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Objectives: Resting-state EEG (rsEEG) offers unique advantages for examining brain activity in children with autism spectrum disorder (ASD) due to their challenges in following instructions and heightened sensory hypersensitivity. To investigate functional brain states, it is essential to focus on the temporal dynamics of rsEEG. Methods: We applied two methods with different time resolutions (macrostate or k-means clustering of the continuous rsEEG segment and microstate analysis of shorter temporal EEG epochs) to study the transient states of brain electrical activity in several age groups of children. A total of 158 children with low-functioning ASD and 177 typically developing (TD) children aged 2 to 14 years participated in the study. The groups were matched for age, gender, and IQ. Results: The results indicated that in the control group, micro- and macrostates exhibited increased age-related dynamics during maturation. We identified micro- and macrostates with similar topographies related to salience, sensory processing, and the default mode network (DMN). The coverage of macro- and microstates associated with sensory processing and DMN was significantly higher in children with ASD. Children with ASD displayed a shorter coverage of macro- and microstates with interhemispheric asymmetry compared to the TD group. Conclusions: The obtained results support previous findings regarding atypical resting-state EEG microstate patterns in ASD when compared to TD children. Additionally, EEG microstates and macrostates exhibit age-related changes that differ between ASD and TD individuals. The findings related to EEG micro- and macrostates have promising implications for the diagnosis and rehabilitation of children with low-functioning autism.

AbstractFrom childhood to adulthood, an individual’s ability to estimate and anticipate the timing of events changes continuously. This study investigated the ability of 287 children aged 5–14 years to estimate the duration of prior events and anticipate the timing of future events for determination of the age at which children improve their timing skills. The Luria neuropsychological assessment battery and the Wechsler Intelligence Scale for Children (WISC-IV) were applied to find correlations between timing skills and the development of cognitive functions. The findings demonstrated that retrospective estimation of duration has a zone of proximal development in children between the ages of six to eight; in these children, the accuracy of time assessment significantly improved after receiving the prompt. However, improvement in time estimation was significantly lower in those children who achieved lower results in the attention and memory tests and demonstrated reduced spatial and verbal reasoning skills. The zone of proximal development for the ability to anticipate the timing of future events was demonstrated in children between the ages of nine to eleven years. The improvement of time anticipation was negatively correlated with the number of mistakes made during the dynamic praxis test.

Purpose: An electroencephalography (EEG) examination may cause psychological stress in children with autism that can interfere with the examination results. The objective information on the presence or absence of psycho-emotional stress in patients can help interpret electroencephalograms. This paper aimed to demonstrate the potential of noninvasive objective diagnostics of emotional stress in autistic children undergoing an EEG examination based on analysis of saliva. Design/methodology/approach: This study involved 19 children with autism spectrum disorder (ASD) (ICD-10 F84.0); the mean age was seven years. During EEG examination of the children, behavioral parameters were assessed. The activity of cytochrome P450 reductase (CYPOR) in saliva was measured before and after the EEG procedure using lucigenin-enhanced nicotinamide adenine dinucleotide phosphate-stimulated chemiluminescence assay. Findings: Significant differences in CYPOR activity were found between the children who were distressed during an EEG examination and the children without behavioral disturbances (Mann–Whitney test, p= 0.002). Thus, the EEG examination resulted in an increase in CYPOR activity in saliva cells, which may prove the stressful effect of this procedure on autistic children. Originality/value: The chemiluminescent indices reflecting the activity of microsomal CYPOR in cells presenting in saliva correlate with the absence or presence of psychological stress in children; this phenomenon can be explained by an increased metabolism of the stress hormone, cortisol, by the cytochrome P450 microsomal system. Furthermore, the proposed method is completely safe, noninvasive, rapid (recording time is 20 min), inexpensive and promising for an objective assessment of psycho-emotional stress in autistic children undergoing medical examinations.

Purpose: The purpose of this paper is to assess emotional response to music and its EEG correlates in children with autism spectrum disorders (ASD). Design/methodology/approach: Six musical fragments eliciting emotional states of calmness/serenity, sadness and anxiety/fear were presented to children with ASD (n=21, aged 5–9) and typically developing (TD) peers (n=21), while 19-channel EEG was recorded. Emotion self-reports were assessed using visual analogous scales. Findings: Children with ASD assessed most music fragments similarly to their TD peers, with likelihood of EEG oscillatory patterns closely corresponding to emotion self-reports. Somewhat contrary to the expectations, a major difference was observed for one fragment only, which was identified as sad by TD children and adult neurotypical raters, but found “angry and frightening” by children with ASD, with EEG oscillatory response confirming greater cortical activation, particularly for the right hemisphere. Research limitations/implications: The data suggest that children with ASD may have emotional reactions to music either similar or highly aberrant compared to TD peers, rather than having general difficulties in assessing emotions. The data should be confirmed by further studies, ideally involving high functioning adult autists. Practical implications: The findings may increase the understanding of autists’ difficulties in perceiving prosodic nuances and reading emotional cues. The results can be taken into consideration when developing music-based interventions. Originality/value: The findings show that music may be perceived by children with ASD in a unique way, which may be difficult to predict by neurotypical raters.

Clinical neurology is constantly searching for reliable indices of ischemic brain damage to prevent a possible development of stroke. We suggest that resting state electroencephalogram (rsEEG) with respect to other clinical data may provide important information about the severity of ischemia. We carried out correlation analysis of rsEEG, data of transcranial Doppler ultrasonography of head vessels, and clinical assessment scores collected from healthy volunteers and four groups of patients with mild chronic microvascular ischemia (CMI-1), moderate CMI (CMI-2), severe atrophy of the cerebral hemisphere, ischemic stroke in the left middle cerebral artery stroke, and ischemic stroke in the right middle cerebral artery stroke. Using independent component analysis and k-mean clustering of EEG data, we observed prominent changes in rsEEG reflected in specific distributions of spectral peaks in all groups of patients. We found a significant correlation of EEG spectral distribution and the blood flow velocity in coronal arteries, which was also affected by the severity of ischemia and the localization of stroke. Moreover, EEG spectral distribution was more indicative of early stages of ischemia than the blood flow velocity. Our data support the hypothesis that rsEEG may reflect altered neural activity caused by ischemic brain damage.

We explore the potential of the contrastive variational autoencoder to detect latent disorder-specific patterns in the network, analyzing functional brain networks in autistic individuals as the case. Autism spectrum disorder has long troubled medical practitioners, neurologists, and researchers. It is due to its extremely variable nature, both neurologically and behaviorally. Though machine learning has been in use to automate autism diagnosis, little has been done to delve into its intricacies. Here, we attempt to understand the neural mechanisms of autism spectrum disorder using contrastive variational autoencoder in conjunction with feature engineering. Our proposed methodology results in a physiologically interpretable classifier with a remarkable F1-score (up to 95%) and reveals a weak frontal lobe functional connectivity in the alpha band for children with autism spectrum disorder. Our study suggests an increased focus on efficient frontal lobe EEG sampling. Additionally, it highlights the importance of the proposed pipeline for understanding the underlying neural abnormalities in autism over the traditional machine learning pipeline. Thus, the obtained results have proven a contrastive variational autoencoder to be a promising approach for discovering latent patterns and features in complex networks. • Contrastive variational autoencoder discovers latent patterns in complex networks. • Contrastive machine learning disentangles autism-specific brain connectivity patterns. • Disentangled patterns achieved autism classification F1-score up to 95%. • Contrastive ML and feature engineering result in interpretable classifier. • Weak α -band frontal lobe functional connectivity is specific for children with autism. [ABSTRACT FROM AUTHOR]