Abstract

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.

Highlights

  • Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct definitions and diagnostic criteria

  • ASD children displayed an overall increase of total grey matter volume in comparison with typical development (TD), distributed in the fronto-temporal regions

  • Grey matter volume increase is one of the most consistent structural findings in ASD, and it is striking in younger children [20], supporting the early brain overgrowth of ASD related to abnormal cortical development and expansion [38,39]

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Summary

Introduction

Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct definitions and diagnostic criteria. ASD/CAS association is supported by a possible shared genetic basis only few syndromes or genes have been currently identified such as the 16p11.2 deletion syndrome [12] and the CNTNAP2 gene deletion on 7q35 position The latter encodes a ‘neurexin’ protein that is associated with several neurodevelopmental disorders, including speech and language disorders [13] and autism [14, 15]. There has been a growing interest in the identification of shared brain abnormalities across psychiatric and neurodevelopmental disorders, especially among the disorders that frequently overlap in phenotypic presentation [29] These studies enlarge our understanding of whether symptoms’ overlap between some brain disorders could be at least partly explained by common altered neuroanatomy, or vice-versa, is subtended by disorder-specific brain underpinnings. (3) To evaluate the predictive power of machine-learning analysis in differentiating these three young populations (ASD, CAS, TD)

Participants and MRI Data Acquisition
MRI Acquisition and Processing
FreeSurfer Processing and Feature Extraction
Statistical Analysis
Comparison between CAS and TD
Comparison between ASD and CAS
Machine Learning Analysis
Discussion
ASD Versus TD
CAS Versus TD
Is Machine Learning Informative about Diagnosis Prediction?
Final Considerations
Findings
Strenghts and Weaknesses of the Study
Full Text
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