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Background: The perception of basic emotional sounds, such as crying and laughter is associated with effective interpersonal communication. Difficulties with the perception and analysis of sounds that complicate understanding emotions at an early development age may contribute to communication deficits. Methods: This study focused on auditory nonverbal emotional perception including emotional vocalizations with opposite valences (crying and laughter) and neutral sound (phoneme "P?"). We conducted event-related potential analysis and compared peak alpha frequencies (PAFs) for different conditions in children with autism spectrum disorder (ASD) and typically developing (TD) children aged 4 to 6 years old (N = 25 for each group). Results: Children with ASD had a higher amplitude of P100 and lower amplitude of N200 for all types of sounds and higher P270 in response to neutral phoneme. During the perception of emotional sounds, children with ASD demonstrated a single P270 electroencephalography (EEG) component instead of a P200-P300 complex specific to TD children. However, the most significant differences were associated with a response to emotional valences of stimuli. The EEG differences between crying and laughter were expressed as a lower amplitude of N400 and higher PAF for crying compared to laughter and were found only in TD children. Conclusions: Children with ASD have shown not just abnormal acoustical perception but altered emotional analysis of affective sounds as well. [ABSTRACT FROM AUTHOR]

Potocki–Lupski Syndrome (PTLS) is a rare condition associated with a duplication of 17p11.2 that may underlie a wide range of congenital abnormalities and heterogeneous behavioral phenotypes. Along with developmental delay and intellectual disability, autism-specific traits are often reported to be the most common among patients with PTLS. To contribute to the discussion of the role of autism spectrum disorder (ASD) in the PTLS phenotype, we present a case of a female adolescent with a de novo dup(17) (p11.2p11.2) without ASD features, focusing on in-depth clinical, behavioral, and electrophysiological (EEG) evaluations. Among EEG features, we found the atypical peak–slow wave patterns and a unique saw-like sharp wave of 13 Hz that was not previously described in any other patient. The power spectral density of the resting state EEG was typical in our patient with only the values of non-linear EEG dynamics: Hjorth complexity and fractal dimension were drastically attenuated compared with the patient's neurotypical peers. Here we also summarize results from previously published reports of PTLS that point to the approximately 21% occurrence of ASD in PTLS that might be biased, taking into account methodological limitations. More consistent among PTLS patients were intellectual disability and speech and language disorders. [ABSTRACT FROM AUTHOR]

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts' involvement. As also revealed by our study, experts' opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts' knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community. [ABSTRACT FROM AUTHOR]

This study focused on auditory emotional perception in children with low-functioning autism and investigated the children's response to emotionally charged nonverbal sounds which regularly induced emotional response in typically developing (TD) peers. An EEG was conducted, and emotional reactions were assessed using analog scales and images of presented sounds with additional images during the presentation of emotional stimuli. The results showed that EEG and emotional responses to the fearful sounds were similar in TD children and children with autism spectrum disorders (ASD). Both groups of children showed an increase in peak alpha frequency and power of alpha2-band and a decrease in low-frequency bands. Sounds of crying and laughter induced an atypical EEG response in children with ASD, with no change in alpha-band's power and frequency observed in them; this was contrary to the observation in TD children. The decrease in the fractal dimension detected in children with ASD only for sounds of crying and laughter correlated with the accuracy of assessment of these stimuli. [ABSTRACT FROM AUTHOR]