Toward differential diagnosis of autism spectrum disorder using multimodal behavior descriptors and executive functions

Chen, Chin-Po; Gau, Susan Shur‐Fen; Lee, Chi-Chun

doi:10.1016/j.csl.2018.12.003

Cited by 16 publications

(6 citation statements)

References 43 publications

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“…The severity of autistic features as well as the incidence of comorbid illnesses, which include intellectual disability, anxiety, epilepsy, and gastrointestinal disorders, greatly differ among individuals with autism [3][4][5][6]. ASD is currently diagnosed by observing common autistic behaviors in children [7]. Although expert clinicians can diagnose autism in children as young as 24 months the average age at which autism is diagnosed is still considerably high and may reach that of four years [8].…”

Section: Introductionmentioning

confidence: 99%

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder

et al. 2020

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BackgroundAutism spectrum disorder (ASD) is a complex group of heterogeneous neurodevelopmental disorders the prevalence of which has been in the rise in the past decade. In an attempt to better target the basic causes of ASD for diagnosis and treatment, efforts to identify reliable biomarkers related to the body's metabolism are increasing. Despite an increase in identifying biomarkers in ASD, there are none so far with enough evidence to be used in routine clinical examination, unless medical illness is suspected. Promising biomarkers include those of mitochondrial dysfunction, oxidative stress, energy metabolism, and apoptosis. Methods and participantsSodium (Na+), Potassium (K+), glutathione (GSH), glutathione-s-transferase (GST), Creatine kinase (CK), lactate dehydrogenase (LDH), Coenzyme Q10, and melatonin (MLTN) were evaluated in 13 participants with ASD and 24 age-matched healthy controls. Additionally, five ratios, which include Na + /K + , GSH:GST, CK:Cas7, CoQ10: Cas 7, and Cas7: MLTN, were tested to measure their predictive values in discriminating between autistic individuals and controls. These markers, either in absolute values, as five ratios, or combined (9 markers + 5 ratios) were subjected to a principal component analysis and multidimensional scaling (MDS), and hierarchical clustering, which are helpful statistical tools in the field of biomarkers. OPEN ACCESSCitation: El-Ansary A, Hassan WM, Daghestani M, Al-Ayadhi L, Ben Bacha A (2020) Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder. PLoS ONE 15(1): e0227626. https://doi.org/10.

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Section: Introductionmentioning

confidence: 99%

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder

et al. 2020

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“…Eighty-six participants with a clinical diagnosis of ASD, made by child psychiatrists based on the DSM-5, and 39 TDC initially joined the study. The ASD diagnosis in all participants in the ASD group were further confirmed by the Autism Diagnostic Observation Schedule, ADOS [ 24 , 25 ] and Autism Diagnostic Interview-Revised, ADI-R [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

White matter microstructural and morphometric alterations in autism: implications for intellectual capabilities

Yeh

Tseng

et al. 2022

Molecular Autism

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Background Neuroimage literature of autism spectrum disorder (ASD) has a moderate-to-high risk of bias, partially because those combined with intellectual impairment (II) and/or minimally verbal (MV) status are generally ignored. We aimed to provide more comprehensive insights into white matter alterations of ASD, inclusive of individuals with II (ASD-II-Only) or MV expression (ASD-MV). Methods Sixty-five participants with ASD (ASD-Whole; 16.6 ± 5.9 years; comprising 34 intellectually able youth, ASD-IA, and 31 intellectually impaired youth, ASD-II, including 24 ASD-II-Only plus 7 ASD-MV) and 38 demographic-matched typically developing controls (TDC; 17.3 ± 5.6 years) were scanned in accelerated diffusion-weighted MRI. Fixel-based analysis was undertaken to investigate the categorical differences in fiber density (FD), fiber cross section (FC), and a combined index (FDC), and brain symptom/cognition associations. Results ASD-Whole had reduced FD in the anterior and posterior corpus callosum and left cerebellum Crus I, and smaller FDC in right cerebellum Crus II, compared to TDC. ASD-IA, relative to TDC, had no significant discrepancies, while ASD-II showed almost identical alterations to those from ASD-Whole vs. TDC. ASD-II-Only had greater FD/FDC in the isthmus splenium of callosum than ASD-MV. Autistic severity negatively correlated with FC in right Crus I. Nonverbal full-scale IQ positively correlated with FC/FDC in cerebellum VI. FD/FDC of the right dorsolateral prefrontal cortex showed a diagnosis-by-executive function interaction. Limitations We could not preclude the potential effects of age and sex from the ASD cohort, although statistical tests suggested that these factors were not influential. Our results could be confounded by variable psychiatric comorbidities and psychotropic medication uses in our ASD participants recruited from outpatient clinics, which is nevertheless closer to a real-world presentation of ASD. The outcomes related to ASD-MV were considered preliminaries due to the small sample size within this subgroup. Finally, our study design did not include intellectual impairment-only participants without ASD to disentangle the mixture of autistic and intellectual symptoms. Conclusions ASD-associated white matter alterations appear driven by individuals with II and potentially further by MV. Results suggest that changes in the corpus callosum and cerebellum are key for psychopathology and cognition associated with ASD. Our work highlights an essential to include understudied subpopulations on the spectrum in research.

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“…Eighty-six participants with a clinical diagnosis of ASD, made by child psychiatrists based on the DSM-5, and 39 TDC initially joined the study. The ASD diagnosis in all participants in the ASD group were further confirmed by the Autism Diagnostic Observation Schedule, ADOS [22, 23] and Autism Diagnostic Interview-Revised, ADI-R [24, 25].…”

Section: Methodsmentioning

confidence: 99%

White matter microstructural and morphometric alterations in autism: Implications for intellectual capabilities

Yeh

Tseng

et al. 2021

Preprint

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Background: Neuroimage literature of autism spectrum disorder (ASD) has a moderate-to-high risk of bias, partially because those combined with intellectual impairment (II) and/or minimally verbal (MV) status are generally ignored. We aimed to provide more comprehensive insights into white matter alterations of ASD, inclusive of individuals with II (ASD-II-Only) or MV expression (ASD-MV). Methods: Sixty-five participants with ASD (ASD-Whole; 16.6±5.9 years; comprising 34 intellectually able youth, ASD-IA, and 31 intellectually impaired youth, ASD-II, including 24 ASD-II-Only plus 7 ASD-MV) and 38 demographic-matched typically developing controls (TDC; 17.3±5.6 years) were scanned in accelerated diffusion-weighted MRI. Fixel-based analysis was undertaken to investigate the categorical differences in fiber density (FD), fiber cross-section (FC), and a combined index (FDC), and brain-symptom/cognition associations. Results: ASD-Whole had reduced FD in the anterior and posterior corpus callosum and left cerebellum Crus I, and smaller FDC in right cerebellum Crus II, compared to TDC. ASD-II, relative to TDC, showed almost identical alterations to those from ASD-Whole vs. TDC. ASD-II-Only had greater FD/FDC in the isthmus-splenium of callosum than ASD-MV. Autistic severity negatively correlated with FC in right Crus I. Non-verbal full-scale IQ positively correlated with FC/FDC in cerebellum VI. FD/FDC of the right dorsolateral prefrontal cortex showed a diagnosis-by-executive function interaction. Conclusions: ASD-associated white matter alterations appear driven by individuals combined with II and/or MV. Results suggest that changes in the corpus callosum and cerebellum are key for psychopathology and cognition associated with ASD. Our work highlights an essential to include understudied sub-populations on the spectrum in research.

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Toward differential diagnosis of autism spectrum disorder using multimodal behavior descriptors and executive functions

Cited by 16 publications

References 43 publications

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder

White matter microstructural and morphometric alterations in autism: implications for intellectual capabilities

White matter microstructural and morphometric alterations in autism: Implications for intellectual capabilities

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