Early-onset restrictive food intake disorders in children: a latent class analysis.

The two most frequent early-onset restrictive food intake disorders are early-onset anorexia nervosa (EOAN) and avoidant/restrictive food intake disorders (ARFID). Although the core symptoms of EOAN (i.e., fear of gaining weight and disturbed body image) are not present in ARFID, these symptoms are difficult to assess during the initial phase of hospitalisation. Our aim was  to identify restrictive food intake disorder subtypes in children using latent class analysis (LCA) based on the information available at admission to hospital, and to determine the agreement between the subtypes identified using LCA and the final diagnosis: EOAN or ARFID. We retrospectively included 97 children under 13 years old with severe eating disorders (DSM-5) at their first hospitalisation in a specialised French paediatric unit. LCA was based on clinical information, growth chart analyses and socio-demographic parameters available at admission. We then compared the probabilities of latent class membership with the diagnosis (EOAN or ARFID) made at the end of the hospitalisation. The most parsimonious LCA model was a 2-class solution. Children diagnosed with EOAN at the end of hospitalisation had a 100% probability of belonging to class 1 while children diagnosed with ARFID had an 8% probability of belonging to class 1 based on parameters available at admission. Our results indicate that clinical and socio-demographic characteristics other than the core symptoms of EOAN may be discriminating for a differential diagnosis.

Cortico-Cerebellar neurodynamics during social interaction in Autism Spectrum Disorders.

BACKGROUND: Exploring neural network dynamics during social interaction could help to identify biomarkers of Autism Spectrum Disorders (ASD). A cerebellar involvement in autism has long been suspected and recent methodological advances now enable studying cerebellar functioning in a naturalistic setting. Here, we investigated the electrophysiological activity of the cerebro-cerebellar network during real-time social interaction in ASD. We focused our analysis on theta oscillations (3-8 Hz), which have been associated with large-scale coordination of distant brain areas and might contribute to interoception, motor control, and social event anticipation, all skills known to be altered in ASD. METHODS: We combined the Human Dynamic Clamp, a paradigm for studying realistic social interactions using a virtual avatar, with high-density electroencephalography (HD-EEG). Using source reconstruction, we investigated power in the cortex and the cerebellum, along with coherence between the cerebellum and three cerebral-cortical areas, and compared our findings in a sample of participants with ASD (n = 107) and with typical development (TD) (n = 33). We developed an open-source pipeline to analyse neural dynamics at the source level from HD-EEG data. RESULTS: Individuals with ASD showed a significant increase in theta band power over the cerebellum and the frontal and temporal cortices during social interaction compared to resting state, along with significant coherence increases between the cerebellum and the sensorimotor, frontal and parietal cortices. However, a phase-based connectivity measure did not support a strict activity increase in the cortico-cerebellar functional network. We did not find any significant differences between the ASD and the TD group. CONCLUSIONS: This exploratory study uncovered increases in the theta band activity of participants with ASD during social interaction, pointing at the presence of neural interactions between the cerebellum and cerebral networks associated with social cognition. It also emphasizes the need for complementary functional connectivity measures to capture network-level alterations. Future work will focus on optimizing artifact correction to include more participants with TD and increase the statistical power of group-level contrasts.

Interactive Psychometrics for Autism With the Human Dynamic Clamp: Interpersonal Synchrony From Sensorimotor to Sociocognitive Domains.

The human dynamic clamp (HDC) is a human-machine interface designed on the basis of coordination dynamics for studying realistic social interaction under controlled and reproducible conditions. Here, we propose to probe the validity of the HDC as a psychometric instrument for quantifying social abilities in children with autism spectrum disorder (ASD) and neurotypical development. To study interpersonal synchrony with the HDC, we derived five standardized scores following a gradient from sensorimotor and motor to higher sociocognitive skills in a sample of 155 individuals (113 participants with ASD, 42 typically developing participants; aged 5 to 25 years; IQ > 70). Regression analyses were performed using normative modeling on global scores according to four subconditions (HDC behavior "cooperative/competitive," human task "in-phase/anti-phase," diagnosis, and age at inclusion). Children with ASD had lower scores than controls for motor skills. HDC motor coordination scores were the best candidates for stratification and diagnostic biomarkers according to exploratory analyses of hierarchical clustering and multivariate classification. Independently of phenotype, sociocognitive skills increased with developmental age while being affected by the ongoing task and HDC behavior. Weaker performance in ASD for motor skills suggests the convergent validity of the HDC for evaluating social interaction. Results provided additional evidence of a relationship between sensorimotor and sociocognitive skills. HDC may also be used as a marker of maturation of sociocognitive skills during real-time social interaction. Through its standardized and objective evaluation, the HDC not only represents a valid paradigm for the study of interpersonal synchrony but also offers a promising, clinically relevant psychometric instrument for the evaluation and stratification of sociomotor dysfunctions.