Autism Spectrum Disorder In Toddlers Research Articles

Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms

BackgroundSocial affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with “profound” autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known.MethodsBecause ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions.ResultsAt the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences.LimitationsLarger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes.ConclusionsBy embryogenesis, the biological bases of two subtypes of ASD social and brain development—profound autism and mild autism—are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler’s social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.

Comparison between the Clancy Behavior Scale and the Modified Checklist for Autism in Toddlers in Taiwan.

(1) Background: Precise diagnosis and early intervention are crucial for toddlers with autism spectrum disorder (ASD) to achieve a better prognosis. This study investigated the efficacy of the Clancy Behavior Scale (CBS) and Modified Checklist for Autism in Toddlers (M-CHAT) in detecting ASD among toddlers under 30 months of age. (2) Methods: A total of 215 toddlers (117 with ASD and 98 with development delays) aged between 18 and 29 months participated in this study. All the primary caregivers of these toddlers were recruited to complete the CBS and M-CHAT. (3) Results: The findings indicated that the accuracy of the CBS and M-CHAT was promising, and the short forms of these two instruments performed better than their full versions. The CBS:9 critical items presented a sensitivity of 0.75 and a specificity of 0.74, while the M-CHAT:14 brief items showed a sensitivity of 0.72 and a specificity of 0.85. (4) Conclusions: The diagnostic accuracy of high-risk ASD toddlers improved via the combination of CBS and M-CHAT, particularly when the information gathered from these two instruments were consistent. The findings may provide implications for enhancing the early detection of ASD.

Towards an automatic early screening system for autism spectrum disorder in toddlers based on eye-tracking

AbstractAccording to official estimations, autism spectrum disorder (ASD) affects around 1% of European newborns. The high level of dependency of ASD-affected subjects entails an extremely high social and economic cost. However, early intervention can drastically improve children’s development and thus reduce their dependency. One of the main common characteristics of subjects with ASD is difficulties with social interaction, which determines how they react to certain stimuli. This behavior can be automatically detected by analyzing their gaze. This study explores and evaluates the feasibility of automatic screening for ASD in toddlers under 24 months of age based on this specific behavior. We applied a matched pairs experimental design and a set of test videos, using a set of variables extracted from gaze analysis from toddlers using eye-tracking devices. The different videos try to capture social engagement, social information gathering gaze exchanges, and gaze following. We used the data to make a thorough comparison of machine learning algorithms (nine learning schemes), including some that were used in related prior research, and others that are popular in classification problems. The results show that several of the tested algorithms provided notable performance.

Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism spectrum disorder.

Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however. In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified. We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88. Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.

A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and adolescents

Individuals with autism spectrum disorder (ASD) have social interaction and communication challenges due to a disruption in brain development that impacts how they perceive and interact with others. The symptoms of autism often present themselves within the first two years of a persons life but can be diagnosed at any time. The ASD theory suggests that the onset of symptoms occurs in early childhood and persists until young adulthood. Machine learning research has been applied to the study of autism. We use Machine Learning (ML) methods and technologies, including Naive Bayes (NB) classifiers, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) to analyse and predict ASD in toddlers (kids between 12 to 36 months old) and adolescents (children between 12 to 18 years old) using non-clinical ASD datasets. In this paper, we review the literature and conduct an in-depth investigation of supervised machine-learning algorithms to evaluate the effectiveness of four leading classifiers on a dataset used for ASD screening.

Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD

BackgroundSocial and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior temporal cortex, a well-established social and language region, shows reduced activation to social affective speech in ASD toddlers; however, the atypical cortical connectivity that accompanies this deviance remains unknown.MethodsWe collected clinical, eye tracking, and resting-state fMRI data from 86 ASD and non-ASD subjects (mean age 2.3 ± 0.7 years). Functional connectivity of left and right superior temporal regions with other cortical regions and correlations between this connectivity and each child’s social and language abilities were examined.ResultsWhile there was no group difference in functional connectivity, the connectivity between superior temporal cortex and frontal and parietal regions was significantly correlated with language, communication, and social abilities in non-ASD subjects, but these effects were absent in ASD subjects. Instead, ASD subjects, regardless of different social or nonsocial visual preferences, showed atypical correlations between temporal–visual region connectivity and communication ability (r(49) = 0.55, p < 0.001) and between temporal–precuneus connectivity and expressive language ability (r(49) = 0.58, p < 0.001).LimitationsThe distinct connectivity–behavior correlation patterns may be related to different developmental stages in ASD and non-ASD subjects. The use of a prior 2-year-old template for spatial normalization may not be optimal for a few subjects beyond this age range.ConclusionsSuperior temporal cortex is known to have reduced activation to social affective speech in ASD at early ages, and here we find in ASD toddlers that it also has atypical connectivity with visual and precuneus cortices that is correlated with communication and language ability, a pattern not seen in non-ASD toddlers. This atypicality may be an early-age signature of ASD that also explains why the disorder has deviant early language and social development. Given that these atypical connectivity patterns are also present in older individuals with ASD, we conclude these atypical connectivity patterns persist across age and may explain why successful interventions targeting language and social skills at all ages in ASD are so difficult to achieve.

The teleNIDA: Early Screening of Autism Spectrum Disorder Through a Novel Telehealth Approach

The COVID-19 pandemic has fast-tracked interest in telehealth methods to guarantee the continuity of care of children with Autism Spectrum Disorder (ASD). Store-and-forward telehealth approaches offer the opportunity to facilitate timely screening of ASD, allowing parents to record videos of their child’s behaviors, subsequently shared with clinicians that provide an assessment remotely. This study aimed to examine the psychometric properties of a new telehealth screening tool, the teleNIDA, administered in home settings for remote observation of early signs of ASD in toddlers aged 18–30 months. Results showed good psychometric properties of the teleNIDA, as compared to the gold standard in-person assessment, and the predictive validity on the diagnosis of ASD at 36 months was demonstrated. This study supports the teleNIDA as a promising level 2 screening tool for ASD able to speed up diagnostic and intervention processes.

Efficient Deep Learning and Machine Learning Models for Early-stage Identification of Autism Spectrum Disorder in Toddlers: Evidence from Saudi Arabia

Autism Spectrum Disorder (ASD) is a type of developmental disorder that can have notable effects on a person’s cognitive abilities, language skills, ability to recognize objects, social interactions, and communication skills. The primary etiology of this condition is attributed to genetics, and prompt detection and intervention may mitigate the potential for the individual to face exorbitant healthcare expenses and protracted diagnostic procedures. A machine learning (ML) and deep learning architecture was developed with the capability to effectively analyze datasets of autistic toddlers, accurately classifying and identifying ASD traits. To explore the feasibility of predicting and analyzing ASD characteristics across various age cohorts, we employed multiple supervised ML models, namely support vector machine (SVM), k-nearest neighbors algorithm, and decision tree, and deep learning models, such as long short-term memory (LSTM). In this study, we analyzed the ASD screening dataset of toddlers from Saudi Arabia. The ASD screening datasets of toddlers from Kaggle were used to test these models. The first dataset includes 1054 instances and 19 toddler-related features, while the remaining datasets consist of 16 features, 507 instances, 165 normal, and 141 ASD cases. We report baseline results of behavior classification using ML and DL approaches. The SVM approach achieved 100% accuracy, whereas the LSTM approach attained 100% accuracy in terms of the accuracy metric. The developed system demonstrates the efficacy of the ASD system in detecting ASD toddlers in Saudi Arabia. Furthermore, the ASD system has the potential to assist parents in examining their children at an early stage.

High-Risk Siblings without Autism: Insights from a Clinical and Eye-Tracking Study.

Joint attention (JA)-the human ability to coordinate our attention with that of other people-is impaired in the early stage of Autism Spectrum Disorder (ASD). However, little is known about the JA skills in the younger siblings of children with ASD who do not develop ASD at 36 months of age [high-risk (HR)-noASD]. In order to advance our understanding of this topic, a prospective multicenter observational study was conducted with three groups of toddlers (age range: 18-33 months): 17 with ASD, 19 with HR-noASD and 16 with typical development (TD). All subjects underwent a comprehensive clinical assessment and an eye-tracking experiment with pre-recorded stimuli in which the visual patterns during two tasks eliciting initiating joint attention (IJA) were measured. Specifically, fixations, transitions and alternating gaze were analyzed. Clinical evaluation revealed that HR-noASD subjects had lower non-verbal cognitive skills than TD children, while similar levels of restricted and repetitive behaviors and better social communication skills were detected in comparison with ASD children. Eye-tracking paradigms indicated that HR-noASD toddlers had visual patterns resembling TD in terms of target-object-to-face gaze alternations, while their looking behaviors were similar to ASD toddlers regarding not-target-object-to-face gaze alternations. This study indicated that high-risk, unaffected siblings displayed a shared profile of IJA-eye-tracking measures with both ASD patients and TD controls, providing new insights into the characterization of social attention in this group of toddlers.

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85–89% and AUC-PR scores of 84–92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

Motor impairments in Chinese toddlers with autism spectrum disorder and its relationship with social communicative skills.

ObjectiveMotor impairments are prevalent in children with autism spectrum disorder (ASD) and persistent across age. Our current study was designed to investigate motor deficits in Chinese toddlers with ASD and to explore the relationships between motor deficits and social communication skills.MethodsFor this cross-sectional study, we recruited a total of 210 Chinese toddlers with ASD aged between 18 and 36 months in the study during December 2017 to December 2020. Griffiths Developmental Scales-Chinese (GDS-C), Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) and Communication and Symbolic Behavior Scales Developmental Profile-Infant-Toddler Checklist (CSBS-DP-ITC) were administered in these toddlers to evaluate their development, social communicative skills, and autism severity. We compared the developmental and social communicational profiles of ASD toddlers in different gross and fine motor subgroups, and explored potential associated factors. The univariate generalized linear model tested the relationship of fine and gross motor skills and social communicative skills.ResultsThe prevalence of gross and fine motor deficits were 59.5 and 82.5%, respectively, which are almost equivalent in boys and girls. The motor impairments tended to be more severe with age in toddlers. After adjusting for age, sex, non-verbal development quotient (DQ) and restricted, repetitive behaviors, severer gross motor impairments were significantly related to higher comparison score of ADOS-2 and higher social composite score of CSBS-DP-ITC, without interactions with other variables. Meanwhile, lower fine motor skills were associated with more deficits of social communication and higher severity of ASD, also depending on non-verbal DQ. In the lower non-verbal DQ subgroup, both fine motor deficits and restricted repetitive behaviors (RRBs) might have effects on autism symptomology.ConclusionMotor impairments are common in Chinese toddlers with ASD. Toddlers with weaker gross and fine motor skills have greater deficits in social communicative skills. Gross motor impairment might be an independent predictor of the severity of autism and social communication skills, while the effect of fine motor deficits might be affected by non-verbal DQ and RRBs of toddlers with ASD. We provide further justification for the inclusion of motor impairments in the early intervention for toddlers with ASD.

The Validity of the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F) Thai Version as the Autism Screening Application: A Pilot Study

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder where symptoms first appear during the first two years of life. Early screening and diagnosis are the keys to identifying toddlers with ASD so that early intervention could be recommended. The Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) is a worldwide ASD screening instrument with adequate validity, which is used to screen ASD in toddlers between the ages of 16 and 30 months. Objective: To compute the sensitivity and specificity of the Thai version of the M-CHAT-R/F on the Autism Screening application (AS app) among children ranging from 16 to 30 months of age. Materials and Methods: The permission to translate M-CHAT-R/F into Thai and place it on an AS app on the computer website, on iOS, and on Android smartphones/tablets was granted. The forward translation of M-CHAT-R/F to Thai and the back translation into English were conducted by the language experts. The content was proofed and edited by two Child and Adolescent Psychiatrists and by one Developmental Pediatrician. The final Thai version of the M-CHAT-R/F was placed in the AS app and was used in the pilot study, which consisted of 15 toddlers with ASD and 15 toddlers with typical development. The screening results from the AS app were compared to the diagnoses from either Child and Adolescent Psychiatrists or Developmental Pediatricians to calculate the sensitivity and specificity. Results: The number of female(s) and male(s) among typical toddlers and toddlers with ASD were 1/14 and 2/13. The mean ages were 16 (SD 0)/26 (SD 4.31) and 23 (SD 9.90)/29.08 (SD 1.71) months old. The Thai M-CHAT-R/F gave the sensitivity and specificity of 100% at the cutoff score of 6 or greater from the full score of 20. Conclusion: The AS app had a high level of validity at the cutoff score of 6 or greater. Keywords: Application; Screening; Autism; Toddlers; Thai

Screening for Autism Spectrum Disorder in Toddlers During the 18- and 24-Month Well-Child Visits.

ObjectiveEarly screening contributes to the early detection of children with autism spectrum disorder (ASD). We conducted a longitudinal ASD screening study in a large community setting. The study was designed to investigate the diagnostic rate of ASD screening and determine the effectiveness of ASD screening model in a community-based sample.MethodsWe enrolled children who attended 18- and 24-month well-child care visits in Shanghai Xuhui District. Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) and Binomial Observation Test (BOT) were selected as screening instruments. Screen-positive children were referred to a tertiary diagnostic center for comprehensive ASD diagnostic evaluation. Screen-negative children received well-child checkups and follow-up every 3–6 months until age three and were referred if they were suspected of having ASD.ResultsA total of 11,190 toddlers were screened, and 36 screen-positive toddlers were diagnosed with ASD. The mean age at diagnosis for these children was 23.1 ± 4.55 months, diagnosed 20 months earlier than ASD children not screened. The diagnostic rate of ASD was 0.32% (95% CI: 0.23–0.45%) in this community-based sample. In addition, 12 screen-negative children were diagnosed with ASD during subsequent well-child visit and follow-up. The average diagnostic rate of ASD rose to 0.43% (95% CI: 0.32–0.57%) when toddlers were followed up to 3 years old. The positive predictive values (PPVs) of M-CHAT-R/F, M-CHAT-R high risk, and BOT for ASD were 0.31, 0.43, and 0.38 respectively.ConclusionOur findings provide reliable data for estimating the rate of ASD detection and identifying the validity of community-based screening model. M-CHAT-R/F combined with BOT can be an effective tool for early detection of ASD. This community-based screening model is worth replicating.

Transitioning to Telemedicine During COVID-19: Impact on Perceptions and Use of Telemedicine Procedures for the Diagnosis of Autism in Toddlers.

The COVID-19 pandemic has increased the use of telemedicine as an avenue to address the need for diagnostic clarification in young children at risk for autism spectrum disorder (ASD). Although preliminary research has supported the use of telemedicine for identifying ASD in toddlers, little is known about the experiences of practitioners attempting direct-to-home tele-assessment. We surveyed diagnostic providers regarding changes in practice behavior in the initial months of the COVID-19 pandemic and their perceptions of ASD tele-assessment. We also examined the use of the TELE-ASD-PEDS, a novel tool for ASD tele-assessment, in response to COVID-19 at seven sites across the country. Results support the clinical acceptability and diagnostic utility of ASD tele-assessment while also highlighting critical avenues of future investigation.

Maternal mediation strategies during interaction with toddlers- a comparison of dyads with autism spectrum disorder (ASD) and dyads with typical development (TD)

IntroductionDuring interactions with toddlers, mothers use various mediation strategies to encourage mutual play. Such mediation skills play an important role in the development of toddlers’ communicative skills. Autism Spectrum Disorder (ASD) introduces challenges to this interaction.ObjectivesTo study the use of maternal strategies during interaction with ASD and TD toddlers at early lexical levels.MethodsNine ASD and fifteen TD dyads participated. Toddlers were matched by lexical levels. The mean age in the ASD was 31.5 months and in TD - 17 months. Each dyad was video-recorded three times, during naturalistic interaction. Mothers’ verbal mediation strategies were divided into five main communicative categories.Results1. Exact repetition of toddler’s utterances was similarly used and increased in both groups across the three visits (f (2,44)=3.77, p&lt; 0.05). 2. Significant differences were found between the two groups regarding strategies associated with control of the interaction eg mothers of toddlers with ASD (MASD) made more frequent attempts to redirect their child’s attention (F (1,22)= 74.56, p&lt;0.01). 3. MASD had higher indices of overall talkativeness (F (1,22)= 5.43, p&lt;0.05); use of nonverbal means (F(1,22)= 9,51, p&lt;0.01); simultaneous use of different means of communication (F (1,22)=19.8, p&lt;0.01).ConclusionsOur results highlight that in some respects, maternal mediation strategies reflect the child’s lexical level. However, our main finding is a distinct interaction style expressed in MASD’s elevated use of verbal and nonverbal mediation strategies. This, in hope of maintaining continuous interaction that could not be otherwise achieved due to their toddlers’ difficultiesDisclosureNo significant relationships.

Replication study of ADOS-2 Toddler Module cut-off scores for autism spectrum disorder classification.

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) has been regarded as the gold standard assessment of autism spectrum disorder (ASD). While clinical validity of ADOS-2 Modules 1-4 have been extensively studied, there has been very limited research examining the clinical validity of ADOS-2 Toddler Module. The goal of this study was to examine alignment of the ADOS-2 Toddler Module classification with clinicians' determination of ASD, with assessing cut-off scores for diagnosing ASD in toddlers. A total of 412 toddlers ages 12-30 months who received ADOS-2 Toddler Module as well as a best estimate clinical (BEC) diagnosis, were included in this study. ADOS-2 Toddler Module cut-off scores were determined using the appropriate algorithms (Algorithm 1 for toddlers aged 12-20 months and those aged 21-30 months with <5 words, Algorithm 2 for toddlers aged 21-30 months with 5 words or more). Receiver operating characteristic (ROC) curves were used to assess cut-off scores that optimized sensitivity and specificity when compared against BEC diagnosis. The selected cut-off scores were examined using frequency tables to compare ADOS-2 classification against BEC diagnosis. For Algorithm 1, classification statistics were optimized at the cut-off score of 12 with an area under the curve (AUC) of 0.92. For Algorithm 2, classification statistics were optimized at the cut-off score of 10 with an AUC of 0.96. The ADOS-2 Toddler Module classification is strongly aligned with BEC diagnosis. The optimal cut-off scores identified in the current study reflect the same results configured by the prior study. LAY SUMMARY: ADOS-2 Toddler Module has been widely used for the ASD assessment, but there have been limited research on its clinical validity. This study is the first replication of the ADOS-2 Toddler Module with a large independent sample. We examined alignment of the ADOS-2 Toddler Module classification with clinicians' determination of ASD, with assessing cut-off scores, and confirmed the clinical validity of ADOS-2 Toddler Module. Cut-off scores of ADOS-2 Toddler Module cited in the manual yielded best clinical utility for diagnosing ASD in toddlers.

Gaze Following and Pupil Dilation as Early Diagnostic Markers of Autism in Toddlers.

Background: Children with autism spectrum disorder (ASD) show certain characteristics in visual attention. These may generate differences with non-autistic children in the integration of relevant social information to set the basis of communication. Reliable and objective measurement of these characteristics in a language learning context could contribute to a more accurate early diagnosis of ASD. Gaze following and pupil dilation are being studied as possible reliable measures of visual attention for the early detection of ASD. The eye-tracking methodology allows objective measurement of these biomarkers. The aim of this study is to determine whether measurements of gaze following and pupillary dilation in a linguistic interaction task are potential objective biomarkers for the early diagnosis of ASD. Method: A group of 20 children between 17 and 24 months of age, made up of 10 neurotypical children (NT) and 10 children with an increased likelihood of developing ASD were paired together according to chronological age. A human face on a monitor pronounced pseudowords associated with pseudo-objects. Gaze following and pupil dilation were registered during the task These measurements were captured using eye-tracking methodology. Results: Significant statistical differences were found in the time of gaze fixation on the human face and on the object, as well as in the number of gazes. Children with an increased possibility of developing ASD showed a slightly higher pupil dilation than NT children. However, this difference was not statistically significant. Nevertheless, their pupil dilation was uniform throughout the different periods of the task while NT participants showed greater dilation on hearing the pseudoword. Conclusions: The fixing and the duration of gaze, objectively measured by a Tobii eye-tracking system, could be considered as potential biomarkers for early detection of ASD. Additionally, pupil dilation measurement could reflect differential activation patterns during word processing in possible ASD toddlers and NT toddlers.

Psychometric evaluation of an Arabic Red Flag Screening Checklist of autism spectrum disorders in infants and toddlers

BackgroundAlthough autism spectrum disorder (ASD) is increasing around the world, the interest in developing and validating screening instruments in the Arab countries is still in the initial stage. The current study examined psychometric properties of a unidimensional 14-item screening tool that was developed in Qatar to identify ASD infants and toddlers between the ages of 15 and 33 months. MethodsRash analysis was carried out on a sample of 22,307 records of children who were screened by this instrument. The analysis was done for two age groups: 15–24 months, and 25–33 months, and across two administration modes: paper and pencil, and electronic modes. ResultsThe findings of the current study showed mixed results: some scale properties were good, whereas others were poor. Almost all 12 items retained for the final form showed acceptable values for the infit and outfit statistics and fulfilled the assumptions of local independence and unidimensionality for the two groups of children and across the two administration modes. However, reliability and separation for persons and items, and targeting of items to children along the autistic trait were not acceptable. Moreover, low sensitivity and specificity asserted that ASD was not accurately identified in children. ConclusionsThe findings of the current study identified weaknesses in three areas: the small number of items used to measure the autistic trait, the small number of response categories within each item, and the low sensitivity and specificity of the instrument. Suggestions were provided to address these weaknesses.

Pragmatic Profiles of Toddlers With Autism Spectrum Disorder at the Onset of Speech.

Using speech to communicate pragmatic functions is challenging among individuals with Autism Spectrum Disorder (ASD). Given the role language plays in developing everyday skills, we traced the unique pragmatic profile of early words, seeking comparison to typically developing (TD) toddlers at similar lexical stages. Twenty-four mother-toddler dyads participated (9 ASD and 15 TD). Dyads were video recorded when toddlers reached a productive lexicon of 40–70 words. These recordings were captured three times during naturalistic interaction and at two consecutive visits with a 2-month interval. Seven thousand three hundred seventy-six productions were analyzed and classified into four communicative intentions (Declaratives, Requests, Objections, and Non-Communicative speech). ASD toddlers were delayed in the emergence of words compared to TD toddlers, with a greater within-group variability (median 28 months, IQR 24.5–35, median 17 months, IQR 17–18, respectively, p < 0.001). In both groups, the most common communicative intention was Declarative. However, the percentage of Declaratives was higher among TD toddlers across visits compared to ASD toddlers. In both groups, most productions were directed toward the communicative partner, but ASD toddlers used Non-Communicative speech more often than TD peers. Non-Communicative speech gradually decreased over time. We conclude that while TD toddlers begin to talk with an already-established knowledge of the main communicative functions of words, ASD toddlers seem to have only a partial understanding and gradually improve communicative use as they expand their lexicon. These findings bear theoretical and practical implications for early intervention in ASD. We suggest that communicative profiles are affected by individual characteristics and by the interaction style.

Improving Early Identification and Access to Diagnosis of Autism Spectrum Disorder in Toddlers in a Culturally Diverse Community with the Rapid Interactive screening Test for Autism in Toddlers

The objective of this study was to test a screening model that employs the Rapid Interactive Screening Test for Autism in Toddlers (RITA-T), in an underserved community to improve ASD detection. We collaborated with a large Early Intervention (EI) program and trained 4 providers reliably on the RITA-T. Toddlers received the Modified Checklist for Autism in Toddlers (MCHAT-R/F), the RITA-T, developmental and autism testing, and a best-estimate clinical diagnosis. Eighty-One toddlers were enrolled: 57 with ASD and 24 with Developmental Delay (DD) non-ASD. Wait-time for diagnosis was on average 6 weeks. The RITA-T correlated highly with autism measures and EI staff integrated this model easily. The RITA-T significantly improved the identification and wait time for ASD in this underserved community.