Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct diagnostic criteria and different epidemiology. However, a common genetic background as well as overlapping clinical features between ASD and CAS have been recently reported. To date, brain structural language-related abnormalities have been detected in both the conditions, but no study directly compared young children with ASD, CAS and typical development (TD). In the current work, we aim: (i) to test the hypothesis that ASD and CAS display neurostructural differences in comparison with TD through morphometric Magnetic Resonance Imaging (MRI)-based measures (ASD vs. TD and CAS vs. TD); (ii) to investigate early possible disease-specific brain structural patterns in the two clinical groups (ASD vs. CAS); (iii) to evaluate predictive power of machine-learning (ML) techniques in differentiating the three samples (ASD, CAS, TD). We retrospectively analyzed the T1-weighted brain MRI scans of 68 children (age range: 34-74 months) grouped into three cohorts: (1) 26 children with ASD (mean age ± standard deviation: 56 ± 11 months); (2) 24 children with CAS (57 ± 10 months); (3) 18 children with TD (55 ± 13 months). Furthermore, a ML analysis based on a linear-kernel Support Vector Machine (SVM) was performed. All but one brain structures displayed significant higher volumes in both ASD and CAS children than TD peers. Specifically, ASD alterations involved fronto-temporal regions together with basal ganglia and cerebellum, while CAS alterations are more focused and shifted to frontal regions, suggesting a possible speech-related anomalies distribution. Caudate, superior temporal and hippocampus volumes directly distinguished the two conditions in terms of greater values in ASD compared to CAS. The ML analysis identified significant differences in brain features between ASD and TD children, whereas only some trends in the ML classification capability were detected in CAS as compared to TD peers. Similarly, the MRI structural underpinnings of two clinical groups were not significantly different when evaluated with linear-kernel SVM. Our results may represent the first step towards understanding shared and specific neural substrate in ASD and CAS conditions, which subsequently may contribute to early differential diagnosis and tailoring specific early intervention.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768516PMC
http://dx.doi.org/10.3390/jpm10040275DOI Listing

Publication Analysis

Top Keywords

asd cas
32
asd
13
cas
13
autism spectrum
8
spectrum disorder
8
childhood apraxia
8
apraxia speech
8
features asd
8
brain structural
8
children asd
8

Similar Publications

β-Lactams are the most widely used antibiotics for the treatment of bacterial infections because of their proven track record of safety and efficacy. However, susceptibility to β-lactam antibiotics is continually eroded by resistance mechanisms. Emerging multidrug-resistant (MDR) strains possessing altered alleles (encoding PBP2) pose a global health emergency as they threaten the utility of ceftriaxone, the last remaining outpatient antibiotic.

View Article and Find Full Text PDF

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment.

J Vis Exp

December 2024

CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences; Department of Psychology, University of Chinese Academy of Sciences;

The Acute Stress Disorder Scale (ASDS) is crucial for assessing acute stress disorder (ASD), especially in high-stress environments like Intensive Care Units (ICUs). Traditional methods struggle to interpret all 19 ASDS variables simultaneously. This study introduces a novel polar histogram visualization approach to enhance ASDS score analysis, focusing on elderly ICU caregivers.

View Article and Find Full Text PDF

Establishment of a standardized daily behavior collection and analysis system for brain disease models of rhesus and cynomolgus monkeys and its application in autism spectrum disorder.

J Zhejiang Univ Sci B

November 2024

National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Key Laboratory of Genetic Evolution & Animal Models, National Resource Center for Non-human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650107, China.

Article Synopsis
  • Complex brain diseases pose significant health risks, and there's a need for early diagnostic tools and treatments, which human research limitations hinder.
  • To overcome ethical research barriers, transgenic monkey models, particularly for autism spectrum disorder (ASD), have been created to study these diseases.
  • A standardized system for tracking and analyzing daily behaviors of these monkey models has been developed, enabling quantitative assessment of ASD symptoms through a new specific behavior ethogram.
View Article and Find Full Text PDF
Article Synopsis
  • * In a Phase II clinical trial, 27 patients received entinostat followed by nivolumab, resulting in an objective response rate of 11% and a median response duration of over 10 months, although the primary endpoint for overall effectiveness was not reached.
  • * The combination treatment led to significant immune profile changes, including increased dendritic cell activity and enhanced inflammatory response, suggesting potential for improving treatment strategies in PDA despite
View Article and Find Full Text PDF

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that is significantly increasing, resulting in severe distress. The approved treatment for ASD only partially improves the sympoms, but it does not entirely reverse the symptoms. Developing novel disease-modifying drugs is essential for the continuous improvement of ASD.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!