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Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies.

ABSTRACT: Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD). Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation, connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and large-scale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.

SUBMITTER: Li D

PROVIDER: S-EPMC5360855 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies.

Li Dongyun D Karnath Hans-Otto HO Xu Xiu X

Neuroscience bulletin 20170310 2

Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD). Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation, connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers s ...[more]

PMID: 28283808

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Project description:Importance Sleep disorders are one of the most frequent comorbidities in children with autism spectrum disorder (ASD). However, the link between neurodevelopmental effects in ASD children with their underlying sleep microarchitecture is not well understood. An improved understanding of etiology of sleep difficulties and identification of sleep-associated biomarkers for children with ASD can improve the accuracy of clinical diagnosis. Objectives To investigate whether machine learning models can identify biomarkers for children with ASD based on sleep EEG recordings. Design, setting, and participants Sleep polysomnogram data were obtained from the Nationwide Children’ Health (NCH) Sleep DataBank. Children (ages: 8–16 yrs) with 149 autism and 197 age-matched controls without neurodevelopmental diagnosis were selected for analysis. An additional independent age-matched control group (n = 79) selected from the Childhood Adenotonsillectomy Trial (CHAT) was also used to validate the models. Furthermore, an independent smaller NCH cohort of younger infants and toddlers (age: 0.5–3 yr.; 38 autism and 75 controls) was used for additional validation. Main outcomes and measures We computed periodic and non-periodic characteristics from sleep EEG recordings: sleep stages, spectral power, sleep spindle characteristics, and aperiodic signals. Machine learning models including the Logistic Regression (LR) classifier, Support Vector Machine (SVM), and Random Forest (RF) model were trained using these features. We determined the autism class based on the prediction score of the classifier. The area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. Results In the NCH study, RF outperformed two other models with a 10-fold cross-validated median AUC of 0.95 (interquartile range [IQR], [0.93, 0.98]). The LR and SVM models performed comparably across multiple metrics, with median AUC 0.80 [0.78, 0.85] and 0.83 [0.79, 0.87], respectively. In the CHAT study, three tested models have comparable AUC results: LR: 0.83 [0.76, 0.92], SVM: 0.87 [0.75, 1.00], and RF: 0.85 [0.75, 1.00]. Sleep spindle density, amplitude, spindle-slow oscillation (SSO) coupling, aperiodic signal’s spectral slope and intercept, as well as the percentage of REM sleep were found to be key discriminative features in the predictive models. Conclusion and relevance Our results suggest that integration of EEG feature engineering and machine learning can identify sleep-based biomarkers for ASD children and produce good generalization in independent validation datasets. Microstructural EEG alterations may help reveal underlying pathophysiological mechanisms of autism that alter sleep quality and behaviors. Machine learning analysis may reveal new insight into the etiology and treatment of sleep difficulties in autism.

Project description:BackgroundEarly identification and treatment of individuals with autism spectrum disorder (ASD) improves outcomes, but specific evidence needed to individualize treatment recommendations is lacking. Biomarkers that could be routinely measured within the clinical setting could potentially transform clinical care for patients with ASD. This demonstration project employed collection of biomarker data during regular autism specialty clinical visits and explored the relationship of biomarkers with clinical ASD symptoms.MethodsEighty-three children with ASD, aged 5-10 years, completed a multi-site feasibility study integrating the collection of biochemical (blood serotonin, urine melatonin sulfate excretion) and clinical (head circumference, dysmorphology exam, digit ratio, cognitive and behavioral function) biomarkers during routine ASD clinic visits. Parents completed a demographic survey and the Aberrant Behavior Checklist-Community. Cognitive function was determined by record review. Data analysis utilized Wilcoxon two-sample tests and Spearman correlations.ResultsParticipants were 82% male, 63% White, 19% Hispanic, with a broad range of functioning. Group means indicated hyperserotonemia. In a single regression analysis adjusting for race and median household income, higher income was associated with higher levels of blood serotonin and urine melatonin sulfate excretion levels (p = 0.004 and p = 0.04, respectively). Melatonin correlated negatively with age (p = 0.048) and reported neurologic problems (p = 0.02). Dysmorphic status correlated with higher reported stereotyped behavior (p = 0.02) and inappropriate speech (p = 0.04).ConclusionThis demonstration project employed collection of multiple biomarkers, allowed for examination of associations between biochemical and clinical measures, and identified several findings that suggest direction for future studies. This clinical research model has promise for integrative biomarker research in individuals with complex, heterogeneous neurodevelopmental disorders such as ASD.

Dataset Information

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies.

Publications

Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies.

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