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Piloting a battery to evaluate parasympathetic reactivity and externalizing behaviours during early childhood in autism spectrum disorder.
Infant Child Dev
April 2024
Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States.
Background: Respiratory Sinus Arrhythmia Reactivity (RSA-R) correlates both positively and negatively with externalizing behavior in autistic individuals. These inconsistencies may result from task-based differences. This pilot study measured RSA-R in 4-to 6-year-olds, across two time-points, using four validated tasks with matched baseline and challenge periods.
View Article and Find Full Text PDFDaily oscillation of the excitation/inhibition ratio is disrupted in two mouse models of autism.
iScience
January 2025
Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA.
Alterations to the excitation/inhibition (E/I) ratio are postulated to underlie behavioral phenotypes in autism spectrum disorder (ASD) patients and mouse models. However, in wild type mice the E/I ratio is not constant, but instead oscillates across the 24-h day. Therefore, we tested whether E/I regulation, rather than the overall E/I ratio, is disrupted in two ASD-related mouse lines: KO and BTBR, models of syndromic and idiopathic ASD, respectively.
View Article and Find Full Text PDFInfluential articles in autism and gut microbiota: bibliometric profile and research trends.
Front Microbiol
January 2025
Research Division, Institute of Mental Health, Singapore, Singapore.
Objective: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Increasing evidence suggests that it is potentially related to gut microbiota, but no prior bibliometric analysis has been performed to explore the most influential works in the relationships between ASD and gut microbiota. In this study, we conducted an in-depth analysis of the most-cited articles in this field, aiming to provide insights to the existing body of research and guide future directions.
View Article and Find Full Text PDFProfile and development of adaptive behavior in adults with autism spectrum disorder and severe intellectual disability.
Front Psychiatry
January 2025
Laboratoire Lorrain de Psychologie et Neurosciences de la Dynamique des Comportements, Université de Lorraine, Nancy, Lorraine, France.
Background: This study examined the profiles of adaptive behavior development in adults with autism spectrum disorder (ASD) and severe intellectual disability (ID), and the relationships between the levels of the different domains and subdomains of adaptive development and the intensity of autistic symptomatology.
Participants: This study involved 71 adults (44 men and 27 women with average ages of 39 years 7 months and 36 years 2 months, respectively) living in medico-social institutions and having a level of adaptive development corresponding to age below 3 years 4 months and a level of cognitive development corresponding to ages between 12 and 24 months.
Methods: ASD was diagnosed using Pervasive Development Disorder-Mental Retardation Scale (PDD-MRS) and Childhood Autistic Rating Scale (CARS), ID and its severity were determined based on the Diagnostic Statistical Manual-5 (DSM-5) criteria, and the very low cognitive developmental level was assessed using the Socio-emotional Cognitive Evaluation Battery (Adrien, Pearson-ECPA, 2007), adapted for adults (SCEB-A).
Identification of Bacterial Lipopolysaccharide-Associated Genes and Molecular Subtypes in Autism Spectrum Disorder.
Pharmgenomics Pers Med
January 2025
Department of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by diverse symptoms affecting social interaction, communication, and behavior. This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder.
Methods: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms.
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