Autism Spectrum Disorder and Childhood Apraxia of Speech: Early Language-Related Hallmarks across Structural MRI Study

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.

Neural substrates of neuropsychological profiles in dystrophynopathies: A pilot study of diffusion tractography imaging

“…MRI analysis. Brain tissue segmentation was performed using FreeSurfer based on 3D T1-weighted images as in [54]. FreeSurfer is used as pre-processing workflow for structural MRI data to perform volumetric segmentation and cortical reconstruction through 31 processing steps [55].…”

MCP-1 Signaling Disrupts Social Behavior by Modulating Brain Volumetric Changes and Microglia Morphology

“…We identified that systemic injection of MCP-1-neutralizing Ab promotes a robust volume increased in the SSP and a prominent volume decreased in the CbX. ASD patients and preclinical models of ASD-like behavior exhibit a reduction of MRI-based volume in SSP [3], increased cerebellum volume [4,5], and in uncinate fasciculus overgrowth [6], a region associated with impaired socio-emotional processing. Molecular traits setting volumetric changes in brain have not been totally documented; a recent proposal suggests that systemic proinflammatory profile associates with volume reduction of cortical gray and white matter [56], and volume reduction in the lower parietal lobe in humans [57].…”

“…Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by the inability to process and respond to external stimuli, atrophied verbal and non-verbal communication, disinterest in socialization, and stereotypic behaviors [1,2]. ASD subjects integrate structural abnormalities in critical regions of the central nervous system (CNS) including total volume reduction on primary somatosensory cortex [3], and an increase in cerebellum volume [4,5]. Studies employing diffusion tensor imaging (DTI)-based tractography found that individuals with ASD exhibit a uncinate fasciculus overgrowth [6], volume reduction in corpus callosum [7], as well as a decreased volume in lateral and medial orbitofrontal surface area, isthmus cingulate thickness, and an increased volume in the putamen [8].…”

“…Studies employing diffusion tensor imaging (DTI)-based tractography found that individuals with ASD exhibit a uncinate fasciculus overgrowth [6], volume reduction in corpus callosum [7], as well as a decreased volume in lateral and medial orbitofrontal surface area, isthmus cingulate thickness, and an increased volume in the putamen [8]. Notably, structural and functional alterations on primary somatosensory cortex (SSP), cerebellum, frontal cortex, and inferior parietal lobule have been reported to regulate social behavior, socioemotional processing and affective functions in humans and preclinical models [3][4][5][9][10][11][12].…”

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging

“…Additionally, Xiao et al (2017) employed a random forest classifier and utilized the regional average cortical thickness feature for ASD prediction. Similarly, Conti et al (2020) constructed a linear‐kernel SVM to explore possible ASD‐specific brain structural features, concluding that a set of cortical thickness features can achieve the best predictive performance. Previous studies indicate that autistic subjects show volume differences compared with typically developing subjects, such as smaller cerebellum volume (Rogers et al, 2013; Stoodley, 2014), larger amygdala volume (Nordahl et al, 2020), or larger hippocampus volume (Xu et al, 2020).…”