Lyubov' О. Tkacheva

Background . Due to the high prevalence of autism spectrum disorders (ASD) and the dependence of prognosis on intervention time, the development of factor models for autistic symptoms in preschool childhood is an urgent area of research. Comparing autism symptoms multifactorial models obtained using structural equation modeling for preschoolers with autism of different age groups is valuable because it provides information that cannot be covered by studying a single age group. Objective : To compare the factor patterns for ASD symptoms in children aged 3–4 and 5–7 years to identify autism development trajectories. Material and methods . Since autism symptoms pattern changes as children develop, comparing factor models allows to identify age-related indicators and use more accurate diagnostic criteria adapted to each age group for improving sensitivity and specificity of early autism diagnosis and optimize corrective interventions. Within the years 2023–2024, 7-factor and 8-factor models for autism symptoms, respectively, were obtained in 3–4-year-old (n=294) and 5–6-year-old (n=374) ASD children. The data in both studies were collected by specialists who worked with the patients on a regular basis by filling out the online questionnaires. Results . When comparing two autism factor models, it was found that the identified factor pattern, which includes three groups of symptoms for young children, was dramatically changed in older children. With age, the relationship between specific symptoms and the underlying factors has significantly increased and become more complex. For younger children, the identified 7-factor pattern consists of three unrelated groups of symptoms: communication problems (including factors such as emotional dysregulation, speech understanding, echolalia, alienation), persistence on sameness with sensory disintegration and hyperactivity/disinhibition. For older children, the 8-factor pattern has developed into three other groups of relatively independent but related symptoms: the verbal group (including factors of speech understanding and empathy), praxis disorders (including factors such as hyperactivity, emotional dysregulation, motor disorders) and sensory disintegration (including factors such as persistence on sameness, sensory disintegration and echolalia). Conclusion . Speech and motor development are two lead symptoms that deserve special attention from specialists. By understanding how the pattern of ASD symptoms and factor load change throughout preschool age, researchers can improve existing diagnostic tools to increase their accuracy and effectiveness in detecting autism at different ages and develop targeted intervention strategies.

The age-related manifestation of autism spectrum disorders (ASD) symptoms at an early age is important, since such knowledge allows to choose the best intervention methods. Data were collected on 242 1–2-year-olds (131 with ASD, 84 normal, and 27 with DD). These data were combined with previously obtained data on 3–7-year-olds belonging to the same 3 groups (in total 1.749 children). Using exploratory and confirmatory factor analysis, we obtained a 3-factor model of ASD symptoms for 1–2-year-olds, consisting of 3 factor-scales: communication impairment, sensory disintegration, and hyperactivity. The structure of the model and its equivalence were confirmed for 1–2- and 3–7-year-old children. Three identified symptoms accompany children with ASD throughout preschool age. It has been revealed that hyperactivity is a common symptom inherent in 1–2-year-olds with both ASD and DD. It was found that in 1–2-year-old children, the most pronounced symptoms of ASD are communication disorders and hyperactivity; in 3–5-year-olds — sensory disintegration, and in 6–7-year-olds — hyperactivity increases and sensory impairments decrease compared to 3–5-year-old children. It was also found that hyperactivity is higher in boys regardless of age; 1–2-year-old boys have higher communication impairments than girls, and at the age of 3–7 years, conversely, communication problems are more common in girls. The revealed patterns in ASD symptoms manifestation are due to nervous system development, are subject to age and gender differences, and depend on neurophysiological, behavioral, and environmental factors. Taking these patterns into account will improve intervention methods and create individualized developmental programs for preschoolers with ASD.

The work identifies the factor structure of autism symptoms in older preschoolers to check their age dynamics. An Autism Marker Questionnaire containing 330 potential symptoms of autism spectrum disorder (ASD) was developed to be implemented as an online survey. 501 children were examined (371 with ASD) already classified by experts as a group of ASD, typically developing (TD) or with developmental delay (DD). At the exploratory stage, a relatively simple 8-factor structure was obtained: Speech Understanding (SU), Emotions (Em), Persistence on Sameness (Sam), Hyperactivity/Disinhibition (Hyp), Sensory disintegration (Sen), Echolalia (Ech). Each factor formed a sufficiently reliable scale in terms of internal consistency. Logistic regression demonstrated high accuracy in dividing children into groups with and without ASD. The model for confirmatory analysis included eight primary factors and 3 secondary factors: 1) Communication disorders, COM (SU, Em, Emp, Ech), 2) Sensory disintegration, SD (SD, PS, Ech), 3) Praxis impairment, PI (Hyp, Mot). The multigroup confirmatory analysis confirmed the equivalence of the model for boys and girls, as well as 5- and 6-year-old children. The factors obtained can be considered as key autistic symptoms for this age group and can shed some light on our theoretical understanding of the autistic symptoms age dynamics in preschoolers.

The work is devoted to the empirical analysis of age shifts within the framework of the factor model of autism for preschool children. In the 2020–2022 studies, we obtained a factor model of autism and identified 4 vectors of autism — typical subtypes of ASD in 3–4 years old children. Among them are: 1) difficulties in establishing emotional contact, expressing one's emotions and decoding other people's emotions in the context of social interaction; 2) sensory disintegration; 3) impairments in verbal and nonverbal communication and social skills; 4) hyperactivity, including excessive movements, motor disinhibition and restless behavior. It should be emphasized that the first three vectors correspond to the core symptoms of ASD in accordance with the DSM-5. While the hyperactivity factor goes beyond the main symptoms of ASD. In this paper, a comparative analysis of the severity and stability of symptoms was carried out within the framework of a 4-factor model of autism for 3–4 and 5–6-year-old children with autism. Sensory and communication impairments have been found to decrease with age, however, emotional impairments and hyperactivity remain at a high level. The theoretical foundations of emotional disorders and hyperactivity in children with ASD are analyzed in detail. Brief practical recommendations are presented for effective early intervention in order to improve the potential trajectory of development of preschool children with ASD.

The study focuses on examining of age-related changes in ASD symptoms among children 3-10 years old. Sample consisted of 70 children, of which 21 were children without ASD, but with autistic traits. 881 children from the previous studies were included. The following symptom groups were investigated: 1) “Communication impairments”, 2) “Emotional disturbances”; 3) “Sensory disintegration”; “Disinhibition”; 5) Overall symptoms. Assumptions of this study: 1) there are age-related differences in ASD symptoms among children aged 3-10 years. Results found that overall symptoms statistically significant decreased for ASD children, as well as communication impairments, emotional disturbances, sensory disintegration. Additionally, for children without ASD decreasing is not registered.

The study aims to reveal autism vectors that are inherent in 3-4-year-olds with ASD. Three hundred eighty-three children with ASD took part in the research. An online questionnaire developed earlier by our research group was used. In the first stage, with the repeated use of exploratory factor analysis, the task of identifying the simplest factor structure was solved. As a result, a 7-factor system was obtained, including from 9 to 14 items in each factor (78 items in total): 1) "Persistence on sameness" (Sam); 2) "Emotional dysregulation" (Em); 3) "Alienation" (Al); 4) "Speech understanding" (SU); 5) "Disinhibition/Hyperactivity" (Hyp); 6) "Echolalia" (Ech); 7) "Sensory disintegration" (Sen). Next, a "parcel approach" was applied: binary items included in each factor were randomly distributed into 3 packages, from 3 to 5 items in each box. Thus, instead of 78 binary things, 21 quantitative variables were analyzed. At the second stage, a multi-group confirmatory factor analysis was applied to verify the factor validity and structural and measurement equivalence of the obtained 7-factor model to the parts of the sample that differ by gender (m, f) and age (3 and 4 years). The factor validity of the 7-factor model was confirmed: the factors Em, Al, Su, and Ech correlated with each other (correlations from 0.33 to 0.65), forming a group of communication disorders, the second group consisted of factors Sam and Sen (r = 0.66), the factor Hyp did not correlate with other elements. The equivalence of this 7-factor model was confirmed with respect to samples differing in gender (m, f) and age (3 and 4 years). According to all 7 calculated factors-scales, the example of children with ASD statistically significantly differed from other children with the magnitude of the Coen's d effect from 0.688 to 2.088. However, it can be supposed that the factor dimension of autism symptoms can be even more significant, because the revealed structure is based on 436 initial hypothetical symptoms of autism.

The research aims to reveal neural indicators of recognition for iconic words and the possible cross-modal multisensory integration behind this process. The goals of this research are twofold: (1) to register event-related potentials (ERP) in the brain in the process of visual and auditory recognition of Russian imitative words on different de-iconization stages; and (2) to establish whether differences in the brain activity arise while processing visual and auditory stimuli of different nature. Sound imitative (onomatopoeic, mimetic, and ideophonic) words are words with iconic correlation between form and meaning (iconicity being a relationship of resemblance). Russian adult participants (n = 110) were presented with 15 stimuli both visually and auditorily. The stimuli material was equally distributed into three groups according to the criterion of (historical) iconicity loss: five explicit sound imitative (SI) words, five implicit SI words and five non-SI words. It was established that there was no statistically significant difference between visually presented explicit or implicit SI words and non-SI words respectively. However, statistically significant differences were registered for auditorily presented explicit SI words in contrast to implicit SI words in the N400 ERP component, as well as implicit SI words in contrast to non-SI words in the P300 ERP component. We thoroughly analyzed the integrative brain activity in response to explicit IS words and compared it to that in response to implicit SI and non-SI words presented auditorily. The data yielded by this analysis showed the N400 ERP component was more prominent during the recognition process of the explicit SI words received from the central channels (specifically Cz). We assume that these results indicate a specific brain response associated with directed attention in the process of performing cognitive decision making tasks regarding explicit and implicit SI words presented auditorily. This may reflect a higher level of cognitive complexity in identifying this type of stimuli considering the experimental task challenges that may involve cross-modal integration process.

Iconic words constitute an integral part of the lexicon of a language, exhibiting form-meaning resemblance. Over the course of time, semantic and phonetic transformations “weaken” the degree of iconicity of a word. This iconicity loss is known as the process of de-iconization, which is divided into four stages, and, at each consecutive stage, the degree of a word’s iconicity is reduced. The current experimental study is the first to compare and contrast how English (N = 50) and Russian (N = 106) subjects recognize visually presented native iconic words (N = 32). Our aim is two-fold: first, to identify native speakers’ ability to perceive the fine-grained division of iconicity; and second, to control for the influence of participants’ native languages. This enables us to provide a more exhaustive analysis of the role of iconicity in word recognition and to combine empirical results with a theoretical perspective. The findings showed that the speakers of these languages are not equally sensitive to iconicity. As opposed to the English-speaking participants, who showed almost similar performance on each group of iconic words, the Russian participants tended to respond slower and less accurately to the words that were higher in iconicity. We discuss the major factors that may affect iconic word recognition in each language.

Background: Autistic spectrum disorder (ASD) is a significant socio-biological problem due to its wide prevalence and negative outcomes. In the current study, we aimed to develop an autism scale for early and accurate differentiation of 3- to 4-year-olds at risk for ASD since there is no systematic monitoring of young children in Russia yet. Methods: The total sample (N = 324) included 116 children with ASD, 131 children without ASD (healthy controls), and 77 children with developmental delay (DD). An online survey of specialists working with children was conducted based on a specially designed autism questionnaire consisting of 85 multiple-choice tasks distributed across 12 domains. Initially, each child was assessed by 434 items using a dichotomous scale (0 = no, 1 = yes). Factor and discriminant analyses were performed to identify a compact set of subscales that most accurately and with sufficient reliability predicted whether a child belongs to the ASD group. Results: As a result, four subscales were obtained: Sensorics, Emotions, Hyperactivity, and Communication. The high discriminability of the subscales in distinguishing the ASD group from the non-ASD group was revealed (accuracy 85.5–87.0%). Overall, the obtained subscales meet psychometric requirements and allow for creating an online screening system for wide application.