Biomarkers of preschool children with autism spectrum disorder: quantitative analysis of whole-brain tissue component volumes, intelligence scores, ADOS-CSS, and ages of first-word production and walking onset.

BACKGROUND: Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening. METHODS: Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model. RESULTS: WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001). CONCLUSIONS: This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children.

Cognitive-adaptive Functioning Gap and Mediating Factors that Impact Adaptive Functioning in Chinese Preschool-aged Children with Autism Spectrum Disorder.

This study aimed to investigate the gap between adaptive functioning and cognitive functioning, especially verbal and nonverbal intelligence quotient (IQ) in Chinese children with ASD. We systematically explored cognitive functioning, ASD severity, early signs of developmental abnormalities, and socioeconomic factors as mediating factors of adaptive functioning. We enrolled 151 children (age: 2.5?6 years) with ASD and categorized them into one group with IQ ≥ 70 and another with IQ < 70. The two groups were calibrated for age, age at diagnosis, and IQ, and the relationship of adaptive skills with vocabulary acquisition index (VAI) and nonverbal index (NVI) were separately analyzed. Results show that the gap between IQ and adaptive functioning was significant in children with ASD having IQ ≥ 70, with both VAI and NVI showing statistically significant differences (all P < 0.001). VAI correlated positively with scores for overall adaptive skills and specific domains, whereas NVI had no significant correlations with adaptive skill scores. Age of first walking unaided had an independent positive correlation (all P < 0.05) with scores of adaptive skills and specific domains. IQ-adaptive functioning gap is significant in children with ASD having IQ ≥ 70, suggesting that defining "high-functioning autism" merely on the basis of IQ is not appropriate. Verbal IQ and early signs of motor development are specific and possible predictors of adaptive functioning in children with ASD, respectively.

A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name.

Background: Reduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN. Methods: A total of 125 toddlers were recruited, including ASD (n = 61), developmental delay (DD, n = 31), and typical developmental (TD, n = 33). Procedures of RTN were, respectively, performed by the evaluator and caregiver. Behavioral data were collected by eight-definition tripod-mounted cameras and coded by the MMLS. Response score, response time, and response duration time were accurately calculated to evaluate RTN. Results: Total accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all P-values < 0.05). The area under the curve (AUC) was 0.81 for the computer-rated results, and the AUC was 0.91 for the human-rated results. The accuracy in the identification of ASD based on the computer- and human-rated results was, respectively, 74.8 and 82.9%. There was a significant difference between the AUC of the human-rated results and computer-rated results (Z = 2.71, P-value = 0.007). Conclusion: The multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.

Efficacy and moderating factors of the Early Start Denver Model in Chinese toddlers with autism spectrum disorder: a longitudinal study.

BACKGROUND: Several studies have shown the effectiveness of the Early Start Denver Model (ESDM), but few studies have explored the long-term efficacy of ESDM. This study aimed to explore the efficacy and moderating factors of ESDM in Chinese toddlers with autism spectrum disorder (ASD) in a longitudinal way. METHODS: A total of 60 toddlers with ASD were recruited and randomly divided into two groups: ESDM group all received 24 weeks intervention; Control group were waiting for intervention. Baseline assessment (T0) was conducted before intervention, including Gesell Developmental Scale (GDS) and Psycho-educational Profile-3rd Edition (PEP-3). All toddlers with ASD were examined in the first assessment (T1) at 6 months and in the second assessment (T2) at 12 months. RESULTS: In T1 assessment, the increments in speech and personal communication development quotient in GDS were significantly larger in the ESDM group than in the control group (P = 0.010, 0.047). In T2 assessment, the ESDM group had higher elevation in cognitive verbal/preverbal (CVP), social reciprocity and characteristic verbal behaviors assessed by PEP-3 (P = 0.021, 0.046, 0.014). In addition, the severity of stereotyped behavior was negatively associated with improvement in CVP. Family income was positively associated with improvement in speech and CVP (all P < 0.05). CONCLUSIONS: ESDM can effectively improve speech and communication in toddlers with ASD after 24-week intervention. More importantly, ESDM can promote cognition and social interaction and can reduce stereotyped verbal behavior in toddlers with ASD in longitudinal observation. The severity of stereotyped behavior and family ecological factors may be considered as affecting the efficacy of ESDM.