Moving beyond a categorical diagnosis of autism

Exploring cognitive, behavioral and autistic trait network topology in very preterm and term-born children

Introduction

Compared to full-term (FT) born peers, children who were born very preterm (VPT; <32 weeks' gestation) are likely to display more cognitive and behavioral difficulties, including inattention, anxiety and socio-communication problems. In the published literature, such difficulties tend to be studied independently, thus failing to account for how different aspects of child development interact. The current study aimed to investigate children's cognitive and behavioral outcomes as interconnected, dynamically related facets of development that influence one another.

Methods

Participants were 93 VPT and 55 FT children (median age 8.79 years). IQ was evaluated with the Wechsler Intelligence Scale for Children-4th edition (WISC-IV), autism spectrum condition (ASC) traits with the social responsiveness scale-2nd edition (SRS-2), behavioral and emotional problems with the strengths and difficulties questionnaire (SDQ), temperament with the temperament in middle childhood questionnaire (TMCQ) and executive function with the behavior rating inventory of executive functioning (BRIEF-2). Outcome measures were studied in VPT and FT children using Network Analysis, a method that graphically represents partial correlations between variables and yields information on each variable's propensity to form a bridge between other variables.

Results

VPT and FT children exhibited marked topological differences. Bridges (i.e., the variables most connected to others) in the VPT group network were: conduct problems and difficulties with organizing and ordering their environment. In the FT group network, the most important bridges were: difficulties with initiating a task or activity and prosocial behaviors, and greater emotional problems, such as lower mood.

Discussion

These findings highlight the importance of targeting different aspects of development to support VPT and FT children in person-based interventions.

Developmental and Intelligence Quotient in Autism: A Brief Report on the Possible Long-Term Relation

Developmental level and cognitive skills assessment represents a crucial aspect in the delineation of the clinical phenotype and long-term outcomes of individuals with autism spectrum disorder (ASD). Nevertheless, the evaluation of cognitive development trajectory across a lifespan ranging from birth to school age appears challenging for clinicians and researchers, because of the lack of measures that coherently cover this timeframe. Thus, the main goal of this community-based study was to investigate within a sample of ASD children if the developmental quotient (DQ), evaluated through the Griffiths Mental Development Scales Extended Revised (GMDS-ER) scale, predicts the non-verbal brief intelligence quotient (IQ), measured through the Leiter-R at follow-up. The main observation of our study was a positive correlation between the level of DQ and nonverbal IQ at follow-up evaluations, highlighting that ASD children characterized by a greater developmental profile will later present higher non-verbal IQ.

High Intellectual Potential and High Functioning Autism: Clinical and Neurophysiological Features in a Pediatric Sample

High Intellectual Potential (HIP) and High Functioning Autism (HFA) are two different conditions sharing some clinical and neurobiological features. The aim of the present study was to characterize a sample of HIP children (n: 16; M/F: 14/2; median age: 10 years) in comparison to those with HFA (n: 17; M/F: 16/1; median age: 13 years) and to neurotypically developed (NTD) children (n: 10; M/F: 4/6; median age: 11 years) from a clinical and neurophysiological perspective. Specifically, a standardized clinical assessment of cognitive and adaptive skills, autistic symptoms, executive functions and behavioral features was performed. Moreover, event-related potentials (ERPs) were recorded, referring specifically to the mismatch negativity (MMN) and P300 paradigm. Our data highlighted the presence of similarities between the intellectually gifted individuals and the ones with autism (i.e., a nonhomogeneous intellective profile, an adaptive skills impairment, subthreshold autistic symptoms and increased perfectionism). Interestingly, a distinct neurophysiological characterization between groups came out, with evidence of a reduced MMN amplitude only in the HFA group. Furthermore, no differences within groups in the P300 component emerged. Therefore, our results start to provide a more informative characterization of the HIP phenotype in comparison to those of HFA and NTD, highlighting the potential role of the MMN amplitude index in helping clinicians and researchers to distinguish between HIP and HFA. Nevertheless, further research on the topic is strongly needed.

Trait Autism is a Better Predictor of Empathy than Alexithymia

It has been proposed that atypical empathy in autism spectrum disorder (ASD) is due to co-occurring alexithymia. However, difficulties measuring empathy and statistical issues in previous research raise questions about the role of alexithymia in empathic processing in ASD. Addressing these issues, we compared the associations of trait alexithymia and autism with empathy in large samples from the general population. Multiple regression analyses showed that both trait autism and alexithymia were uniquely associated with atypical empathy, but dominance analysis found that trait autism, compared to alexithymia, was a more important predictor of atypical cognitive, affective, and overall empathy. Together, these findings indicate that atypical empathy in ASD is not simply due to co-occurring alexithymia.

Network Analysis of Anxiety in the Autism Realm

The anxiety and autism realms are each complicated and heterogeneous, and relationships between the two areas are especially complex. Network analysis offers a promising approach to the phenotypic complexities of typical and atypical human behavior. The Revised Children's Anxiety and Depression Scale (RCADS) was used to assess anxiety in 126 high-functioning 9-13 year-olds with ASDs. Network graphs of Autism Diagnostic Observation Schedule items and RCADS anxiety total score, social, generalized, panic and separation anxiety subscores consistently found the anxiety node (score) to be highly peripheral. Also, the networks of RCADS anxiety items themselves were similar for the ASDs group and a general population comparison group (n = 2017). The results suggest anxiety is not a central part of autism and that anxiety is dynamically similar (aspects of anxiety relate to one another in a similar manner) in high-functioning autism and the general population.

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm

DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.

Do Handwriting Difficulties Correlate with Core Symptomology, Motor Proficiency and Attentional Behaviours?

Handwriting is commonly identified as an area of weakness in children with autism spectrum disorder (ASD), but precise deficits have not been fully characterised. Boys with ASD (n = 23) and matched controls (n = 20) aged 8-12 years completed a simple, digitised task to objectively assess handwriting performance using advanced descriptive measures. Moderate to large associations were identified between handwriting performance and attention, ASD symptoms and motor proficiency. The ASD group demonstrated significantly less smooth movements and significantly greater sizing variability and peak velocity relative to controls. These findings provide a clearer indication of the specific nature of handwriting impairments in children with ASD, and suggest a relationship with core clinical symptom severity, attention and motor behaviours.

Variation in the Early Trajectories of Autism Symptoms Is Related to the Development of Language, Cognition, and Behavior Problems

OBJECTIVE

The objectives of this study were to model more homogeneous subgroups within autism spectrum disorder (ASD) based on early trajectories of core symptoms; and to further characterize these subgroups in terms of trajectories of language, cognition, co-occurring (attention-deficit/hyperactivity disorder [ADHD]-related) traits and clinical outcome diagnosis.

METHOD

Children (N = 203) referred for possible ASD at ages 1 to 4 years were assessed at three time points at intervals ranging from 9 months to 3 years. Assessments included standardized measures for ASD (Autism Diagnostic Observation Schedule [ADOS]), language (ADOS-language item), nonverbal IQ (NV-IQ; different tests adequate to chronological/mental age), and parent-reported behavioral problems (Infant-Toddler Social and Emotional Assessment, Child Behavior Checklist).

RESULTS

Latent-class growth curve analysis with ADOS total scores led to the identification of three main stable and two small improving groups: a severe-stable group (19.5% of sample)-the only group without considerable language improvement-showed persistent low NV-IQ and marked increase in attention problems over time; a moderate-stable group (21.7%) with below-average increasing NV-IQ; and a mild-stable group (48%) with stable-average NV-IQ and the highest scores on ADHD-related traits, whose ASD outcome diagnoses increased despite stable-low ASD scores. Two groups (each 5.4%) improved: one moved from severe to moderate ASD scores, and the other moved from moderate to mild/nonspectrum scores. Both of these groups improved on language, NV-IQ, and ADHD-related traits.

CONCLUSION

Results support the high stability of ASD symptoms into various severity levels, but also highlight the significant contribution of non-ASD domains in defining and explaining the different ASD trajectories.

Considerations in biomarker development for neurodevelopmental disorders

PURPOSE OF REVIEW

Despite significant progress in recognizing the biological bases of autism spectrum disorder, diagnosis and treatment rely primarily on subjective evaluation of behavior. This review highlights the challenges unique to neurodevelopmental disorders that have limited biomarker development.

RECENT FINDINGS

The field of neurodevelopmental disorders requires objective quantification of biological processes to enable designation of subgroups likely to benefit from specific treatments, index diagnostic status/risk, demonstrate engagement of targeted systems, and provide more rapid assessment of change than traditional clinical observation and caregiver report measures.

SUMMARY

Useful biomarkers for neurodevelopmental disorders must be reliable across development, evident at the individual level, and specific to a unit of analysis, be it diagnostic status or functional process. The ultimate value of biomarkers for neurodevelopmental disorders will relate to their ease of use, cost, scalability, sensitivity, and methodological objectivity.

ASD Validity

ASD research is at an important crossroads. The ASD diagnosis is important for assigning a child to early behavioral intervention and explaining a child’s condition. But ASD research has not provided a diagnosis-specific medical treatment, or a consistent early predictor, or a unified life course. If the ASD diagnosis also lacks biological and construct validity, a shift away from studying ASD-defined samples would be warranted. Consequently, this paper reviews recent findings for the neurobiological validity of ASD, the construct validity of ASD diagnostic criteria, and the construct validity of ASD spectrum features. The findings reviewed indicate that the ASD diagnosis lacks biological and construct validity. The paper concludes with proposals for research going forward.