Group membership modulates empathic neural responses to pain in deaf individuals.

Empathy deficiencies are prevalent among deaf individuals. It has yet to be determined whether they exhibit an ingroup bias in empathic responses. This study employed explicit and implicit empathy tasks (i.e. attention-to-pain-cue [A-P] task and attention-to-nonpain-cue [A-N] task) to explore the temporal dynamics of neural activities when deaf individuals were processing painful/nonpainful stimuli from both ingroup models (deaf people) and outgroup models (hearing people), which aims to not only assist deaf individuals in gaining a deeper understanding of their intergroup empathy traits but also to aid in the advancement of inclusive education. In the A-P task, we found that (i) ingroup priming accelerated the response speed to painful/nonpainful pictures; (ii) the N2 amplitude of painful pictures was significantly more negative than that of nonpainful pictures in outgroup priming trials, whereas the N2 amplitude difference between painful and nonpainful pictures was not significant in ingroup priming trials. For N1 amplitude of the A-N task, we have similar findings. However, this pattern was reversed for P3/late positive component amplitude of the A-P task. These results suggest that the deaf individuals had difficulty in judging whether hearing individuals were in pain. However, their group identification and affective responses could shape the relatively early stage of pain empathy.

An ERP investigation of electrocortical responses in pain empathy from childhood through adolescence into adulthood.

Only a few studies investigated the neurodevelopment of pain empathy. Here, the temporal dynamics of electrocortical processes in pain empathy during individual neurodevelopment from childhood through adolescence into adulthood, along with the moderation effect of top-down attention, were investigated using the event-related potential (ERP) technique. To investigate the role of top-down attention in empathy development, both A-P task and A-N task were conducted. In the A-P and A-N task, participants are instructed to judge whether the models in pictures were painful or non-painful and count the number of limbs in pictures, respectively. We found that compared to the adolescent and adult groups, the children group responded significantly worse, along with stronger neural responses in both tasks. Compared to the adolescent and adult groups, the differential amplitudes between painful and non-painful conditions of P2, N2 and P3 were significantly larger in the children group. Moreover, this P3 differential amplitude could only be modulated by age in the A-P task. These results suggest that the capacity to empathize has not yet attained complete development in these children. Significantly more attention resources were involuntarily attracted by the nociceptive cues in these children, which could also reflect the immaturity of empathy ability in these children.

Resting-state EEG Microstate Features Can Quantitatively Predict Autistic Traits in Typically Developing Individuals.

Autism spectrum disorder (ASD) is not a discrete disorder and that symptoms of ASD (i.e., so-called "autistic traits") are found to varying degrees in the general population. Typically developing individuals with sub-clinical yet high-level autistic traits have similar abnormities both in behavioral performances and cortical activation patterns to individuals diagnosed with ASD. Thus it's crucial to develop objective and efficient tools that could be used in the assessment of autistic traits. Here, we proposed a novel machine learning-based assessment of the autistic traits using EEG microstate features derived from a brief resting-state EEG recording. The results showed that: (1) through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and correlation analysis, the mean duration of microstate class D, the occurrence rate of microstate class A, the time coverage of microstate class D and the transition rate from microstate class B to D were selected to be crucial microstate features which could be used in autistic traits prediction; (2) in the support vector regression (SVR) model, which was constructed to predict the participants' autistic trait scores using these four microstate features, the out-of-sample predicted autistic trait scores showed a significant and good match with the self-reported scores. These results suggest that the resting-state EEG microstate analysis technique can be used to predict autistic trait to some extent.

The neurophysiological mechanism of valence-space congruency effect: evidence from spatial Stroop task and event-related EEG features.

Metaphors commonly represent mental representations of abstract concepts. One example is the valence-space metaphor (i.e., positive word-up, negative word-down), which suggests that the vertical position of positive/negative words can modulate the evaluation of word valence. Here, the spatial Stroop task and electroencephalography (EEG) techniques were used to explore the neural mechanism of the valence-space congruency effect in valence-space metaphors. This study showed that the reaction time of the congruent condition (i.e., positive words at the top and negative words at the bottom of the screen) was significantly shorter than that of the incongruent condition (i.e., positive words at the bottom and negative words at the top of the screen), while the accuracy rate of the congruent condition was significantly larger than that of the incongruent condition. The analysis of the amplitudes of event-related potential components revealed that congruency between the vertical position and valence of Chinese words could significantly modulate the amplitude of attention allocation-related P2 component and semantic violations related N400 component. Moreover, statistical tests conducted on the post-stimulus inter-trial phase coherence (ITPC) found that the ITPC value of an alpha band region of interest (8-12 Hz, 100-300 ms post-stimulus) in the time-frequency plane of the congruent condition was significantly larger than that of the incongruent condition. Above all, the current study proved the existence of the space-valence congruency effect in Chinese words and provided some interesting neurophysiological mechanisms regarding the valence-space metaphor.

Negative inhibition is poor in sub-threshold depression individuals: Evidence from ERP and a Go/No-go task.

In this study, Go/No-go task combined with ERP technology were used to explore the characteristics of negative emotion inhibition in SD and healthy individuals and whether there are differences between negative emotion inhibition and neutral emotion inhibition in SD. The results showed that SD showed the same poor negative inhibition as depressive patients in behavior, but there was no significant difference between SD and CG in ERPs. Overall, compared with neutral emotional information, negative emotional information would reduce attention control in conflict processing, lead to faster conflict processing, attract attention, occupy more cognitive resources, and be more difficult to inhibit. It is concluded that the negative attention bias of SD individuals is only reflected in the bottom-up stimulation processing, but has not developed into the top-down cognitive control, which also suggests that the corresponding intervention measures at the early stage of depression may have better effects.

Behavioral and electrophysiological evidence for valence-space congruency effect of emotional pictures in a spatial Stroop task.

Previous studies have shown that people implicitly associate the emotional valence of abstract words with vertical position (i.e., positive words up, negative words down), resulting in the so-called valence-space congruency effect. Research has demonstrated that there is a valence-space congruency effect when it comes to emotional words. It's interesting to see that whether the emotional pictures with different levels of valence are mapped to distinct vertical space positions. Here, the event-related potential (ERP) and time-frequency techniques were employed to investigate the neural basis of the valence-space congruency effect of emotional pictures in a spatial Stroop task. Firstly, this study showed that the reaction time of the congruent condition (i.e., positive pictures in the top and negative pictures in the bottom of the screen) was significantly shorter than that of the incongruent condition (i.e., positive pictures in the bottom and negative pictures in the top of the screen), suggesting that exposure to stimuli with positive or negative valence, regardless of whether these stimuli were comprised of words or pictures, would be enough to invoke the vertical metaphor. Moreover, we found that the congruency between the vertical position and the valence of emotional pictures could significantly modulate the amplitude of the P2 component and the Late Positive Component (LPC) in ERP waveforms, as well as the post-stimulus alpha-ERD in the time-frequency plane. This study has conclusively demonstrated the presence of a space-valence congruency effect in emotional pictures and has elucidated the underlying neurophysiological mechanisms associated with the valence-space metaphor.

Scale-Free Dynamics in Instantaneous Alpha Frequency Fluctuations: Validation, Test-Retest Reliability and Its Relationship with Task Manipulations.

Previous studies showed that scale-free structures and long-range temporal correlations are ubiquitous in physiological signals (e.g., electroencephalography). This is supposed to be associated with optimized information processing in human brain. The instantaneous alpha frequency (IAF) (i.e., the instantaneous frequency of alpha band of human EEG signals) may dictate the resolution at which information is sampled and/or processed by cortical neurons. To the best of our knowledge, no research has examined the scale-free dynamics and potential functional significance of IAF. Here, through three studies (Study 1: 25 participants; Study 2: 82 participants; Study 3: 26 participants), we investigated the possibility that time series of IAF exhibit scale-free property through maximum likelihood based detrended fluctuation analysis (ML-DFA). This technique could provide the scaling exponent (i.e., DFA exponent) on the basis of presence of scale-freeness being validated. Then the test-retest reliability (Study 1) and potential influencing factors (Study 2 and Study 3) of DFA exponent of IAF fluctuations were investigated. Firstly, the scale-free property was found to be inherent in IAF fluctuations with fairly high test-retest reliability over the parietal-occipital region. Moreover, the task manipulations could potentially modulate the DFA exponent of IAF fluctuations. Specifically, in Study 2, we found that the DFA exponent of IAF fluctuations in eye-closed resting-state condition was significantly larger than that in eye-open resting-state condition. In Study 3, we found that the DFA exponent of IAF fluctuations in eye-open resting-state condition was significantly larger than that in visual n-back tasks. The DFA exponent of IAF fluctuations in the 0-back task was significantly larger than in the 2-back and 3-back tasks. The results in studies 2 and 3 indicated that: (1) a smaller DFA exponent of IAF fluctuations should signify more efficient online visual information processing; (2) the scaling property of IAF fluctuations could reflect the physiological arousal level of participants.

Aberrant dynamic minimal spanning tree parameters within default mode network in patients with autism spectrum disorder.

The altered functional connectivity (FC) level and its temporal characteristics within certain cortical networks, such as the default mode network (DMN), could provide a possible explanatory framework for Autism spectrum disorder (ASD). In the current study, we hypothesized that the topographical organization along with its temporal dynamics of the autistic brain measured by temporal mean and variance of complex network measures, respectively, were significantly altered, which may further explain the autistic symptom severity in patients with ASD. To validate these hypotheses, the precise FCs between DMN regions at each time point were calculated using the resting-state functional magnetic resonance imaging (fMRI) datasets from the Autism Brain Imaging Data Exchange (ABIDE) project. Then, the minimal spanning tree (MST) technique was applied to construct a time-varying complex network of DMN. By analyzing the temporal mean and variance of MST parameters and their relationship with autistic symptom severity, we found that in persons with ASD, the information exchange efficiencies between cortical regions within DMN were significantly lower and more volatile compared with those in typical developing participants. Moreover, these alterations within DMN were closely associated with the autistic symptom severity of the ASD group.

Aberrant dynamic functional connectivity features within default mode network in patients with autism spectrum disorder: evidence from dynamical conditional correlation.

Autism spectrum disorder (ASD) is characterized by aberrant functional connectivity (FC) within/between certain large-scale brain networks. Although relatively lower level of FC between default mode network (DMN) regions (i.e., DMN-FC) has been detected in many previous studies, they failed to capture the temporal dynamic features of DMN-FC and were limited by small sample size. Here, the dynamical conditional correlation, which could assess precise FC at each time point and has been proved to be a technique with high test-retest reliability, was applied to investigate the DMN-FC pattern of patients with ASD from the Autism Brain Imaging Data Exchange, which included functional and structural brain imaging data of more than 1000 participants. The data analysis here showed that compared to typical developing (TD) participants, patients with ASD exhibited significantly lower mean DMN-FC level across recording time, but significantly higher variance of DMN-FC level across recording time. Moreover, these alterations were significantly associated with symptom severity of patients, especially their impaired communication skills and repetitive behaviors. These results support the view that aberrant temporal dynamic of FC within DMN is an important neuropathological feature of ASD and is a potential biomarker for ASD diagnosis. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-021-09723-9.

Altered Temporal Structure of Neural Phase Synchrony in Patients With Autism Spectrum Disorder.

Functional connectivity, quantified by phase synchrony, between brain regions is known to be aberrant in patients with autism spectrum disorder (ASD). Here, we evaluated the long-range temporal correlations of time-varying phase synchrony (TV-PS) of electrocortical oscillations in patients with ASD as well as typically developing people using detrended fluctuation analysis (DFA) after validating the scale-invariance of the TV-PS time series. By comparing the DFA exponents between the two groups, we found that those of the TV-PS time series of high-gamma oscillations were significantly attenuated in patients with ASD. Furthermore, the regions involved in aberrant TV-PS time series were mainly within the social ability and cognition-related cortical networks. These results support the notion that abnormal social functions observed in patients with ASD may be caused by the highly volatile phase synchrony states of electrocortical oscillations.

Is left-behind a real reason for children's social cognition deficit? An fNIRS study on the effect of social interaction on left-behind preschooler's prefrontal activation.

The left-behind phenomenon, caused by parent out-migration, has become a common social issue and might lead to long-term and potential risks for children in rural areas of China. It is important to investigate the effect of social interaction on prefrontal activation of left-behind children in China because of possible effects of parent out-migration on children's social cognition. We recruited 81 rural Chinese preschoolers aged 52-76 months (mean = 64.98 ± 6.321 months) preschoolers with three different statuses of parental out-migration (including non-, partially, and completely left-behind children). Using functional Near-Infrared Spectroscopy (fNIRS), we compared behavior and brain activation and in three groups (non-, partially-, completely-left-behind children) under two different social interaction conditions (child-teacher and child-stranger situation). Results revealed that initiating joint attention (IJA) may evoke higher brain activation than responding to joint attention (RJA) in the prefrontal cortex (PFC), especially in the case of initiating joint attention with the stranger. In addition, the activation of joint attention was positively correlated with children's language score, cognitive flexibility, and facial expression recognition. More importantly, partially-left-behind children evoked higher brain activation in the IJA condition and presented a higher language level than completely/non-left-behind children. The current study provides insight into the neural basis of left-behind children's development and revealed for the first time that family economic level and left-behind status may contribute to the lower social cognition.

Interbrain Synchrony of Team Collaborative Decision-Making: An fNIRS Hyperscanning Study.

In many situations, decision-making behaviors are mostly composed of team patterns (i.e., more than two persons). However, brain-based models that inform how team interactions contribute and impact team collaborative decision-making (TCDM) behavior, is lacking. To examine the neural substrates activated during TCDM in realistic, interpersonal interaction contexts, dyads were asked to model TCDM toward their opponent, in a multi-person prisoner's dilemma game, while neural activity was measured using functional near infrared spectroscopy. These experiments resulted in two main findings. First, there are different neural substrates between TCDM and ISDM, which were modulated by social environmental cues. i.e., the low incentive reward yielded higher activation within the left inferior frontal gyrus (IFG), in individual separately decision-making (ISDM) stage while the dorsolateral prefrontal cortex (DLPFC) and the middle frontopolar area was activated in TCDM stage. The high incentive reward evoked a higher interbrain synchrony (IBS) value in the right IFG in TCDM stage. Second, males showed higher activation in the DLPFC and the middle frontopolar area during ISDM, while females evoked higher IBS in the right IFG during TCDM. These sex effects suggest that in individual social dilemma situations, males and females may separately depend on non-social and social cognitive ability to make decisions, while in the social interaction situations of TCDM, females may depend on both social and non-social cognitive abilities. This study provide a compelling basis and interesting perspective for future neuroscience work of TCDM behaviors.

Interbrain Synchrony in the Expectation of Cooperation Behavior: A Hyperscanning Study Using Functional Near-Infrared Spectroscopy.

Expectation of others' cooperative behavior plays a core role in economic cooperation. However, the dynamic neural substrates of expectation of cooperation (hereafter EOC) are little understood. To fully understand EOC behavior in more natural social interactions, the present study employed functional near-infrared spectroscopy (fNIRS) hyperscanning to simultaneously measure pairs of participants' brain activations in a modified prisoner's dilemma game (PDG). The data analysis revealed the following results. Firstly, under the high incentive condition, team EOC behavior elicited higher interbrain synchrony (IBS) in the right inferior frontal gyrus (rIFG) than individual EOC behavior. Meanwhile, the IBS in the IFG could predict the relationship between empathy/agreeableness and EOC behavior, and this prediction role was modulated by social environmental cues. These results indicate the involvement of the human mirror neuron system (MNS) in the EOC behavior and the different neural substrates between team EOC and individual EOC, which also conform with theory that social behavior was affected by internal (i.e., empathy/agreeableness) and external factors (i.e., incentive). Secondly, female dyads exhibited a higher IBS value of cooperative expectation than male dyads in the team EOC than the individual EOC in the dorsal medial prefrontal cortex (DMPFC), while in the individual EOC stage, the coherence value of female dyads was significantly higher than that of male dyads under the low incentive reward condition in the rIFG. These sex effects thus provide presumptive evidence that females are more sensitive to environmental cues and also suggest that during economic social interaction, females' EOC behavior depends on more social cognitive abilities. Overall, these results raise intriguing questions for future research on human cooperative behaviors.

Attenuated long-range temporal correlations of electrocortical oscillations in patients with autism spectrum disorder.

The aim of this study was to investigate the long-range temporal correlations (LRTCs) of instantaneous amplitude of electrocortical oscillations in patients with autism spectrum disorder (ASD). Using the resting-state electroencephalography (EEG) of 15 patients with ASD (aged between 5˜18 years, mean age = 11.6 years, SD = 4.4 years) and 18 typical developing (TD) people (aged between 5˜18 years, mean age = 8.9 years, SD = 2.4 years), we estimated the LRTCs of neuronal oscillations amplitude of 84 predefined cortical regions of interest using detrended fluctuation analysis (DFA) after confirming the presence of scale invariance. We found that the DFA exponents of instantaneous amplitude of beta and low-gamma oscillations were significantly attenuated in patients with ASD compared to TD participants. Moreover, the regions with attenuated DFA exponent were mainly located in social functions related cortical networks, including the default mode network (DMN), the mirror neuron system (MNS) and the salience network (SN). These findings suggest that ASD is associated with highly volatile neuronal states of electrocortical oscillations, which may be related to social and cognitive dysfunction in patients with ASD.

Differences in brain signal complexity between experts and novices when solving conceptual science problem: a functional near-infrared spectroscopy study.

Assessing the result of conceptual change (i.e., whether an individual has come to correctly understand a science concept) is important in science education, however traditional assessment methods lack objectivity. In this study, permutation entropy (PE) based complexity, a kind of entropy used to quantify the complexity describing the uncertainty of time series, was explored by the functional near-infrared spectroscopy to seek an objective neurobiological indicator for this assessment. Two groups of participants, engineering students (classified as "experts") and humanities students (classified as "novices"), were tested on their conceptions to discriminate the speed of cars according to the animation, while the hemodynamic response was recorded over their inferior frontal gyrus (IFG). The activation analysis, PE based complexity analysis, and k-means clustering analysis were conducted. The results indicated that experts performed the task better than novices in behavioral performances, and PE values in the IFG were smaller for experts, especially in the right IFG. Furthermore, the k-means clustering analysis showed that the PE could be a feature to classify the students into two groups. It is concluded that the PE is a promising neurobiological indicator for assessment of this kind.

Aberrant Intrinsic Brain Activity in Patients with Autism Spectrum Disorder: Insights from EEG Microstates.

Autism spectrum disorder (ASD) involves aberrant organization and functioning of large-scale brain networks. The aim of this study was to examine whether the resting-state EEG microstate analysis could provide novel insights into the abnormal temporal and spatial properties of intrinsic brain activities in patients with ASD. To achieve this goal, EEG microstate analysis was conducted on the resting-state EEG datasets of 15 patients with ASD and 18 healthy controls from the Healthy Brain Network. The parameters (i.e., duration, occurrence rate, time coverage and topographical configuration) of four classical microstate classes (i.e., class A, B, C and D) were statistically tested between two groups. The results showed that: (1) the occurrence rate and time coverage of microstate class B in ASD group were significantly larger than those in control group; (2) the duration of microstate class A, the duration and time coverage of microstate class C were significantly smaller than those in control group; (3) the map configuration and occurrence rate differed significantly between two groups for microstate class D. These results suggested that EEG microstate analysis could be used to detect the deviant functions of large-scale cortical activities in ASD, and may provide indices that could be used in clinical researches of ASD.

Brain-to-brain synchronization of the expectation of cooperation behavior: A fNIRS hyperscanning study.

Expectation of cooperation (hereafter EOC) plays an important role in social dilemmas. In the present study, participant dyads performed an improved prisoner's dilemma game, with their prefrontal cortex and inferior frontal gyrus recorded via the functional near-infrared spectroscopy (fNIRS) hyperscanning technique. Inter brain results indicated significant inter-brain neural synchronization (INS) across participant pairs' inferior frontal gyrus (IFG) in high-powered incentives and defective expectation. Furthermore, the agreeableness proved to be a predictor of cooperative expectation in the inter brain frame. These results may revealed the inter-brain underlying substrate of EOC in social dilemmas and indicated the involvement of the mentalizing network and human mirror neuron system network in social dilemmas.

Novel analysis of fNIRS acquired dynamic hemoglobin concentrations: application in young children with autism spectrum disorder.

A novel analysis of the spatial complexity of functional connectivity (SCFC) was proposed to investigate the spatial complexity of multiple dynamic functional connectivity series in an fNIRS study, using an approach combining principal component analysis and normalized entropy. The analysis was designed to describe the complex spatial features of phase synchrony based dynamic functional connectivity (dFC), which are unexplained in traditional approaches. The feasibility and validity of this method were verified in a sample of young patients with autism spectrum disorders (ASD). Our results showed that there were information exchange deficits in the right prefrontal cortex (PFC) of children with ASD, with markedly higher interregion SCFCs between the right PFC and other brain regions than those of normal controls. Furthermore, the global SCFC was significantly higher in young patients with ASD, along with considerably higher intraregion SCFCs in the prefrontal and temporal lobes which represents more diverse information exchange in these areas. The study suggests a novel method to analyze the fNIRS required dynamic hemoglobin concentrations by using concepts of SCFC. Moreover, the clinical results extend our understanding of ASD pathology, suggesting the crucial role of the right PFC during the information exchange process.

Attenuation of long-range temporal correlations of neuronal oscillations in young children with autism spectrum disorder.

Although autism spectrum disorder (ASD) was previously found to be associated with aberrant brain structure, neuronal amplitudes and spatial neuronal interactions, surprisingly little is known about the temporal dynamics of neuronal oscillations in this disease. Here, the hemoglobin concentration signals (i.e., oxy-Hb and deoxy-Hb) of young children with ASD and typically developing (TD) children were recorded via functional near infrared spectroscopy (fNIRS) when they were watching a cartoon. The long-range temporal correlations (LRTCs) of hemoglobin concentration signals were quantified using detrended fluctuation analysis (DFA). Compared with TD group, the DFA exponents of young children with ASD were significantly smaller over left temporal region for oxy-Hb signal, and over bilateral temporo-occipital regions for deoxy-Hb signals, indicating a shift-to-randomness of brain oscillations in the children with ASD. Testing the relationship between age and DFA exponents revealed that this association could be modulated by autism. The correlation coefficients between age and DFA exponents were significantly more positive in TD group, compared to those in ASD group over several brain regions. Furthermore, the DFA exponents of oxy-Hb in left temporal region were negatively correlated with autistic symptom severity. These results suggest that the decreased DFA exponent of hemoglobin concentration signals may be one of the pathologic changes in ASD, and studying the temporal structure of brain activity via fNIRS technique may provide physiological indicators for autism.

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.