted-BSST/k, which uses only the deviant response to derive the MMR. Deviant concatenation and weight assignment are the primary procedures of weighted-BSST/k, which maximize the benefits of time-delayed correlation. We hypothesized that this novel weighted-BSST/k method highlights responses related to the detection of the deviant stimulus and is more sensitive than independent component analysis (ICA). To test this hypothesis and the validity and efficacy of the weighted-BSST/k in comparison with ICA (infomax), we evaluated the methods in 12 healthy adults. Auditory stimuli were presented at a constant rate of 2 Hz. Frequency MMRs at a sensor level were obtained from the bilateral temporal lobes with the subtraction approach at 96–276 ms (the MMR time range), defined based on spatio-temporal cluster permutation analysis. In the application of the weighted-BSST/k, the deviant responses were given a constant weight using a rectangular window on the MMR time range. The ERF elicited by the weighted deviant responses demonstrated one or a few dominant components representing the MMR that fitted well with that of the sensor space analysis using the conventional subtraction approach. In contrast, infomax or weighted-infomax revealed many minor or pseudo components as constituents of the MMR. Our single-trial, contrast-free approach may assist in using the MMR in basic and clinical research, and it opens a new and potentially useful way to analyze event-related MEG/EEG data.

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.

Developmental Language Disorder as Syntactic Prediction Impairment

We provide evidence that children with Developmental Language Disorder (DLD) are impaired in predictive syntactic processing. In the current study, children listened passively to auditorily-presented sentences, where the critical condition included an unexpected "filled gap" in the direct object position of the relative clause verb. A filled gap is illustrated by the underlined phrase in "The zebra that the hippo kissed the camel on the nose…", rather than the expected "the zebra that the hippo kissed [e] on the nose", where [e] denotes the gap. Brain responses to the filled gap were compared to a control condition using adverb-relative clauses with identical substrings: "The weekend that the hippo kissed the camel on the nose [e]…". Here, the same noun phrase is not unexpected because the adverb gap occurs later in the structure. We hypothesized that a filled gap would elicit a prediction error brain signal in the form of an early anterior negativity, as we have previously observed in adults. We found an early (bilateral) anterior negativity to the filled gap in a control group of children with Typical Development (TD), but the children with DLD exhibited no brain response to the filled gap during the same early time window. This suggests that children with DLD fail to predict that a relativized object should correspond to an empty position after the relative clause verb, suggesting an impairment in predictive processing. We discuss how this lack of a prediction error signal can interact with language acquisition and result in DLD.

EEG Beta-Band Spectral Entropy Can Predict the Effect of Drug Treatment on Pain in Patients With Herpes Zoster

BACKGROUND

Medication is the main approach for early treatment of herpes zoster, but it could be ineffective in some patients. It is highly desired to predict the medication responses to control the degree of pain for herpes zoster patients. The present study is aimed to elucidate the relationship between medication outcome and neural activity using EEG and to establish a machine learning model for early prediction of the medication responses from EEG.

METHODS

The authors acquired and analyzed eye-closed resting-state EEG data 1 to 2 days after medication from 70 herpes zoster patients with different drug treatment outcomes (measured 5-6 days after medication): 45 medication-sensitive pain patients and 25 medication-resistant pain patients. EEG power spectral entropy of each frequency band was compared at each channel between medication-sensitive pain and medication-resistant pain patients, and those features showing significant difference between two groups were used to predict medication outcome with different machine learning methods.

RESULTS

Medication-sensitive pain patients showed significantly weaker beta-band power spectral entropy in the central-parietal regions than medication-resistant pain patients. Based on these EEG power spectral entropy features and a k-nearest neighbors classifier, the medication outcome can be predicted with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity, and an area under the receiver operating characteristic curve of 0.85.

CONCLUSIONS

EEG beta-band power spectral entropy in the central-parietal region is predictive of the effectiveness of drug treatment on herpes zoster patients, and it could potentially be used for early pain management and therapeutic prognosis.

Are covered faces eye-catching for us? The impact of masks on attentional processing of self and other faces during the COVID-19 pandemic

During the COVID-19 pandemic, we have been confronted with faces covered by surgical-like masks. This raises a question about how our brains process this kind of visual information. Thus, the aims of the current study were twofold: (1) to investigate the role of attention in the processing of different types of faces with masks, and (2) to test whether such partial information about faces is treated similarly to fully visible faces. Participants were tasked with the simple detection of self-, close-other's, and unknown faces with and without a mask; this task relies on attentional processes. Event-related potential (ERP) findings revealed a similar impact of surgical-like masks for all faces: the amplitudes of early (P100) and late (P300, LPP) attention-related components were higher for faces with masks than for fully visible faces. Amplitudes of N170 were similar for covered and fully visible faces, and sources of brain activity were located in the fusiform gyri in both cases. Linear Discriminant Analysis (LDA) revealed that irrespective of whether the algorithm was trained to discriminate three types of faces either with or without masks, it was able to effectively discriminate faces that were not presented in the training phase.

Skin Color and Attractiveness Modulate Empathy for Pain: An Event-Related Potential Study

Although racial in-group bias in empathy for pain has been reported, empathic responses to others’ pain may be influenced by other characteristics besides race. To explore whether skin color and attractiveness modulate empathy for pain, we recorded 24 participants’ reactions to painful faces from racial in-group members with different skin color (fair, wheatish, or dark) and attractiveness (more or less attractive) using event-related potentials (ERPs). Results showed that, for more attractive painful faces, dark skin faces were judged as less painful and elicited smaller N2 amplitudes than fair- and wheatish-skinned faces. However, for less attractive faces, there were no significant differences among the three skin colors. Our findings suggest that empathy for pain toward racial in-group members may be influenced by skin color and attractiveness.

Empathy for pain in individuals with autistic traits during observation of static and dynamic stimuli

Previous studies have reported that individuals with autistic traits, like those with Autism Spectrum Disorder (ASD), may have impaired empathic responses when observing static stimuli of others' pain. However, it remains unclear whether individuals with autistic traits exhibit impaired empathy for pain in response to dynamic stimuli. The present study addressed this question by recruiting 529 individuals whose autistic traits were assessed using the autism-spectrum quotient (AQ) questionnaire. Thirty participants who scored within the top 10% and bottom 10% on the AQ were selected into High-AQ and Low-AQ groups, respectively. This study employed painful whole-body action pictures and videos as static and dynamic stimuli. Both groups were instructed to judge whether the models in the stimuli were experiencing pain, and their reaction times, accuracy and event-related potential (ERP) data were recorded. Results showed that the P2 amplitudes were larger in the High-AQ group than in the Low-AQ group when viewing painful static stimuli, while no difference between the two groups was found when viewing painful dynamic stimuli. These results suggest that autistic traits influenced the emotional processing of others' pain in response to static stimuli.

Whole-brain propagating patterns in human resting-state brain activities

Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. "Whole-brain" propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.

Mobile Electroencephalography for Studying Neural Control of Human Locomotion

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

Time-series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattended listening, and frequency-modulated tones classification

BACKGROUND

The nonstationarity of EEG/MEG signals is important for understanding the functioning of the human brain. From our previous research we know that short, 250-500-ms MEG signals are variance-nonstationary. The covariance of a stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time.

NEW METHOD

We analyse data from 148-channel MEG, which represent rest state, unattended listening, and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain and for the dominant frequencies of 8-12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity, and gaussianity, and propose ARMA-modelling for their description.

RESULTS

The analysed time series have weak-stationarity properties independently of the functional state of the brain and channel localization. Only a small percentage of them, mostly related to the cognitive task, reveal nonstationarity. The obtained mathematical models show that the spectral density of the analysed signals depends on only two to three previous trials.

COMPARISON WITH EXISTING METHODS

The presented method has limitations related to FFT resolution and univariate models, but it is computationally simple and allows obtaining a low-complex stochastic model of the EEG/MEG spectrum variability.

CONCLUSIONS

Although physiological short-time MEG signals are in principle nonstationary in time, their power spectrum at the dominant (alpha) frequencies varies as a weakly stationary process. The proposed methodology has possible applications in prediction of EEG/MEG spectral properties in theoretical and clinical neuroscience.

Sub-second transient activated patterns to sad expressions in major depressive disorders discovered via hidden Markov model

The pathological mechanisms of major depressive disorders (MDDs) is associated with the overexpression of negative emotions, and the fast transient-activated patterns underlying overrepresentation in depression still remain to be revealed to date. We hypothesized that the aberrant spatiotemporal attributes of the process of sad expressions are related to the neuropathology of MDD and help to detect the depression severity. We enrolled a total of 96 subjects including 47 patients with MDD and 49 healthy controls (HCs), and recorded their magnetoencephalography data under a sad expression recognition task. A hidden Markov model (HMM) was applied to separate the whole neural activity into several brain states, then to characterize the dynamics. To find the disrupted temporal-spatial characteristics, power estimations and fractional occupancy (FO) of each state were estimated and contrasted between MDDs and HCs. Three states were found over the period of emotional stimuli processing procedure. The early visual stage (0-270 ms) was mainly manifested by state 1, and the emotional information processing stage (270-600 ms) was manifested by state 2, while the state 3 remained a steady proportion across the whole period. MDDs activated statistically more in limbic system during state 2 (p = 0.0045) and less in frontoparietal control network during state 3 (p = 5.38 × 10^-5 ) relative to HCs. Hamilton Depression Rating Scale scores were significantly correlated with the predicted disorder severity using FO values (p = 0.0062, r = 0.3933). Relative to HCs, MDDs perceived the sad contents quickly and spent more time overexpressing the negative emotions. These phenomena indicated MDD patients might easily indulge in negative emotion and neglect other things. Furthermore, temporal descriptors built by HMM could be potential biomarkers for identifying the severity of depression disorders.

Efficacy of a Single-Task ERP Measure to Evaluate Cognitive Workload During a Novel Exergame

This study aimed to validate the efficacy of single-task event-related potential (ERP) measures of cognitive workload to be implemented in exergame-based rehabilitation. Twenty-four healthy participants took part in a novel gamified balance task where task-irrelevant auditory tones were presented in the background to generate ERPs in the participants’ electroencephalogram (EEG) as a measure of cognitive workload. For the balance task, a computer-based tilt-ball game was combined with a balance board. Participants played the game by shifting their weight to tilt the balance board, which moved a virtual ball to score goals. The game was manipulated by adjusting the size of the goalposts to set three predefined levels of game difficulty (easy, medium, and hard). The participant’s experience of game difficulty was evaluated based on the number of goals scored and their subjective reporting of perceived difficulty. Participants experienced a significant difference in the three levels of task difficulty based on the number of goals scored and perceived difficulty (p < 0.001). Post hoc analysis revealed the lowest performance for the hardest level. The mean amplitude of the N1 ERP component was used to measure the cognitive workload associated with the three difficulty levels. The N1 component’s amplitude decreased significantly (p < 0.001), with an increase in the task difficulty. Moreover, the amplitude of the N1 component for the hard level was significantly smaller compared to medium (p = 0.0003) and easy (p < 0.001) levels. These results support the efficacy of the N1 ERP component to measure cognitive workload in dynamic and real-life scenarios such as exergames and other rehabilitation exercises.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Self-face and emotional faces-are they alike?

The image of one’s own face is a particularly distinctive feature of the self. The self-face differs from other faces not only in respect of its familiarity but also in respect of its subjective emotional significance and saliency. The current study aimed at elucidating similarities/dissimilarities between processing of one’s own face and emotional faces: happy faces (based on the self-positive bias) and fearful faces (because of their high perceptual saliency, a feature shared with self-face). Electroencephalogram data were collected in the group of 30 participants who performed a simple detection task. Event-related potential analyses indicated significantly increased P3 and late positive potential amplitudes to the self-face in comparison to all other faces: fearful, happy and neutral. Permutation tests confirmed the differences between the self-face and all three types of other faces for numerous electrode sites and in broad time windows. Representational similarity analysis, in turn, revealed distinct processing of the self-face and did not provide any evidence in favour of similarities between the self-face and emotional (either negative or positive) faces. These findings strongly suggest that the self-face processing do not resemble those of emotional faces, thus implying that prioritized self-referential processing is driven by the subjective relevance of one’s own face.

Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

Money gained through suffering is less valuable: Pain reduces the sensitivity to outcome magnitude in monetary decision making

Enduring pain would change individuals' behavioral preferences and neural responses in multiple decision-making tasks. Yet few studies have investigated how money's casual association with painful experience would modify people's decisions with it. It is an important and common social situation. The present study investigated how money's association with pain influences the way people make monetary decisions. Participants gambled with money that they earned in four different ways: enduring pain (Pain), randomly assigned (Random), non-painful effort task (Effort), and observing negative images (NO). Results revealed two different patterns. In the Random and Pain conditions, participants were not sensitive to the gambling risk such that they more randomly chose high- and low-risk options; the differences in FNR amplitude triggered by high- and low-risk choices were comparable on the neural level. In contrast, in the Effort and NO conditions, participants showed higher sensitivity to the magnitude and larger differences in FNR amplitudes between high- and low-risk choices. These findings suggested that pain cannot increase the subjective value of monetary gain like other non-painful efforts can do and monetary rewards may not be the optimal way to compensate for the physical suffering or loss in society.

Processing of expressions by individuals with autistic traits: Empathy deficit or sensory hyper-reactivity?

Individuals with autistic traits display impaired social interaction and communication in everyday life, but the underlying cognitive neural mechanisms remain very unclear and still remain controversial. The mind-blindness hypothesis suggests that social difficulties in individuals with autistic traits are caused by empathy impairment in individuals; however, the intense world theory suggests that these social difficulties are caused by sensory hyper-reactivity and sensory overload, rather than empathy impairment. To further test these two theories, this study investigated event-related potentials (ERPs) to explore the cognitive neural processing of repetitive expressions in individuals with autistic traits. This study employed the Mandarin version of the autism-spectrum quotient (AQ) to assess autistic traits in 2,502 healthy adults. Two subset groups were used, e.g., the participants of a high-AQ group were randomly selected among the 10% of individuals with the highest AQ scores; similarly, the participants in the low-AQ group were randomly selected from the 10% of participants with the lowest AQ scores. In an experiment, three different facial expressions (positive, neutral, or negative) of the same person were presented successively and pseudo-randomly in each trial. Participants needed to define the expression of the face that was presented last. The results showed that compared with the low-AQ group, the high-AQ group exhibited higher P1 amplitudes induced by the second and third presented expressions, as well as higher P3 amplitudes induced by the third presented negative expressions. This indicates that individuals with autistic traits may experience overly strong perception, attention, and cognitive evaluation to repetitive expressions, particularly negative expressions. This result supports the intense world theory more strongly than the mind-blindness hypothesis.

Spectral features of cortical auditory evoked potentials inform hearing threshold and intensity percepts in acoustic and electric hearing

Objective. Stimulus-elicited changes in electroencephalography (EEG) recordings can be represented using Fourier magnitude and phase features (Makeiget al(2004Trends Cogn. Sci.8204-10)). The present study aimed to quantify how much information about hearing responses are contained in the magnitude, quantified by event-related spectral perturbations (ERSPs); and the phase, quantified by inter-trial coherence (ITC). By testing if one feature contained more information and whether this information was mutually exclusive to the features, we aimed to relate specific EEG magnitude and phase features to hearing perception.Approach.EEG responses were recorded from 20 adults who were presented with acoustic stimuli, and 20 adult cochlear implant users with electrical stimuli. Both groups were presented with short, 50 ms stimuli at varying intensity levels relative to their hearing thresholds. Extracted ERSP and ITC features were inputs for a linear discriminant analysis classifier (Wonget al(2016J. Neural. Eng.13036003)). The classifier then predicted whether the EEG signal contained information about the sound stimuli based on the input features. Classifier decoding accuracy was quantified with the mutual information measure (Cottaris and Elfar (2009J. Neural. Eng.6026007), Hawelleket al(2016Proc. Natl Acad. Sci.11313492-7)), and compared across the two feature sets, and to when both feature sets were combined.Main results. We found that classifiers using either ITC or ERSP feature sets were both able to decode hearing perception, but ITC-feature classifiers were able to decode responses to a lower but still audible stimulation intensity, making ITC more useful than ERSP for hearing threshold estimation. We also found that combining the information from both feature sets did not improve decoding significantly, implying that ERSP brain dynamics has a limited contribution to the EEG response, possibly due to the stimuli used in this study.Significance.We successfully related hearing perception to an EEG measure, which does not require behavioral feedback from the listener; an objective measure is important in both neuroscience research and clinical audiology.

Effects of rTMS on the brain: is there value in variability?

The ability of repetitive transcranial magnetic stimulation (rTMS) to non-invasively induce neuroplasticity in the human cortex has opened exciting possibilities for its application in both basic and clinical research. Changes in the amplitude of motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation has so far provided a convenient model for exploring the neurophysiology of rTMS effects on the brain, influencing the ways in which these stimulation protocols have been applied therapeutically. However, a growing number of studies have reported large inter-individual variability in the mean MEP response to rTMS, raising legitimate questions about the usefulness of this model for guiding therapy. Although the increasing application of different neuroimaging approaches has made it possible to probe rTMS-induced neuroplasticity outside the motor cortex to measure changes in neural activity that impact other aspects of human behaviour, the high variability of rTMS effects on these measurements remains an important issue for the field to address. In this review, we seek to move away from the conventional facilitation/inhibition dichotomy that permeates much of the rTMS literature, presenting a non-standard approach for measuring rTMS-induced neuroplasticity. We consider the evidence that rTMS is able to modulate an individual's moment-to-moment variability of neural activity, and whether this could have implications for guiding the therapeutic application of rTMS.

Using multilevel models for the analysis of event-related potentials

Multilevel modeling (MLM) is becoming increasingly accessible and popular in the analysis of event-related potentials (ERPs). In this article, we review the benefits of MLM for analyzing psychophysiological data, which often contains repeated observations within participants, and introduce some of the decision-making points in the analytic process, including how to set up the data set, specify the model, conduct hypothesis tests, and visualize the model estimates. We highlight how the use of MLM can extend the types of theoretical questions that can be answered using ERPs, including investigations of how ERPs vary meaningfully across trials within a testing session. We also address reporting practices and provide tools to calculate effect sizes and simulate power curves. Ultimately, we hope this review contributes to emerging best practices for the use of MLM with psychophysiological data.