Impaired detection and differentiation of briefly presented facial emotions in adults with high-functioning autism and asperger syndrome

Facial mimicry and metacognitive judgments in emotion recognition are distinctly modulated by social anxiety and autistic traits

Facial mimicry as well as the accurate assessment of one's performance when judging others' emotional expressions have been suggested to inform successful emotion recognition. Differences in the integration of these two information sources might explain alterations in the perception of others' emotions in individuals with Social Anxiety Disorder and individuals on the autism spectrum. Using a non-clinical sample (N = 57), we examined the role of social anxiety and autistic traits in the link between facial mimicry, or confidence in one's performance, and emotion recognition. While participants were presented with videos of spontaneous emotional facial expressions, we measured their facial muscle activity, asked them to label the expressions and indicate their confidence in accurately labelling the expressions. Our results showed that confidence in emotion recognition was lower with higher social anxiety traits even though actual recognition was not related to social anxiety traits. Higher autistic traits, in contrast, were associated with worse recognition, and a weakened link between facial mimicry and performance. Consequently, high social anxiety traits might not affect emotion recognition itself, but the top-down evaluation of own abilities in emotion recognition contexts. High autistic traits, in contrast, may be related to lower integration of sensorimotor simulations, which promote emotion recognition.

Attentional bias to fearful faces in infants at high risk for autism spectrum disorder

Individuals with autism spectrum disorder (ASD) and their first-degree relatives show differences from neurotypical individuals in emotional face processing. Prospective studies of infant siblings of children with ASD, a group at high risk for autism (HRA), allow researchers to examine the early emergence of these differences. This study used eye tracking to examine disengagement of attention from emotional faces (fearful, happy, neutral) at 6, 9, and 12 months in low-risk control infants (LRC) and HRA infants who received a subsequent clinical judgment of ASD (HRA+) or non-ASD (HRA-). Infants saw centrally presented faces followed by a peripheral distractor (with face remaining present). For each emotion, latency to shift to the distractor and percentage of trials with no shift were calculated. Results showed increased saccadic latency and a greater percentage of no-shift trials for fearful faces. No between-group differences were present for emotion; however, there was an interaction between age and group for disengagement latency, with HRA+ infants slower to shift at 12 months compared with the other 2 groups. Exploratory correlational analyses looking at shift biases to fearful faces alongside measures of social behavior at 12 and 18 months (from the Communication and Symbolic Behavior Scales) revealed that for HRA+ infants, 9- and 12-month fear biases were significantly related to 12- and 18-month social abilities, respectively. This work suggests that both low- and high-risk infants show biases to threat-relevant faces, and that for HRA+, differences in attention shifting emerge with age, and a stronger fear bias could potentially relate to less social difficulty. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Fast Periodic Visual Stimulation EEG Reveals Reduced Neural Sensitivity to Fearful Faces in Children with Autism

We objectively quantified the neural sensitivity of school-aged boys with and without autism spectrum disorder (ASD) to detect briefly presented fearful expressions by combining fast periodic visual stimulation with frequency-tagging electroencephalography. Images of neutral faces were presented at 6 Hz, periodically interleaved with fearful expressions at 1.2 Hz oddball rate. While both groups equally display the face inversion effect and mainly rely on information from the mouth to detect fearful expressions, boys with ASD generally show reduced neural responses to rapid changes in expression. At an individual level, fear discrimination responses predict clinical status with an 83% accuracy. This implicit and straightforward approach identifies subtle deficits that remain concealed in behavioral tasks, thereby opening new perspectives for clinical diagnosis.

Analysis of Asperger Syndrome Using Genetic-Evolutionary Random Support Vector Machine Cluster

Asperger syndrome (AS) is subtype of autism spectrum disorder (ASD). Diagnosis and pathological analysis of AS through resting-state fMRI data is one of the hot topics in brain science. We employed a new model called the genetic-evolutionary random Support Vector Machine cluster (GE-RSVMC) to classify AS and normal people, and search for lesions. The model innovatively integrates the methods of the cluster and genetic evolution to improve the performance of the model. We randomly selected samples and sample features to construct GE-RSVMC, and then used the cluster to classify and extract lesions according to classification results. The model was validated by data of 157 participants (86 AS and 71 health controls) in ABIDE database. The classification accuracy of the model reached to 97.5% and we discovered the brain regions with significant differences, such as the Angular gyrus (ANG.R), Precuneus (PCUN.R), Caudate nucleus (CAU.R), Cuneus (CUN.R) and so on. Our method provides a new perspective for the diagnosis and treatment of AS, and a universal framework for other brain science research as the model has excellent generalization performance.