Neural Patterns Research Articles

Different patterns of MRI structural alterations in schizophrenia spectrum disorders with delusions of influence vs persecution

IntroductionThere is strong evidence that delusions are associated with cortical and subcortical structural alterations. However, whether these abnormalities differ in different types of delusions within schizophrenia spectrum disorders remains unclear.ObjectivesWe aimed at exploring structural neural patterns underlying influence/persecutory delusions across diagnostic categories within the schizophrenia-spectrum disorders.MethodsTwenty six right-handed patients with delusional disorder (n=7) and schizophrenia (n=19), presenting with delusions were divided in two clinical subgroups. The first one was presented with persecutory delusions (n=12, 20.2-55.4 years, mean age 36.0±11.6, 2 females) and the second with delusions of influence (n=14, 21.2-47.6 years, mean age 36.7±8.4, 2 females). The control group consist of 26 matched healthy controls (20.2-53.6 years, mean age 36.3±9.9, 4 females). All participants underwent structural MRI at 3T scanner. MRI images were processed via FreeSurfer 6.0 to quantify cortical thickness (CTh) and volumes for subcortical and brainstem (midbrain, pons, superior cerebellar peduncle and medulla) structures.ResultsCompared to healthy controls, patients with delusions of influence showed decreased gray matter thickness in 17 cortical regions (Figure, A), and decreased volumes of thalamus bilaterally (left: F(1,35)=9.0, p=0.005; Cohen’s d=−0.8; right: F(1,35)=8.0, p=0.008; Cohen’s d=−0.8), left hippocampus (F(1,35)=8.8, p=0.005; Cohen’s d=−0.8), midbrain (F(1,35)=11.0, p=0.002; Cohen’s d=−0.9) and pons (F(1,35)=8.2, p=0.007; Cohen’s d=−0.8). Conversely, patients with delusions of persecution showed decreased thickness only in 4 cortical regions of the right hemisphere (figure, B) and no alterations in subcortical volumes compared to healthy controls.No differences in cortical or subcortical measures between two clinical subgroups survived corrections for multiple comparisons. No correlations between structural alterations and total PANSS or BABS scores were found in either clinical groups.Figure. Clusters of decreased cortical thickness according to atlas of Desikan et al. (2006) in delusions of influence (A) and delusions of persecution (B) subgroups compared to healthy controls.Image:Conclusions The findings suggest that patients with delusions of influence have more pronounced cortical, subcortical and brainstem structural deficit as compared to patients with delusions of persecution. However, the limited sample size and the lack of correlations with clinical scores do not allow to conclude definitely whether the revealed structural abnormalities underlies delusions of influence, which should be elucidated via further research.This study was supported by RFBR grant 21-515-12007Disclosure of InterestNone Declared

Electroencephalography (EEG) Evidence for the Psychological Processes of Humor Generation: A Comparison Perspective on Humor and Creativity.

(1) Background: Humor stands out as the most dynamic and innovative aspect of human intelligence. Drawing on the cognitive parallels between humor and creativity, this study explored the EEG alpha frequency band activity patterns during humor generation by comparing the process of generating humorous and creative ideas. (2) Methods: Thirty-six participants were randomly assigned to either the humor generation group or the creative generation group, and the dependent variable was the neural oscillation in both low-frequency and high-frequency alpha during the early, middle, and late stages of both humor and creative generation. (3) Results: In the early stages, both humor and creative generation exhibited significantly higher power in low-frequency alpha and high-frequency alpha in the temporal region compared to the middle and late stages. In the middle and late stages, the low-frequency alpha oscillation in the frontal region for humor generation was significantly higher than that for creative generation. (4) Conclusions: Humor and creative generation share similar neural activation patterns in the early stages, involving the activation and retrieval of long-term memory information based on contextual cues. The differences between the two primarily manifest in the middle and late stages, where the selection of humorous ideas requires inhibiting not only irrelevant or ordinary ideas, akin to creative generation but also novel yet non-humorous ideas. This study sheds light on the neurocognitive mechanisms of humor generation and provides insights into the cognitive parallels and distinctions between humor generation and creative generation.

За някои разстройства на детската реч: ранен детски аутизъм, вербална диспраксия на развитието, фетален алкохолен синдром

The main function of speech is the communicative one, i.e. the transmission of comprehensive information from the speaker to his interlocutor and vice versa. The communicative chain model has long been known in linguistics and its simplified form includes addressee (encoding)→ message/noise→ (decoding)→ addressee. It can be seen that interference could occur on both sides of the communication chain. In this paper, we will briefly describe some problems in communication due to pervasive developmental disorders such as early childhood autism as well as developmental verbal dyspraxia, and fetal alcohol syndrome. A relationship between neural patterns and the disorders under consideration will be outlined in general terms.

Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. We hypothesize that language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language. To test this hypothesis, we densely record the neural activity patterns in the inferior frontal gyrus (IFG) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation for each word (i.e., a brain embedding) in each patient. Using stringent zero-shot mapping we demonstrate that brain embeddings in the IFG and the DLM contextual embedding space have common geometric patterns. The common geometric patterns allow us to predict the brain embedding in IFG of a given left-out word based solely on its geometrical relationship to other non-overlapping words in the podcast. Furthermore, we show that contextual embeddings capture the geometry of IFG embeddings better than static word embeddings. The continuous brain embedding space exposes a vector-based neural code for natural language processing in the human brain.

Effects of high carbon dioxide concentration on emotional processing: Based on multimodal evidence

The effects of CO2 on emotional processing are still incompletely understood, although its effects on cognitive performance are widely recognized. This study hypothesized that elevated CO2 concentration would affect the processing of different valence emotions, which leads to more negative emotions and fewer positive emotions. A within-subject experiment was conducted to compare subjective emotional scores, emotional processing behavioral performance, EEG patterns, and other physiological changes of 30 participants exposed to CO2 concentration at 600 ppm and 5000 ppm. The results suggested that CO2 concentration could affect neural arousal patterns during emotional processing, changes in blood pressure, and salivary cortisol secretion in healthy participants. Emotional experience task performance of participants was also affected by CO2, which was not statistically significant. At the high CO2 concentration, the systolic blood pressure (SBP) level was significantly lower (p = 0.005), while the salivary cortisol level was significantly higher (p = 0.037). Power spectral density analysis showed that there was a significant interaction between CO2 concentration and emotional valence for theta waves in frontal regions (p = 0.029), beta waves in temporal regions (p = 0.017), and gamma waves in frontal-parietal occipital regions (p = 0.013). The present study found that CO2 has a negative effect on emotional processing, such as reducing the speed and accuracy of emotional processing. To some extent, there are also some positive effects, like reducing emotionally-induced increases in blood pressure. This finding warrants further research.

Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.

Replication study on the role of dopamine-dependent prefrontal reactivations in human extinction memory retrieval

Even after successful extinction, conditioned fear can return. Strengthening the consolidation of the fear-inhibitory safety memory formed during extinction is one way to counteract return of fear. In a previous study, we found that post-extinction L-DOPA administration improved extinction memory retrieval 24 h later. Furthermore, spontaneous post-extinction reactivations of a neural activation pattern evoked in the ventromedial prefrontal cortex (vmPFC) during extinction predicted extinction memory retrieval, L-DOPA increased the number of these reactivations, and this mediated the effect of L-DOPA on extinction memory retrieval. Here, we conducted a preregistered replication study of this work in healthy male participants. We confirm that spontaneous post-extinction vmPFC reactivations predict extinction memory retrieval. This predictive effect, however, was only observed 90 min after extinction, and was not statistically significant at 45 min as in the discovery study. In contrast to our previous study, we find no evidence that L-DOPA administration significantly enhances retrieval and that this is mediated by enhancement of the number of vmPFC reactivations. However, additional non-preregistered analyses reveal a beneficial effect of L-DOPA on extinction retrieval when controlling for the trait-like stable baseline levels of salivary alpha-amylase enzymatic activity. Further, trait salivary alpha-amylase negatively predicts retrieval, and this effect is reduced by L-DOPA treatment. Importantly, the latter findings result from non-preregistered analyses and thus further investigation is needed.

Neural Network-based Pattern Recognition in the Framework of Edge Computing

Neural network (NN) model has been widely used in pattern recognition (PR), speech recognition, image processing and other fields, but its application in edge computing (EC) environment faces performance and energy consumption problems. This article first introduced the basic structure and training process of NN, including backpropagation algorithms. Then, this article presented a NN modeling approach based on EC, including NN model compression, distributed NN model and knowledge distillation approach. Finally, this article implemented a PR model for the MNIST (Mixed National Institute of Standards and Technology database) dataset and analyzed the experimental results. The experimental outcomes indicated that the presented approach can significantly enhance the performance of the NN model in the EC environment, while ensuring a high recognition accuracy. The NN modeling approach based on EC can reduce the amount of computation and storage of the NN, thus improving the operating efficiency of the NN in the EC environment by 6%-12%. The NN modeling approach based on EC can optimize the performance and efficiency of the NN model in the EC environment, and provide new ideas and approaches for the application of NN in the EC environment.

Neural mechanisms distinguishing two types of cooperative problem-solving approaches: An fNIRS hyperscanning study

Collaborative cooperation (CC) and division of labor cooperation (DLC) are two prevalent forms of cooperative problem-solving approaches in daily life. Despite extensive research on the neural mechanisms underlying cooperative problem-solving approaches, a notable gap exists between the neural processes that support CC and DLC. The present study utilized a functional near-infrared spectroscopy (fNIRS) hyperscanning technique along with a classic cooperative tangram puzzle task to investigate the neural mechanisms engaged by both friends and stranger dyads during CC versus DLC. The key findings of this study were as follows: (1) Dyads exhibited superior behavioral performance in the DLC task than in the CC task. The CC task bolstered intra-brain functional connectivity and inter-brain synchrony (IBS) in regions linked to the mirror neuron system (MNS), spatial perception (SP) and cognitive control. (2) Friend dyads showed stronger IBS in brain regions associated with the MNS than stranger dyads. (3) Perspective-taking predicted not only dyads’ behavioral performance in the CC task but also their IBS in brain regions associated with SP during the DLC task. Taken together, these findings elucidate the divergent behavioral performance and neural connection patterns between the two cooperative problem-solving approaches. This study provides novel insights into the various neurocognitive processes underlying flexible coordination strategies in real-world cooperative contexts.

Reward-Based Decision-Making in Depression or At-Risk Population

This paper discussed the recent studies on the behavioral and neural patterns of individuals with depression and those with familial risk in reward-based decision-making. Current theories for depression and reward-based decision-making were presented so that they can pave the way for investigation of the interaction between the two under scrutiny. The paper argues that depression influences individuals’ aspects of reward-based decision-making, including thinking strategies, flexibility, reward perceptions, and motivation. At-risk populations inherited the same patterns. According to the empirical studies from recent years, results mainly support the argument. Both clinical and at-risk groups have conservative thinking strategies, inflexibility in demanding situations, and lower perceived reward salience. Furthermore, at-risk populations can obtain the same reward amount even though their neural signals indicate a diminishing reward salience. Future studies, such as longitudinal studies to scrutinize the effect of depression on clinical and at-risk populations, should seek insight into the long-term impact. Moreover, in the future, more studies should investigate the critical period for at-risk populations adopting inflexible thinking patterns so that clinicians can prevent depression in time.

A systems identification approach using Bayes factors to deconstruct the brain bases of emotion regulation.

Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent functional magnetic resonance imaging (fMRI) studies (n = 182 and n = 176): (1) an anterior prefrontal system selectively involved in cognitive reappraisal; (2) a fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) a largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions downregulated by reappraisal. These systems covaried with individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, implicating cannabinoid and serotonin systems in reappraisal. These findings challenge 'limbic'-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation.

Analysis of the Characteristics and Intricacies of Arrangement of Neural Elements in the Costoclavicular Block Using Ultrasound: A Retrospective Qualitative Study.

Ultrasound (US)-guided costoclavicular block (CCB) is a promising new approach to brachial plexus (BP) block which is increasingly being utilized. Conventionally, the costoclavicular space (CCS) has been described to contain three cords. However, there may be variations in the neural pattern of the BP which is important to know to prevent inadvertent injury. We intend to describe the variations in neural patterns from retrospective scans of patients receiving costoclavicular BP block. The stored US images of patients who had received BP block using the CCB for surgery at the level of the elbow or below in the last year (from March 2021 to March 2022) were analyzed by two investigators independently. The clinical data were retrieved from the records of the same patients for the study outcomes. We collated the variations of the neural pattern, the number of neural structures seen, and the echogenicity of the structures in the costoclavicular BP space. In the CCS, the median number of neural structures was 4.5 (minimum of 3 to maximum of 8). With the BP lateral to the axillary artery and sandwiched between the subclavius-pectoralis minor superiorly and the serratus anteriorly inferiorly, numerous variations in the neural structures were noted. The most common arrangement was caterpillar-like (28.6%) and pecker-like (20.3%). The neural structures were found to be hypoechoic in the majority (66%). The CCS hosts several mostly hypoechoic neural structures which may be the variations of the cords or the extension of BP divisions. These new findings have been unreported in the recent past.

Empathy from dissimilarity: Multivariate pattern analysis of neural activity during observation of somatosensory experience

Abstract Empathy seems to rely on our ability to faithfully simulate multiple aspects of others’ inferred experiences, often using brain structures we would use during a similar experience. Much neuroimaging work in this vein has related empathic tendencies to univariate correlates of simulation strength or salience. However, novel evidence suggests that empathy may rely on the multivariate distinctiveness of these simulations. Someone whose representations of painful and non-painful stimulation are more distinct from each other may more accurately simulate that experience upon seeing somebody else experience it. We sought to predict empathic tendencies from the dissimilarity between neural activity patterns evoked by observing other people experience pain and touch and compared those findings to traditional univariate analyses. In support of a simulationist perspective, diverse observed somatosensory experiences were best classified by activation patterns in contralateral somatosensory and insular cortices, the same areas that would be active were the subject experiencing the stimuli themselves. In support of our specific hypothesis, the degree of dissimilarity between patterns for pain and touch in distinct areas was each associated with different aspects of trait empathy. Furthermore, the pattern dissimilarity analysis proved more informative regarding individual differences than analogous univariate analyses. These results suggest that multiple facets of empathy are associated with an ability to robustly distinguish between the simulated states of others at corresponding levels of the processing hierarchy, observable via the distinguishability of neural patterns arising with those states. Activation pattern dissimilarity may be a useful tool for parsing the neuroimaging correlates of complex cognitive functions like empathy.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM).MethodsSGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years).ResultsPatients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition.ConclusionsThese results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Machine learning classification approaches to optimize ROP and TOB using drilling and geomechanical parameters in a carbonate reservoir

Drilling optimization has been broadly developed in terms of influential parameters. The assessment time and the effects of both geomechanical and drilling parameters were vital challenges of investigations. Drilling factors are applied force or rotation of drilling agents such as weight on bit (WOB), and geomechanical features represent mechanical indexes of rocks including unconfined compressive strength (UCS). Optimization efforts have been demonstrated on complex prediction methods whereas the simplicity of classification can offer some optimal ranges utilizing machine learning classifications in an accelerated process. In this study, a novel procedure using the supervised and semi-supervised learning approaches was conducted to classify and optimize the rate of penetration (ROP) and torque on bit (TOB). Firstly, in the case well, user-defined classes were assigned based on geomechanical units (GMU) and the ranges of high ROP and low TOB, thus classes divided drilling factors as GMUs of the case. Secondly, the feature selection was carried out by neural pattern recognition with three multi-objective optimization methods for classification. The inputs of classifications were WOB, hook load, pump pressure, flow rate, UCS, and internal friction angle. Classification approaches were decision trees, support vector machine (SVM), and ensemble learning. Finally, the bagged trees permutation and Laplacian SVM (LapSVM) algorithm separately revealed the significance of parameters and predicted the optimal ROP and TOB regions. Findings showed (1) in supervised classification of the case well, the cubic SVM and bagged trees had the highest area under the curve (AUC) and accuracy, on average 0.97 and 0.96, respectively. (2) The average accuracy of the supervised classifications in a test well was 91% except for the fine SVM, which makes them reliable for the fields with the least information. (3) The permutation outcomes for significant features, flow rate and UCS, exposed influential parameters for ROP and TOB optimization. (4) The semi-supervised method, LapSVM, not only acquired both ROP and TOB labels with an accuracy of 88% but also presented their optimal ranges in 95% of the assessed zones. (5) LapSVM deals with a limited training section perfectly opposed to the supervised version, which is vital for drilling investigation. (6) Implementing machine learning classification approaches with rock properties is a key factor in achieving effective drilling parameters in less time. More importantly, the recommended drilling factors concerning geomechanical properties can ameliorate both drilling performance and perception of upcoming collapse.

Characterization of Ultrasonic Vocalization-Modulated Neurons in Rat Motor Cortex Based on Their Activity Modulation and Axonal Projection to the Periaqueductal Gray.

Vocalization, a means of social communication, is prevalent among many species, including humans. Both rats and mice use ultrasonic vocalizations (USVs) in various social contexts and affective states. The motor cortex is hypothesized to be involved in precisely controlling USVs through connections with critical regions of the brain for vocalization, such as the periaqueductal gray matter (PAG). However, it is unclear how neurons in the motor cortex are modulated during USVs. Moreover, the relationship between USV modulation of neurons and anatomical connections from the motor cortex to PAG is also not clearly understood. In this study, we first characterized the activity patterns of neurons in the primary and secondary motor cortices during emission of USVs in rats using large-scale electrophysiological recordings. We also examined the axonal projection of the motor cortex to PAG using retrograde labeling and identified two clusters of PAG-projecting neurons in the anterior and posterior parts of the motor cortex. The neural activity patterns around the emission of USVs differed between the anterior and posterior regions, which were divided based on the distribution of PAG-projecting neurons in the motor cortex. Furthermore, using optogenetic tagging, we recorded the USV modulation of PAG-projecting neurons in the posterior part of the motor cortex and found that they showed predominantly sustained excitatory responses during USVs. These results contribute to our understanding of the involvement of the motor cortex in the generation of USV at the neuronal and circuit levels.

Altered Dynamics of Brain Spontaneous Activity and Functional Networks Associated With Cognitive Impairment in Patients With Type 2 Diabetes.

Cognitive impairment is increasingly recognized as an important comorbidity and complication of type 2 diabetes (T2D), affecting patients' quality of life and diabetes management. Dynamic brain activity indicators can reflect changes in key neural activity patterns of cognition and behavior. To investigate dynamic functional connectivity (DFC) changes and spontaneous brain activity based on resting-state functional magnetic resonance imaging (rs-fMRI) in patients with T2D, exploring their correlations with clinical features. Retrospective. Forty-five healthy controls (HCs) (22 males and 23 females) and 102 patients with T2D (57 males and 45 females). 3.0 T/T1-weighted imaging and rs-fMRI with gradient-echo planar imaging sequence. Functional networks were created using independent component analysis. DFC states were determined using sliding window approach and k-means clustering. Spontaneous brain activity was assessed using dynamic regional homogeneity (dReHo) variability. One-way analysis of variance and post hoc analysis were used to compare the essential information including demographics, clinical data, and features of DFC and dReHo among groups. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve. P-values <0.05 were taken to indicate statistical significance. T2D group had significantly decreased mean dwell time and fractional windows in state 4 compared to HC. T2D with mild cognitive impairment showed significantly increased dReHo variability in left superior occipital gyrus compared to T2D with normal cognition. Mean dwell time and number of fractional windows of state 4 both showed significant positive correlations with the Montreal cognitive assessment scores (r = 0.309; r = 0.308, respectively) and the coefficient of variation of dReHo was significantly positively correlated with high-density lipoprotein cholesterol (r = 0.266). The integrated index had an area under the curve of 0.693 (95% confidence interval = 0.592-0.794). Differences in DFC and dynamic characteristic of spontaneous brain activity associated with T2D-related functional impairment may serve as indicators for predicting symptom progression and assessing cognitive dysfunction. 2 TECHNICAL EFFICACY: Stage 2.

Identification of group differences in predictive anticipatory biasing of pain during uncertainty: preparing for the worst but hoping for the best.

Pain anticipation during conditions of uncertainty can unveil intrinsic biases, and understanding these biases can guide pain treatment interventions. This study used machine learning and functional magnetic resonance imaging to predict anticipatory responses in a pain anticipation experiment. One hundred forty-seven participants that included healthy controls (n = 57) and individuals with current and/or past mental health diagnosis (n = 90) received cues indicating upcoming pain stimuli: 2 cues predicted high and low temperatures, while a third cue introduced uncertainty. Accurate differentiation of neural patterns associated with specific anticipatory conditions was observed, involving activation in the anterior short gyrus of the insula and the nucleus accumbens. Three distinct response profiles emerged: subjects with a negative bias towards high pain anticipation, those with a positive bias towards low pain anticipation, and individuals whose predictions during uncertainty were unbiased. These profiles remained stable over one year, were consistent across diagnosed psychopathologies, and correlated with cognitive coping styles and underlying insula anatomy. The findings suggest that individualized and stable pain anticipation occurs in uncertain conditions.

Improved emotion differentiation under reduced acoustic variability of speech in autism

BackgroundSocio-emotional impairments are among the diagnostic criteria for autism spectrum disorder (ASD), but the actual knowledge has substantiated both altered and intact emotional prosodies recognition. Here, a Bayesian framework of perception is considered suggesting that the oversampling of sensory evidence would impair perception within highly variable environments. However, reliable hierarchical structures for spectral and temporal cues would foster emotion discrimination by autistics.MethodsEvent-related spectral perturbations (ERSP) extracted from electroencephalographic (EEG) data indexed the perception of anger, disgust, fear, happiness, neutral, and sadness prosodies while listening to speech uttered by (a) human or (b) synthesized voices characterized by reduced volatility and variability of acoustic environments. The assessment of mechanisms for perception was extended to the visual domain by analyzing the behavioral accuracy within a non-social task in which dynamics of precision weighting between bottom-up evidence and top-down inferences were emphasized. Eighty children (mean 9.7 years old; standard deviation 1.8) volunteered including 40 autistics. The symptomatology was assessed at the time of the study via the Autism Diagnostic Observation Schedule, Second Edition, and parents’ responses on the Autism Spectrum Rating Scales. A mixed within-between analysis of variance was conducted to assess the effects of group (autism versus typical development), voice, emotions, and interaction between factors. A Bayesian analysis was implemented to quantify the evidence in favor of the null hypothesis in case of non-significance. Post hoc comparisons were corrected for multiple testing.ResultsAutistic children presented impaired emotion differentiation while listening to speech uttered by human voices, which was improved when the acoustic volatility and variability of voices were reduced. Divergent neural patterns were observed from neurotypicals to autistics, emphasizing different mechanisms for perception. Accordingly, behavioral measurements on the visual task were consistent with the over-precision ascribed to the environmental variability (sensory processing) that weakened performance. Unlike autistic children, neurotypicals could differentiate emotions induced by all voices.ConclusionsThis study outlines behavioral and neurophysiological mechanisms that underpin responses to sensory variability. Neurobiological insights into the processing of emotional prosodies emphasized the potential of acoustically modified emotional prosodies to improve emotion differentiation by autistics.Trial registrationBioMed Central ISRCTN Registry, ISRCTN18117434. Registered on September 20, 2020.

The dorsomedial prefrontal cortex prioritizes social learning during rest

Sociality is a defining feature of the human experience: We rely on others to ensure survival and cooperate in complex social networks to thrive. Are there brain mechanisms that help ensure we quickly learn about our social world to optimally navigate it? We tested whether portions of the brain's default network engage "by default" to quickly prioritize social learning during the memory consolidation process. To test this possibility, participants underwent functional MRI (fMRI) while viewing scenes from the documentary film, Samsara. This film shows footage of real people and places from around the world. We normed the footage to select scenes that differed along the dimension of sociality, while matched on valence, arousal, interestingness, and familiarity. During fMRI, participants watched the "social" and "nonsocial" scenes, completed a rest scan, and a surprise recognition memory test. Participants showed superior social (vs. nonsocial) memory performance, and the social memory advantage was associated with neural pattern reinstatement during rest in the dorsomedial prefrontal cortex (DMPFC), a key node of the default network. Moreover, it was during early rest that DMPFC social pattern reinstatement was greatest and predicted subsequent social memory performance most strongly, consistent with the "prioritization" account. Results simultaneously update 1) theories of memory consolidation, which have not addressed how social information may be prioritized in the learning process, and 2) understanding of default network function, which remains to be fully characterized. More broadly, the results underscore the inherent human drive to understand our vastly social world.