ABSTRACT
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.
ABSTRACT
AIMS: Since the beginning of the COVID pandemic, studies reported an increase in children's mental health issues and questioned the impact of SARS-CoV-2 on psychiatric symptoms. METHODS: We compared COVID seroconversion in children hospitalized with acute, severe psychiatric symptoms (n = 52) with the sex- and age-matched control group (n = 52) living in the same low-income geographic area and sampled during the same time period. RESULTS: Contrary to our hypothesis, we observed less seroconverted children with psychiatric conditions 9.61% (95% CI, 3.59-21.80) vs 34.61% (95% CI, 22.33-49.16; χ2 = 14.7, P = 1.24E-4; OR = 0.20; 95% CI, 0.05-0.64). CONCLUSION: This suggests a lower direct impact of SARS-CoV-2 compared with the impact of mitigation strategies on psychiatric symptom deterioration in children reported since early stages of the pandemic.
Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Child , Humans , Pandemics , Risk Factors , SeroconversionABSTRACT
The bulk of social neuroscience takes a 'stimulus-brain' approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a 'brain-to-brain' approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, 'hyperscanning' setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such 'inter-brain connectivity analysis', resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses.
