Subgroups of children with autism by cluster analysis: a longitudinal examination

[Formula: see text] Predicting language outcomes in bilingual children with Down syndrome

Continuous approaches to measuring bilingualism have recently emerged as a means of understanding individual variation in language abilities. To date, limited information is available to assist in understanding the language abilities of bilingual children with Down syndrome (DS), who are specifically known to have a large variation in linguistic outcomes. Group studies in this population report that children exposed to two languages do not differ from their monolingual counterparts after considering age and non-verbal cognitive abilities, although no study to date has examined the relationship between the amount of exposure to one language and the linguistic abilities in the other language within this population. This study sought to identify whether exposure to an additional language, specifically Welsh, predicted linguistic abilities in the majority language, in this case, English. Sixty-five children between the ages of 5;5-16;9 who had varied linguistic experiences completed a range of cognitive and linguistic assessments. Results from hierarchical regression analyses show that the amount of exposure to Welsh had no impact on language abilities in English, after controlling for non-verbal cognitive abilities, short-term memory and socioeconomic status. This demonstrates that exposure to an additional language does not have a negative impact on language development, a finding that has important clinical and educational implications.

Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder

Precision medicine is imminent, and metabolomics is one of the main actors on stage. We summarize and discuss the current literature on the clinical application of metabolomic techniques as a possible tool to improve early diagnosis of autism spectrum disorder (ASD), to define clinical phenotypes and to identify co-occurring medical conditions. A review of the current literature was carried out after PubMed, Medline and Google Scholar were consulted. A total of 37 articles published in the period 2010-2022 was included. Selected studies involve as a whole 2079 individuals diagnosed with ASD (1625 males, 394 females; mean age of 10, 9 years), 51 with other psychiatric comorbidities (developmental delays), 182 at-risk individuals (siblings, those with genetic conditions) and 1530 healthy controls (TD). Metabolomics, reflecting the interplay between genetics and environment, represents an innovative and promising technique to approach ASD. The metabotype may mirror the clinical heterogeneity of an autistic condition; several metabolites can be expressions of dysregulated metabolic pathways thus liable of leading to clinical profiles. However, the employment of metabolomic analyses in clinical practice is far from being introduced, which means there is a need for further studies for the full transition of metabolomics from clinical research to clinical diagnostic routine.

Tele-PCIT: Initial Examination of Internet Delivered PCIT for Young Children with Autism

PURPOSE

Parent-Child Interaction Therapy (PCIT) is an empirically supported behavioral parenting program for disruptive behavior and has been shown to also be effective for children with autism spectrum disorder (ASD). Telehealth delivery of PCIT (Tele-PCIT) is also supported, but no trials have focused on children with ASD. The purpose of the study was to examine the initial efficacy of a time limited version of Tele-PCIT within an ASD sample.

METHODS

Participants included parents of 20 children (ages 2-6) with ASD who received 10 sessions of Tele-PCIT. Parents reported on their parenting stress, parenting practices, and child behavior. A play observation was coded for parent use of treatment skills and for child compliance.

RESULTS

80% of participants completed treatment (n = 16) and results revealed significant improvements across parenting and child outcomes. Parents reported decreases in parenting stress from pre-to-post-treatment, which were maintained at a 3-month follow-up, along with decreases in negative parenting practices (i.e., Laxness and Overactivity) from pre-to-post treatment that were also maintained at follow-up. Significant increases in parent use of positive parenting skills (i.e., "Do" Skills) during child-led play and decreases in negative parenting skills (i.e., "Don't" Skills) were observed. Results also revealed significant reductions in parent rated child externalizing behavior problems from pre-to-post-treatment that were also maintained at follow-up.

CONCLUSIONS

Results of the current study support the initial efficacy of Tele-PCIT for treating disruptive behavior in young children with ASD. Findings from this pilot will inform larger examinations of Tele-PCIT for youth with ASD.

Classifying children with ASD by service utilization and treatment type: A cluster analysis of a nationally representative United States survey

BACKGROUND

If there are patterns of the distribution of services and treatments across the population of people with ASD, these patterns should be based along clinical characteristics or other service needs and not sociodemographic characteristics unrelated to evidence-based care. We examined how individuals in a broad, nationally representative sample "grouped together" based on service utilization and services needed but not covered by insurance. By understanding various treatment patterns, clinicians, researchers, policymakers, and self-advocates and their families can better advocate for high-quality, evidence-based services to be provided equitably.

METHODS

Using the 2011 Survey of Pathways to Diagnosis and Services, a cluster analysis was performed to explore patterns in this population based on medication use, private services use, school-based service use, and services not covered by insurance. Differences in clusters were then explored through multinomial logistic regression.

RESULTS

Six clusters emerged, showing differences in the level of service/medication usage and insurance coverage. Differences across clusters were associated with the level of functional limitation and age at ASD diagnosis. Disparities by insurance type, functional limitation, and age at diagnosis exist among patterns of ASD service provision.

CONCLUSIONS

Our analysis showed that intervention for children with ASD can be across several scales - high and low users of services (both private and school-based), high and low users of medications, and high and low levels of reported non-covered services. The differences were clustered in multiple ways. Further research should incorporate longitudinal and nationally representative data to explore these relationships further.

Autism screening: an unsupervised machine learning approach

Early screening of autism spectrum disorders (ASD) is a key area of research in healthcare. Currently artificial intelligence (AI)-driven approaches are used to improve the process of autism diagnosis using computer-aided diagnosis (CAD) systems. One of the issues related to autism diagnosis and screening data is the reliance of the predictions primarily on scores provided by medical screening methods which can be biased depending on how the scores are calculated. We attempt to reduce this bias by assessing the performance of the predictions related to the screening process using a new model that consists of a Self-Organizing Map (SOM) with classification algorithms. The SOM is employed prior to the diagnostic process to derive a new class label using clusters learnt from the independent features; these clusters are related to communication, repetitive traits, and social traits in the input dataset. Then, the new clusters are compared with existing class labels in the dataset to refine and eliminate any inconsistencies. Lastly, the refined dataset is utilised to derive classification systems for autism diagnosis. The new model was evaluated against a real-life autism screening dataset that consists of over 2000 instances of cases and controls. The results based on the refined dataset show that the proposed method achieves significantly higher accuracy, precision, and recall for the classification models derived when compared to models derived from the original dataset.

Autism-spectrum traits in neurotypicals predict the embodiment of manipulation knowledge about object concepts: Evidence from eyetracking

Sensorimotor-based theories of cognition predict that even subtle developmental motor differences, such as those characterizing autism spectrum disorder (ASD), impact how we represent the meaning of manipulable objects (e.g., faucet). Here, we test 85 neurotypical participants, who varied widely on the Adult Autism Spectrum Quotient (AQ), a measure intended to capture variability in ASD characteristics in the general adult population (participant scores were all below the clinical threshold for autism). Participants completed a visual world eyetracking task designed to assess the activation of conceptual representations of manipulable objects. Participants heard words referring to manually manipulable objects (e.g., faucet) while we recorded their eye movements to arrays of four objects: the named object, a related object typically manipulated similarly (e.g., jar), and two unrelated objects. Consistent with prior work, we observed more looks to the related object than to the unrelated ones (i.e., a manipulation-relatedness effect). This effect likely reflects the overlapping conceptual representations of objects sharing manipulation characteristics (e.g., faucet and jar), due to embodied sensorimotor properties being part of their representations. Critically, we observed—among typically developed young adults—that as AQ scores increased, manipulation-relatedness effects decreased. In contrast, in a visual control condition, in which a target object was paired with related objects of a similar shape (e.g., snake and rope), relatedness effects increased with AQ scores. The results show that AQ scores can predict variation in how object-concept representations are activated for typically developed individuals. More speculatively, they are consistent with the hypothesis that in individuals with ASD, differences in object-concept representations emerge at least in part via differences in sensorimotor experience.

Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

BACKGROUND AND OBJECTIVES

Recent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.

METHODS

We used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.

RESULTS

We included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.

DISCUSSION

Using a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features.

Features that best define the heterogeneity and homogeneity of autism in preschool-age children: A multisite case-control analysis replicated across two independent samples

The heterogeneous nature of children with symptoms of autism spectrum disorder (ASD) makes it difficult to identify risk factors and effective treatment options. We sought to identify behavioral and developmental features that best define the heterogeneity and homogeneity in 2-5-year-old children classified with ASD and subthreshold ASD characteristics. Children were enrolled in a multisite case-control study of ASD. Detailed behavioral and developmental data were gathered by maternal telephone interview, parent-administered questionnaires, child cognitive evaluation, and ASD diagnostic measures. Participants with a positive ASD screen score or prior ASD diagnosis were referred for comprehensive evaluation. Children in the ASD group met study criteria based on this evaluation; children who did not meet study criteria were categorized as having subthreshold ASD characteristics. There were 1480 children classified as ASD (81.6% boys) and 594 children classified as having subthreshold ASD characteristics (70.2% boys) in the sample. Factors associated with dysregulation (e.g., aggression, anxiety/depression, sleep problems) followed by developmental abilities (e.g., expressive and receptive language skills) most contributed to heterogeneity in both groups of children. Atypical sensory response contributed to homogeneity in children classified as ASD but not those with subthreshold characteristics. These findings suggest that dysregulation and developmental abilities are clinical features that can impact functioning in children with ASD and other DD, and that documenting these features in pediatric records may help meet the needs of the individual child. Sensory dysfunction could be considered a core feature of ASD and thus used to inform more targeted screening, evaluation, treatment, and research efforts. LAY SUMMARY: The diverse nature of autism spectrum disorder (ASD) makes it difficult to find risk factors and treatment options. We identified the most dissimilar and most similar symptom(s) in children classified as ASD and as having subthreshold ASD characteristics. Factors associated with dysregulation and developmental abilities contributed to diversity in both groups of children. Sensory dysfunction was the most common symptom in children with ASD but not those with subthreshold characteristics. Findings can inform clinical practice and research.

Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of unsupervised machine learning in ASD research and provide insight into the types of questions being answered with these methods.

Distinct Patterns of Cognitive Outcome in Young Children With Autism Spectrum Disorder Receiving the Early Start Denver Model

Evidence-based, early intervention significantly improves developmental outcome in young children with autism. Nonetheless, there is high interindividual heterogeneity in developmental trajectories during the therapy. It is established that starting intervention as early as possible results in better developmental outcomes. But except for younger age at start, there is no clear consensus about behavioral characteristics that could provide a reliable individual prediction of a child's developmental outcome after receiving an early intervention. In this study, we analyze developmental trajectories of preschoolers with autism who received 2 years of intervention using the Early Start Denver Model (ESDM) approach in Geneva, Switzerland in an individual setting (n = 55, aged 28.7 ± 5.1 months with a range of 15-42). Our aim was to identify early predictors of response to intervention. We applied a cluster analysis to distinguish between 3 groups based on their cognitive level at intake, and rates of cognitive change over the course of intervention. The first group of children only had a mild cognitive delay at intake and nearly no cognitive delay by the end of intervention (Higher Cognitive at baseline: HC). The children in the two other groups all presented with severe cognitive delay at baseline. However, they had two very different patterns of response to intervention. The majority significantly improved developmental scores over the course of intervention (Optimal Responders: OptR) whereas a minority of children showed only modest improvement (Minimal Responders: MinR). Further analyses showed that children who ended up having an optimal 2-year intervention outcome (OptR) were characterized by higher adaptive functioning at baseline combined with rapid developmental improvement during the first 6 months of intervention. Inversely, less significant progress by the sixth month of intervention was associated with a less optimal response to treatment (MinR).

Spoken words activate native and non-native letter-to-sound mappings: Evidence from eye tracking

Many languages use the same letters to represent different sounds (e.g., the letter P represents /p/ in English but /r/ in Russian). We report two experiments that examine how native language experience impacts the acquisition and processing of words with conflicting letter-to-sound mappings. Experiment 1 revealed that individual differences in nonverbal intelligence predicted word learning and that novel words with conflicting orthography-to-phonology mappings were harder to learn when their spelling was more typical of the native language than less typical (due to increased competition from the native language). Notably, Experiment 2 used eye tracking to reveal, for the first time, that hearing non-native spoken words activates native language orthography and both native and non-native letter-to-sound mappings. These findings evince high interactivity in the language system, illustrate the role of orthography in phonological learning and processing, and demonstrate that experience with written form changes the linguistic mind.

EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders

The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.

Sibling Adjustment and Sibling Relationships Associated with Clusters of Needs in Children with Autism: A Novel Methodological Approach

We tested a novel methodological approach to examine associations between characteristics of autistic children and outcomes for siblings. Cluster analysis was used to define five groups of children with autism (n = 168) based on autism symptoms, adaptive behavior, pro-social behavior, and behavior problems. Primary and secondary parent carers, and siblings themselves, reported on sibling relationship quality and psychological adjustment. Siblings of autistic children with a mild symptom profile, high levels of adaptive skills, but high internalizing and externalizing problems had the highest level of these problems themselves and more conflict in their relationship. Siblings of autistic children with the most complex support needs (adaptive skills deficits, severe autism symptoms) reported lower warmth relationships but not elevated internalizing and externalizing problems.

Intellectual Profile of Children with Autism Spectrum Disorder: Identification of verbal and nonverbal subscales predicting intelligence quotient

Objectives

This study aimed to explore the intelligence quotient (IQ) profile among children with autism spectrum disorder (ASD) and identify the most important subscales that predict the IQ. The analysis of an intellectual profile with age and gender differentials and the identification of a battery of subscales of intelligence are important for clinical management of ASD among children and for facilitating placement for remedial and educational services.

Methods

Data were collected through an exploratory study of 100 children aged between three and 13 years, who were referred to the department of child health and development in Sultan Qaboos University Hospital, a tertiary hospital, in Oman between June 2016 and June 2019.

Results

Among the 100 participants of this study, 79% were male, resulting in a male–female ratio of 4:1. The mean of full-scale IQ was found to be 68.6 ± 18.1. Furthermore, the mean of nonverbal IQ (73.5 ± 17.5) was significantly higher than that of verbal IQ (65.5 ± 17.6). Finally, more than half (61%) of the children were observed to have had mild to moderate impairment in their IQ levels.

Conclusions

Age and gender showed no significant association with IQ level. The regression analysis identified nonverbal fluid reasoning, nonverbal visual-spatial processing, nonverbal working memory and verbal knowledge as the significant predictors of total IQ. The crucial dimensions of verbal and nonverbal IQ identified in this study can be used to evaluate complicated cases.

Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study

Background

Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking.

Objective

The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups.

Methods

Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender.

Results

Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression (P<.003).

Conclusions

These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise.

Subgroups of Children with Autism Spectrum Disorder without Intellectual Disability: A Longitudinal Examination of Executive and Socio-Adaptive Behaviors in Adolescence

Within the autistic spectrum, there is remarkable variability in the etiology, presentation, and treatment response. This prospective study was designed to identify, through cluster analysis, subgroups of individuals with ASD without intellectual disability (ID) based on the severity of the core symptoms in childhood. The secondary aim was to explore whether these subgroups and a group with typical development (TD) differ in cognitive, adaptive, and social aspects measured in adolescence. The sample at baseline was comprised of 52 children with ASD without ID and 37 children with TD, aged 7-11. Among the ASD group, three clusters were identified. Cluster 1 (40%), 'high severity', presented high symptom severity on the DSM-5 criteria and the Social Communication Questionnaire. Cluster 2 (34%) showed 'moderate severity' on most of the scores. Cluster 3 (25%) corresponded to 'low severity', showing moderate social impairment and low restrictive, repetitive patterns of behavior, interests and activities. At 5-year follow-up, 45 adolescents with ASD without ID and 27 adolescents with TD were assessed. All clusters had significantly more difficulties in EF, ToM, socialization and adaptive behavior compared to TD. Social and adaptive trajectories between the ASD subgroups were relatively different; Cluster 3 showed poorer socialization and daily living skills than the other two subgroups. These findings highlight the importance of fully assessing social, cognitive, and adaptive profiles to develop care plans tailored to specific needs.

Identifying Subgroups of Toddlers with DSM-5 Autism Spectrum Disorder Based on Core Symptoms

The objective of this study was to identify subgroups of toddlers with DSM-5 ASD based on core ASD symptoms using a person-based analytical framework. This is a retrospective study of 500 toddlers (mean age 26 months, 79% male) with DSM-5 ASD. Data were analyzed using latent class analyses in which profiles were formed based on ASD symptomatology. Social communication (SC) symptoms favored a three-class solution, while restricted/repetitive behaviors (RRBs) favored a two-class solution. Classes with higher consistency of SC deficits were younger, with lower developmental functioning. The class with more RRBs was older, with higher functioning. If confirmed in other populations, these classes may more precisely characterize subgroups within the heterogeneous group of toddlers at time of ASD diagnosis.

Stability of Literacy Profiles of Adolescents With Autism Spectrum Disorder and Associations With Stakeholder Perceptions of Appropriate High School Support Needs

Purpose For many individuals with autism spectrum disorder (ASD), postsecondary outcomes are poor. This may be due to insufficient academic supports, particularly with regard to literacy skills, during high school. More information is needed about skill profiles so that we can better differentiate support for students with varying social, communication, cognitive, and academic proficiency levels. This study was designed to (a) identify unique literacy profiles of high school students with ASD, (b) assess profile stability over time, (c) identify predictors of profile membership, and (d) analyze stakeholder reports of required school support intensity. Method Participants were a diverse sample of high school students with ASD, 14-21 years old (N = 544), their parents, and their teachers who participated in a randomized controlled trial of a comprehensive treatment model for high school students with ASD. Standardized measures were administered to assess nonverbal IQ, autism symptomatology, language/adaptive communication, reading comprehension, academic knowledge, and parent/teacher report of school support needs intensity. Latent transition analysis was conducted to examine sample heterogeneity and to explore the stability of the profiles. Associations between profiles and reports of support intensity were examined. Results Four literacy profiles were identified that were stable over 2 years: Emergent Literacy/Comprehensive Support, Low Literacy/Intensive Support, Average Literacy/Moderate Support, and Average Literacy/Limited Support. Parent and teacher reports of school support intensity generally aligned with the profiles. Conclusions These analyses provide insight into the diverse literacy and support needs in ASD. Implications for practice and the role of speech-language pathologists in assessment and intervention are discussed. Supplemental Material https://doi.org/10.23641/asha.13495119.

Application of Latent Class Analysis to Identify Subgroups of Children with Autism Spectrum Disorders who Benefit from Social Skills Training

With Latent Class Analysis applied on data of 98 children with autism spectrum disorder (ASD) (9-12 years; 17 girls) participating in social skills training (SST) in a randomized controlled trial (Dekker et al. 2019), four subgroups were detected, based on social-communicative skills before, and response patterns to training. Two subgroups improved after SST. Characterizing the subgroups based on participant and intervention characteristics showed that improvement was related to lower parent-reported perceived difficulty of social-communicative skills at start, higher verbal ability, younger age and milder symptoms of ASD and anxiety. The lowest performing non-improving subgroup participated more often in SST without parent/teacher involvement, compared to all other subgroups. Response to SST in ASD seems to vary depending on participant characteristics.

How Children with Autism Spectrum Disorder, Developmental Language Disorder, and Typical Language Learn to Produce Global and Local Semantic Features

A local processing bias, often considered a cognitive style unique to autism spectrum disorder (ASD), may influence the types of semantic features acquired by children with ASD and could contribute to weaknesses in word learning. Children with developmental language disorder (DLD) also struggle to learn semantic aspects of words, but this cognitive style has not been ascribed to children with DLD. The purpose of this study was to explore whether global–local processing differences influence the type of semantic features children with ASD, DLD, and their neurotypical peers learn to produce when learning new words. Novel word definitions produced by 36 school-aged children (12 with ASD, 12 with DLD, and 12 with typical language) who participated in an extended word-learning paradigm were used to extract newly learned semantic features. These semantic features were then coded for global and local attributes and analyzed to detect whether there were differences between groups. Results indicated that the children with ASD and DLD produced more global, rather than local, semantic features in their definitions than the children with typical language. An over-reliance on global, rather than local, features in children with ASD and DLD may reflect deficits in depth of word knowledge.

Exploring the profiles of children with autism spectrum disorder: association with family factors

This study aimed to identify different profiles of children with autism spectrum disorder (ASD) without intellectual disability (ID) and maternal factors characteristic of these profiles. Participants were 89 children between 7 and 11 years old and their mothers, distributed in two groups, 52 children with ASD and 37 with typical development (TD). Based on the children's adaptive, behavioral, and pragmatic manifestations, three clusters were obtained in the ASD group. Children with the most serious difficulties in all the domains assessed were assigned to Cluster 1, whereas the children in Cluster 3 were characterized by relatively minor problems. Cluster 2 comprised children with moderate difficulties in the different domains. Compared to the TD group, group 1, with 'Serious Difficulties', and group 2, with 'Moderate Difficulties', showed significant differences on all the variables evaluated. Group 3, 'Slight Difficulties', was only significantly differentiated from the group with TD on ASD and attention deficit hyperactivity disorder (ADHD) symptoms, pragmatic competence, and applied theory of mind. Therefore, it was outlined as the purest group and basically manifested core ASD symptoms. In addition, mothers of children in the 'Serious Difficulties' group showed higher stress levels and less use of emotional and instrumental support and acceptance strategies, as well as more mental health problems, than mothers of children in the 'Slight Difficulties' group. The results support the need to assess the presentation of other possible problems in ASD children without ID, and design interventions for parents who may be at risk of experiencing higher levels of stress and have fewer coping strategies.

Review of cluster analysis of phenotypic data in Autism Spectrum Disorders: distinct subtypes or a severity gradient model?

Background: Individuals with autism spectrum disorder (ASD) form a heterogeneous group, posing a challenge for clinical definition. Additional problems regarding the diverse clinical presentation arise from changes in diagnostic criteria according to the latest Diagnostic and Statistical Manual of Mental Disorders (DSM-5), with exclusion of individuals who met earlier criteria or inclusion of more than previously. Objectives: To investigate studies that have attempted to reduce the heterogeneity of ASD based on cluster analysis of phenotypic data and to clarify whether ASD should be interpreted as 'a unitary spectrum,' with a severity gradient, or defined by distinct subtypes. This will allow better understanding of the disorder with implications for its treatment and prognosis. Methods: A literature search was made through PubMed, Researchgate and Google Scholar for studies of ASD populations. In addition, reference lists from identified studies were reviewed. Results: Only 10 studies were found that dealt with the heterogeneity of ASD and its different subtypes, based on the review prerequisites. Most of the studies appear to support the existence of subtypes within ASD, but it remains unclear whether these are considered as different specific subtypes with characteristic profiles of symptoms or as a part of a severity gradient across symptom domains. Conclusions: Drawing definitive conclusions from the published studies about the nature of ASD is difficult, due to the fundamental methodological differences among the studies and their inconsistent findings. This review shed light on a number of discrepancies regarding the current classification of ASD. However, future research will be necessary to provide a more definite answer on the question of a definition based on separate diagnostic subtypes or on a severity gradient by including larger samples that are followed longitudinal and by applying better diagnostic system and choosing the appropriate variables.

An intervention targeting social, communication and daily activity skills in children and adolescents with Down syndrome and autism: a pilot study

PURPOSE

To evaluate whether an intervention, targeting deficits in social communication, interaction and restricted activities in children and adolescents with Down syndrome and autism could lead to enhanced participation in family and school activities.

METHODS

The intervention included education for parents and school staff about autism, and workshops to identify social-communication and daily living activities that would be meaningful for the child to practice at home and at school. Thereafter, a three-month period of training for the child followed. Outcome measures comprised evaluation of goal achievement for each child, the "Family Strain Index" questionnaire and a visual scale pertaining to the parents' general opinion about the intervention.

RESULTS

On average, more than 90% of the goals were (to some extent or completely) achieved at home and at school. The mean scores of the "Family Strain Index" were almost identical at the follow-up to those before intervention. The evaluation supported that the use of strategies, intended to facilitate activities and communication, remained largely 18 months after start of the intervention.

CONCLUSION

Despite the group involved in this study being composed of older children and adolescents, most of whom had severe and profound intellectual disability, the goal achievements and parents' views on the intervention were encouraging.

Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes

With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant biomarkers that delineate the resulting subgroups. The optimal results obtained favored predominantly a 5-cluster configuration.

Differential outcome subgroups in children with autism spectrum disorder attending early intervention

BACKGROUND

The finding of positive outcomes at the group level for children with autism spectrum disorder (ASD) who complete comprehensive early intervention programmes often masks considerable individual variability. We therefore aimed to identify subgroups of children based on their response to intervention and to compare outcome variables between groups at two points in time.

METHOD

We used model-based cluster analysis to explore response to intervention using a longitudinal design for 210 children with ASD who had completed an early intervention programme. Children were assessed on entry at time 1 and again at time 2, which was after 12 months or when they exited the programme (whichever came first) using measures of ASD symptoms (Social Communication Questionnaire), cognition (Mullen Scales of Early Learning) and adaptive behaviour (Vineland Adaptive Behaviour Scales-II).

RESULTS

A two-cluster solution was identified, including a high change group who improved consistently more than the low change group across measures, and showed significantly fewer autism symptoms, higher non-verbal and verbal cognition and adaptive behaviour composite scores at time 1.

CONCLUSIONS

The findings indicated that children's response to early intervention is not uniform but instead included subgroups characterised by patterns of high and low change. Further research is needed to identify clinically relevant mediators of differential response group membership.

A Longitudinal Study of Children Diagnosed with Autism Spectrum Disorder Before Age Three: School Services at Three Points Time for Three Levels of Outcome Disability

This study follows 70 children determined to have Autism Spectrum Disorder (ASD) before age three (Time 1). Parents filled out questionnaires and standardized measures about their child when he/she was school-aged (Time 2), including information about their children's preschool, kindergarten, and grade school educational settings. At Time 2, the researchers placed children in three diagnostic groups of No ASD, ASD-Higher Functioning, and ASD-Lower Functioning. Retrospective results showed that most children were receiving intensive services at the preschool level. In kindergarten, there was some divergence among the three groups, with more intensive services continuing for the ASD groups. At school age, classroom placement and services reflected service patterns that were consistent with these three levels of disability.

Cluster analysis of autism spectrum disorder symptomatology: Qualitatively distinct subtypes or quantitative degrees of severity of a single disorder?

The decision to collapse several related disorders into a single diagnosis of Autism Spectrum Disorder (ASD) generated significant controversy and debate. There has been mixed evidence as to whether various ASD subtypes are qualitatively distinct or if they exist on a spectrum of symptom severity. The present study conducted a two-step cluster analysis of major ASD symptoms in a sample of 147 young males with ASD aged between 6yr and 18yr with IQ > 70. Results indicated that a two-cluster solution (high and low severity of ASD symptomatology) was reliable and valid. Further, the construct of challenging behaviour was not a necessary component of the two-cluster solution, verifying the new conceptualisation of ASD. Further replication of these findings with other subsets of individuals with ASD is needed.

Performing an Informatics Consult: Methods and Challenges

Our health care system is plagued by missed opportunities, waste, and harm. Data generated in the course of care are often underutilized, scientific insight goes untranslated, and evidence is overlooked. To address these problems, we envisioned a system where aggregate patient data can be used at the bedside to provide practice-based evidence. To create that system, we directly connect practicing physicians to clinical researchers and data scientists through an informatics consult. Our team processes and classifies questions posed by clinicians, identifies the appropriate patient data to use, runs the appropriate analyses, and returns an answer, ideally in a 48-hour time window. Here, we discuss the methods that are used for data extraction, processing, and analysis in our consult. We continue to refine our informatics consult service, moving closer to a learning health care system.

Subgrouping children with cerebral palsy from a broader perspective using two methods

The purpose was to develop two versions of a body function index in cerebral palsy (BFI-CP I and BFI-CP II) using two methods to explore the relationship and differences among them and the Gross Motor Function Classification System (GMFCS) and to explore the differences among subsets of the classifications that do not correspond to the ordinal levels of the GMFCS. Data on various measures from 405 children with CP between 18 months and 5 years of age were extracted from the Move & PLAY Study. The BFI-CP I was developed using a summing technique and the BFI-CP II was developed using cluster analysis. There was a strong correlation between the BFI-CP I and the GMFCS (r = 0.92), between the BFI-CP II and the GMFCS (r = 0.93), and between the BFI-CP I and II (r = 0.92) all (p < 0.001). There was also a significant difference between the BFI-CP I and the GMFCS (χ² = 670.49, df = 16, p < 0.001) and between the BFI-CP II and the GMFCS (χ² = 685.57, df = 16, p < 0.001). The findings of this study indicate that the two versions of the BFI-CP could be used as complementary methods in describing children with CP.

What will my child's future hold? phenotypes of intellectual development in 2-8-year-olds with autism spectrum disorder

We examined phenotypes of autism spectrum disorder (ASD) based on trajectories of intellectual development from early (ages 2-3 ½) to middle (ages 5-8) childhood in a recent clinically ascertained cohort. Participants included 102 children (82 males) initially diagnosed with ASD from the Autism Phenome Project longitudinal sample. Latent class growth analysis was used to identify distinct IQ trajectories. Baseline and developmental course differences among groups were assessed using univariate techniques and repeated measures regression models, respectively. A four class model best represented the data. Using the highest posterior probability, participants were assigned to High Challenges (25.5%), Stable Low (17.6%), Changers (35.3%), and Lesser Challenges (21.6%) groups. The High Challenges and Stable Low groups exhibited persistently low IQ, although, the High Challenges group experienced declines while the Stable Low group's scores remained more constant. Changers showed IQ improvement of > 2 standard deviations. The Lesser Challenges group had IQs in the average range at both times that were about 1 standard deviation higher at T2. In summation, 75% of the participants experienced some relative improvements in intellectual and/or other areas of functioning between ages 2 and 8 years. The Changers group demonstrated the most significant IQ change that was accompanied by adaptive communication improvement and declining externalizing symptoms. Only the Lesser Challenges group showed a significant reduction in ASD symptom severity, such that by age 8, 14% of them no longer met ADOS-2 criteria for ASD. All groups showed reductions in internalizing symptoms. Intervention history was not associated with group status. Autism Res 2018, 11: 121-132. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

We examined how the IQs of children with autism spectrum disorder change between ages 2 and 8, and identified four patterns. Two groups exhibited persistently lower IQs. One group showed IQ increases of greater than 30 points with improved communicate abilities and declining disruptive behaviors. The final group had IQs in the average or better range at both time points, and 14% of them lost their diagnoses. Over half of the children experienced improved intellectual functioning between ages 2 and 8, whereas about 25% showed declines. Findings were not associated with intervention history.

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development

The objective of this study was to identify homogenous classes of young children with autism spectrum disorder (ASD) to improve phenotypic characterization. Children were enrolled in the Study to Explore Early Development between 2 and 5 years of age. 707 children were classified with ASD after a comprehensive evaluation with strict diagnostic algorithms. Four classes of children with ASD were identified from latent class analysis: mild language delay with cognitive rigidity, mild language and motor delay with dysregulation, general developmental delay, and significant developmental delay with repetitive motor behaviors. We conclude that a four-class phenotypic model of children with ASD best describes our data and improves phenotypic characterization of young children with ASD. Implications for screening, diagnosis, and research are discussed.

Preschool to School in Autism: Neuropsychiatric Problems 8 Years After Diagnosis at 3 Years of Age

The study presents neuropsychiatric profiles of children aged 11 with autism spectrum disorder, assessed before 4.5 years, and after interventions. The original group comprised a community sample of 208 children with ASD. Parents of 128 participated-34 with average intellectual function, 36 with borderline intellectual function and 58 with intellectual disability. They were interviewed using the Autism-Tics, AD/HD and other Comorbidities interview. Criteria for a clinical/subclinical proxy of ASD were met by 71, 89 and 95 %, respectively. Criteria for at least one of ASD, AD/HD, Learning disorder or Developmental Coordination Disorder were met by 82, 94 and 97 %. More than 90 % of children with a preschool diagnosis of ASD have remaining neuropsychiatric problems at 11, despite early intervention.

Classifying Autism Spectrum Disorders by ADI-R: Subtypes or Severity Gradient?

To reduce phenotypic heterogeneity of Autism spectrum disorders (ASD) and add to the current diagnostic discussion this study aimed at identifying clinically meaningful ASD subgroups. Cluster analyses were used to describe empirically derived groups based on the Autism Diagnostic Interview-revised (ADI-R) in a large sample of n = 463 individuals with ASD aged 3-21. Three clusters were observed. Most severely affected individuals regarding all core symptoms were allocated to cluster 2. Cluster 3 comprised moderate symptom severity of social communication impairments (SCI) and less stereotyped repetitive behavior (RRB). Minor SCI and relatively more RRB characterized cluster 1. This study offers support for both, a symptom profile, and a gradient model of ASD within the spectrum due to the sample included.

Continuity and Change in Cognition and Autism Severity from Toddlerhood to School Age

This paper charted the cognitive and behavioural profiles from toddlerhood to middle childhood in 48 children diagnosed with ASD at 24-months. The Mullen Scales of Early Learning (MSEL) was administered at 24- and 48-months and the Wechsler Abbreviated Scale of Intelligence (WASI) at school age. Autism severity was derived using The Autism Diagnostic Observation Schedule (ADOS) Results: Developmental Disability/Intellectual Disability (DD/ID; Developmental Quotient <70) reduced from 64% at 24-months to 8% at outcome. Seventy-three percent of children continued to meet ADOS cut-off at school age.

CONCLUSION

Diagnoses at 24-months, appear to be reliable and stable. Further research is needed to investigate whether early identification, which provides more opportunity to access early intervention, may in turn facilitate cognitive development over time.

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

Background

Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4–15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10–24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects.

Results

Preliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p < 0.05 following Holm–Bonferroni correction. Features derived from hippocampal regions also demonstrate high discriminative power for differentiating between ASD and DC subjects, with classifier accuracy of 67.85%, sensitivity of 62.50%, specificity of 71.42%, and the area under the ROC curve (AUC) of 76.80% for age-matched subjects with typical age range.

Conclusions

Results demonstrate the potential of hippocampal texture features as a biomarker for the diagnosis and characterization of ASD.

Service system and cognitive outcomes for young children with autism spectrum disorders in a rural area of Taiwan

Chiayi is a rural county located in southwestern Taiwan, and the effectiveness of its early intervention service system for autism spectrum disorders was studied in detail. A total of 71 children with autism spectrum disorders ( n = 35) and developmental delay ( n = 36) aged 2.5 years were referred from the only Early Intervention Reporting and Referral Center in Chiayi and followed up at 4 years. Results showed relatively low and varied services of early intervention for both groups during two time-point periods and a relative lack of specific early intervention programs for children with autism spectrum disorders. It was found, however, that cognitive abilities were increased for autism spectrum disorders and developmental delay groups. Additionally, the Early Learning Score at the initial evaluation could contribute to the high learner autism spectrum disorders subgroup. Parental socio-economic level was also determined to benefit the high learner developmental delay subgroup.

Perspectives on classification of selected childhood neurodisabilities based on a review of literature

PURPOSE

Classifying children with heterogeneous health conditions is challenging. The purposes of this perspective are to explore the prevailing classifications in children with the three selected neurodisabilities using the underlying framework of ICF/ICF-CY, explore the utility of the identified classifications, and make recommendations aimed at improving classifications.

METHODS

A literature search on six databases and Google was conducted. Articles published between the years 2000 and 2013 were included if they provided information on classification of cerebral palsy (CP), and/or developmental coordination disorder (DCD) and/or autism spectrum disorders (ASD).

RESULTS

Children with DCD and ASD are classified using combinations of multiple measures. The classifications in CP meet more of the proposed criteria for utility than those in DCD and ASD.

CONCLUSION

None of the existing classifications addressed all the criteria. The heterogeneity associated with the selected neurodisabilities poses major challenges. Further work is required to establish improved classifications.

Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

Early Characteristics of Children with ASD Who Demonstrate Optimal Progress Between Age Two and Four

Although for many children, Autism Spectrum Disorder (ASD) is a lifelong disability, a subset of children with ASD lose their diagnosis and show typical cognitive and adaptive abilities. The ages at which this transition can occur is not known, but it sometimes occurs quite early. Participants in the current study were 207 children with an ASD at age two who were reevaluated at age four. Eighty-three percent retained an ASD diagnosis at reevaluation and 9 % showed "optimal progress": clear ASD at age two but not at age four, and average cognition, language, communication and social skills at age four. Early child-level factors predicted optimal progress: diagnosis of PDD-NOS, fewer repetitive behaviors, less severe symptomatology and stronger adaptive skills.

Restricted and Repetitive Behaviors as Predictors of Outcome in Autism Spectrum Disorders

Questions have been raised about the significance of restricted and repetitive behaviors (RRBs) in predicting outcomes of children with Autism Spectrum Disorders (ASDs). Previous studies have yielded mixed findings, but some suggest that the presence of RRBs during preschool years is a negative prognostic indicator for later childhood. This study examined the effect of RRBs at ages 1-2 and 3-5 years on cognitive functioning, adaptive abilities, and ASD symptomatology at age 8-10 years in 40 children with ASDs. At 1-2 years, RRBs did not predict later functioning. However, at 3-5 years, more severe preoccupations with parts of objects, sensory interests, and stereotyped motor movements predicted less developed cognitive and adaptive skills, and greater ASD symptom severity at age 8-10 years.

Characteristics of communication among Japanese children with autism spectrum disorder: A cluster analysis using the Children's Communication Checklist-2

Some overlap has been suggested among the subtypes of autism spectrum disorder (ASD) in children. The Japanese version of the Children's Communication Checklist-2 (CCC-2) is a useful measure for identifying profiles in relation to communication impairments in children with ASD. The aim of this study was to investigate whether the CCC-2 could identify subtypes in relation to communication impairments in Japanese children with ASD. The study participants were 113 children with ASD but without intellectual disabilities aged 3-12 years. Parents were given the Japanese version of the CCC-2 and asked to rate their children, who were then classified into two groups based on statistical analysis. Significant differences were found between clusters in mean CCC-2 subscales. These results suggest that one subtype was associated with low language competence and strong characteristics of autism, while the other was associated with relatively high language competence and milder characteristics of autism.

Theory of mind predicts severity level in autism

We investigated whether theory of mind skills can indicate autism spectrum disorder severity. In all, 62 children with autism spectrum disorder completed a developmentally sensitive theory of mind battery. We used intelligence quotient, Diagnostic and Statistical Manual of Mental Disorders (4th ed.) diagnosis and level of support needed as indicators of severity level. Using hierarchical cluster analysis, we found three distinct clusters of theory of mind ability: early-developing theory of mind (Cluster 1), false-belief reasoning (Cluster 2) and sophisticated theory of mind understanding (Cluster 3). The clusters corresponded to severe, moderate and mild autism spectrum disorder. As an indicator of level of support needed, cluster grouping predicted the type of school children attended. All Cluster 1 children attended autism-specific schools; Cluster 2 was divided between autism-specific and special needs schools and nearly all Cluster 3 children attended general special needs and mainstream schools. Assessing theory of mind skills can reliably discriminate severity levels within autism spectrum disorder.

Effectiveness of community-based treatment on clinical outcome in children with autism spectrum disorders: An Italian prospective study

OBJECTIVE

Little is known about outcomes of Autism Spectrum Disorders (ASDs) interventions in real-life settings. The main aim of this naturalistic study was to collect real-life data on the actual ASDs treatment practices in Italy.

METHODS

A cohort of 48 children undergoing community-based interventions was observed in terms of personal and environmental characteristics, treatment typology and outcomes.

RESULTS

An earlier start of treatment was associated with an improvement of autistic symptoms, independently from symptoms severity (p < 0.05), but not with improvements in terms of intelligence quotient (p = 0.8). Children belonging to lower socioeconomic status families began treatment later (48.0 months) than those belonging to middle (39.8 months) or upper (39.2 months) classes (p < 0.05), and received less hours of treatment.

CONCLUSION

The study showed that ASDs interventions should be observed not only in experimental settings, but also in naturalistic environments, so to appraise the actual effectiveness of integrating different treatment methods in community settings.

Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes

Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.