Neuroscientist and adjunct professor, taking a sabbatical in toddler neurodevelopment
Following the discovery of new and more comprehensive biomarkers for autism spectrum disorder.
As autism is still diagnosed strictly through behavioral assessment, researchers are continuously mining for objective biomarkers.
Recent areas of promise include genetic, gene expression, metabolic, and neuroimaging measures.
The primary goal of biomarker identification is for diagnosing autism, or risk of autism, earlier than age two, which is the current threshold for behavioral assessment. Such biomarkers for autism could broaden the window for therapeutic interventions.
Evidence for metabolic indicators of autism, such as oxidative stress and methylation patterns, have been increasingly described in recent studies, and include an analysis by Howsmon et al. which identified on the sole basis of blood biomarkers and with over 95% accuracy children with autism and neurotypical children. However many of these studies, including Howsmon et al. were in children already of behavioral diagnostic age, and have yet to show predictive value through proof of principle in neonatal populations.
Neuroimaging studies have shown promise through analysis at the functional level, such as network efficiency patterns in neonatal infants, for autism predictive value. Other recent neuroimaging studies have identified predictive value in volumetric data such as neonatal brain overgrown patterns and white matter pathway densities.
It is important to keep in mind that different clusters of autism, potentially due to differing causes or differing manifestations, will have very separate groups of biomarkers. Efforts such as the Autism Biomarkers Consortium (ABC-CT) will help in further stratification of biomarkers.
Abstract: The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.
Pub.: 17 Mar '17, Pinned: 17 Apr '17
Abstract: The aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.We used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value ＜0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms.Hierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively.The results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.
Pub.: 01 Feb '17, Pinned: 17 Apr '17
Abstract: Autism Spectrum Disorder (ASD) is a developmental disorder defined by behavioural features that emerge during the first years of life. Research indicates that abnormalities in brain connectivity are associated with these behavioural features. However, inclusion of individuals past the age of onset of the defining behaviours complicates interpretation of the observed abnormalities: they may be cascade effects of earlier neuropathology and behavioural abnormalities. Our recent study of network efficiency in a cohort of 24-month-olds at high and low familial risk for ASD reduced this confound; we reported reduced network efficiencies in toddlers classified as ASD. The current study maps the emergence of these inefficiencies in the first year of life.
Pub.: 16 Mar '17, Pinned: 12 Apr '17
Abstract: Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6-12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.
Pub.: 17 Feb '17, Pinned: 12 Apr '17
Abstract: Restricted and repetitive behaviors are defining features of autism spectrum disorder (ASD). Under revised diagnostic criteria for ASD, this behavioral domain now includes atypical responses to sensory stimuli. To date, little is known about the neural circuitry underlying these features of ASD early in life.Longitudinal diffusion tensor imaging data were collected from 217 infants at high familial risk for ASD. Forty-four of these infants were diagnosed with ASD at age 2. Targeted cortical, cerebellar, and striatal white matter pathways were defined and measured at ages 6, 12, and 24 months. Dependent variables included the Repetitive Behavior Scale-Revised and the Sensory Experiences Questionnaire.Among children diagnosed with ASD, repetitive behaviors and sensory response patterns were strongly correlated, even when accounting for developmental level or social impairment. Longitudinal analyses indicated that the genu and cerebellar pathways were significantly associated with both repetitive behaviors and sensory responsiveness but not social deficits. At age 6 months, fractional anisotropy in the genu significantly predicted repetitive behaviors and sensory responsiveness at age 2. Cerebellar pathways significantly predicted later sensory responsiveness. Exploratory analyses suggested a possible disordinal interaction based on diagnostic status for the association between fractional anisotropy and repetitive behavior.Our findings suggest that restricted and repetitive behaviors contributing to a diagnosis of ASD at age 2 years are associated with structural properties of callosal and cerebellar white matter pathways measured during infancy and toddlerhood. We further identified that repetitive behaviors and unusual sensory response patterns co-occur and share common brain-behavior relationships. These results were strikingly specific given the absence of association between targeted pathways and social deficits.
Pub.: 21 Mar '17, Pinned: 12 Apr '17
Abstract: Exosomes can be used for detecting biomarkers for diagnostic, therapy-related or prognostic methods to identify phenotypes, such as a condition or disease, for example, the stage or progression of a disease. Cell-of-origin exosomes can be used in profiling of physiological states or determining phenotypes. Biomarkers or markers from cell-of-origin specific exosomes can be used to determine treatment regimens for diseases, conditions, disease stages, and stages of a condition, and can also be used to determine treatment efficacy. Markers from cell-of-origin specific exosomes can also be used to identify conditions of diseases of unknown origin.
Pub.: 22 Jul '10, Pinned: 12 Apr '17
Abstract: This invention provides methods and biomarkers for diagnosing autism by identifying cellular metabolites differentially produced in autistic patient samples versus non-autistic controls. Methods for identifying a unique profile of metabolites present of secreted in brain tissue, cerebrospinal fluid, plasma, or biofluids of autistic samples are described herein. The individual metabolites or a pattern of secreted metabolites provide metabolic signatures of autism, which can be used to provide a diagnosis thereof.
Pub.: 17 Jan '17, Pinned: 12 Apr '17
Abstract: Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA-mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.
Pub.: 22 Dec '16, Pinned: 12 Apr '17