Pubmed du 21/06/21
1. Cantiani C, Riva V, Dondena C, Riboldi EM, Lorusso ML, Molteni M. Detection without further processing or processing without automatic detection? Differential ERP responses to lexical-semantic processing in toddlers at high clinical risk for autism and language disorder. Cortex; a journal devoted to the study of the nervous system and behavior. 2021; 141: 465-81.
Delays in early expressive vocabulary can reflect a specific delay in language acquisition or more general impairments in social communication. The neural mechanisms underlying the (dis)ability to establish the first lexical-semantic representations remain relatively unknown. Here, we investigate the electrophysiological underpinnings of these mechanisms during the critical phase of lexical acquisition in two groups of 19-month-old toddlers at risk for neurodevelopmental disorders, i.e., children characterized by low expressive vocabulary (late talkers, N = 18) and children with early signs of Autism Spectrum Disorder (ASD, N = 18) as compared to typically developing children (N = 28), with the aim to identify similarities and specificities in lexical-semantic processing between these groups. ERPs elicited by words (either congruous or incongruous with the previous picture context) and pseudo-words are investigated within a picture-word matching paradigm. In order to further interpret ERP responses, we look at longitudinal intra-group associations with language and socio-communications skills at age 24 months. As expected, we found differences between the groups that might underlie specificities, but also similarities. On the one side, late talkers differed from the other two groups in the early component (phonological-lexical priming effect) reflecting detection of the correspondence between the heard word and the lexical representation pre-activated by the picture. On the other side, children with early symptoms of ASD differed from the other two groups in the late component (late positive component) reflecting the effortful semantic re-analysis following a violation. The functional interpretation of the two components is corroborated by significant correlations suggesting that the early component is associated with later socio-communication skills, whereas the late component is associated with linguistic skills. Results point in the direction of differential impaired mechanisms in the two populations, i.e., impaired automatic detection of incongruencies in late talkers vs. absence of high-level re-analysis of such incongruencies in children with early signs of ASD.
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2. Crimi A, Dodero L, Sambataro F, Murino V, Sona D. Structurally constrained effective brain connectivity. NeuroImage. 2021; 239: 118288.
The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of effective connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal. The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task.
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3. Panjwani AA, Bailey RL, Kelleher BL. COVID-19 and behaviors in children with autism spectrum disorder: Disparities by income and food security status. Research in developmental disabilities. 2021; 115: 104002.
BACKGROUND: Research on the impact of the COVID-19 pandemic on behaviors of children with autism spectrum disorder (ASD) is lacking. AIMS: This study investigates the relationship between COVID-19 and behaviors of children with ASD living in the United States. METHODS AND PROCEDURES: Parents and caregivers (n = 200) across the United States, as proxies for children 2-17 years of age with ASD, participated in an online survey querying changes in overall behavior and 15 specific behaviors during the COVID-19 pandemic. Logistic regression was used to assess the association of a moderate-to-large impact on the child’s overall behavior with household income level and food security status. OUTCOMES AND RESULTS: A majority of respondents reported a moderate-to-large impact on the child’s overall behavior (74 %) due to COVID-19. Several specific behaviors were also affected. Stratifying by income level and food security status revealed disparities in the impact on overall behavior and most specific behaviors. Compared to a household income ≥$100 K, an income <$50 K was associated with an increased risk of moderate-to-large impact on the child's overall behavior (odds ratio (OR): 4.07, 95 % CI: 1.60, 10.38). Food insecurity also significantly impacted this risk, even after adjusting for potential confounding factors (OR: 3.31, 95 % CI: 1.13, 9.66). CONCLUSIONS AND IMPLICATIONS: Our findings show a large proportion of caregivers reporting moderate-to-large changes post-COVID-19 in the behaviors of U.S. children with ASD, particularly in families with low income and/or food insecurity. This study highlights the effects of existing disparities on children with ASD and their families during this unprecedented time.
