Characterization Of Autism Spectrum Disorder Research Articles

A Systematic Review on Autism and Hyperserotonemia: State-of-the-Art, Limitations, and Future Directions.

Hyperserotonemia is one of the most studied endophenotypes in autism spectrum disorder (ASD), but there are still no unequivocal results about its causes or biological and behavioral outcomes. This systematic review summarizes the studies investigating the relationship between blood serotonin (5-HT) levels and ASD, comparing diagnostic tools, analytical methods, and clinical outcomes. A literature search on peripheral 5-HT levels and ASD was conducted. In total, 1104 publications were screened, of which 113 entered the present systematic review. Of these, 59 articles reported hyperserotonemia in subjects with ASD, and 26 presented correlations between 5-HT levels and ASD-core clinical outcomes. The 5-HT levels are increased in about half, and correlations between hyperserotonemia and clinical outcomes are detected in a quarter of the studies. The present research highlights a large amount of heterogeneity in this field, ranging from the characterization of ASD and control groups to diagnostic and clinical assessments, from blood sampling procedures to analytical methods, allowing us to delineate critical topics for future studies.

Automated Characterization of Autism Spectrum Disorder using Combined Functional and Structural MRI Analysis

Automated Characterization of Autism Spectrum Disorder using Combined Functional and Structural MRI Analysis

Characterization of Autism Spectrum Disorder (ASD) subtypes based on the relationship between motor skills and social communication abilities.

Motor abnormalities are generally observed in autism spectrum disorder (ASD), and motor difficulties are certainly evident during the early years of life and may thus precede social-communication impairments. The main aim of the present study was to examine ASD subtypes based on the relationship between motor skills and social communication abilities. Motor skills and social communication abilities were evaluated through the Movement Assessment Battery for Children-Second Edition, the Autism Diagnostic Observation Schedule-Second Version and the Psychoeducational Profile-Third Edition. In addition, social communication abilities were classified according to the Autism Classification System of Functioning: Social Communication-ACSF:SC criteria. We found that children with ASD presented poorer motor skills than their TD peers, and motor impairments correlated with poorer social communication abilities in children with ASD. In addition, children with lower social and communication functioning showed a more prominent impairment in manual dexterity and fine motor skills than children with better social and communication functioning. In conclusion, we suggest that stratifying children with ASD based on motor and social endophenotypes may be useful to understand the neurobiological mechanisms of ASD and lead to new types of treatment.

Characterization of Autism Spectrum Disorder across the Age Span by Intrinsic Network Patterns.

Autism spectrum disorder (ASD) is characterized by abnormal functional organization of brain networks, which may underlie the cognitive and social impairments observed in affected individuals. The present study characterizes unique intrinsic connectivity within- and between- neural networks in children through to adults with ASD, relative to controls. Resting state fMRI data were analyzed in 204 subjects, 102 with ASD and 102 age- and sex-matched controls (ages 7-40years), acquired on a single scanner. ASD was assessed using the autism diagnostic observation schedule (ADOS). BOLD correlations were calculated between 47 regions of interest, spanning seven resting state brain networks. Partial least squares (PLS) analyses evaluated the association between connectivity patterns and ASD diagnosis as well as ASD severity scores. PLS demonstrated dissociable connectivity patterns in those with ASD, relative to controls. Similar patterns were observed in the whole cohort and in a subgroup analysis of subjects under 18years of age. Greater inter-network connectivity was seen in ASD with greater intra-network connectivity in controls. In conclusion, stronger inter-network and weaker intra-network resting state-fMRI BOLD correlations characterize ASD and may differentiate control and ASD cohorts. These findings are relevant to understanding ASD as a disruption of network topology.

Characterization of autism spectrum disorder and neurodevelopmental profiles in youth with XYY syndrome

BackgroundXYY syndrome is a sex chromosome aneuploidy that occurs in ~ 1/850 male births and is associated with increased risk for neurodevelopmental difficulties. However, the profile of neurodevelopmental impairments, including symptoms of autism spectrum disorder (ASD) in XYY remains poorly understood. This gap in knowledge has persisted in part due to lack of access to patient cohorts with dense and homogeneous phenotypic data.MethodsWe evaluated a single-center cohort of 64 individuals with XYY aged 5–25 years, using a standardized battery of cognitive and behavioral assessments spanning developmental milestones, IQ, adaptive behavior, academic achievement, behavioral problems, and gold-standard diagnostic instruments for ASD. Our goals were to (i) detail the neurodevelopmental profile of XYY with a focus on ASD diagnostic rates and symptom profiles, (ii) screen phenotypes for potential ascertainment bias effects by contrasting pre- vs. postnatally diagnosed XYY subgroups, and (iii) define major modules of phenotypic variation using graph-theoretical analysis.ResultsAlthough there was marked inter-individual variability, the average profile was characterized by some degree of developmental delay, and decreased IQ and adaptive behavior. Impairments were most pronounced for language and socio-communicative functioning. The rate of ASD was 14%, and these individuals exhibited autism symptom profiles resembling those observed in ASD without XYY. Most neurodevelopmental dimensions showed milder impairment among pre- vs. postnatally diagnosed individuals, with clinically meaningful differences in verbal IQ. Feature network analysis revealed three reliably separable modules comprising (i) cognition and academic achievement, (ii) broad domain psychopathology and adaptive behavior, and (iii) ASD-related features.ConclusionsBy adding granularity to our understanding of neurodevelopmental difficulties in XYY, these findings assist targeted clinical assessment of newly identified cases, motivate greater provision of specialized multidisciplinary support, and inform future efforts to integrate behavioral phenotypes in XYY with neurobiology.Trial registrationsClinicalTrials.gov NCT00001246, “89-M-0006: Brain Imaging of Childhood Onset Psychiatric Disorders, Endocrine Disorders and Healthy Controls.”

Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder

BackgroundEmerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4–15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10–24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects.ResultsPreliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p < 0.05 following Holm–Bonferroni correction. Features derived from hippocampal regions also demonstrate high discriminative power for differentiating between ASD and DC subjects, with classifier accuracy of 67.85%, sensitivity of 62.50%, specificity of 71.42%, and the area under the ROC curve (AUC) of 76.80% for age-matched subjects with typical age range.ConclusionsResults demonstrate the potential of hippocampal texture features as a biomarker for the diagnosis and characterization of ASD.

Characterization of autism spectrum disorder with spontaneous hemodynamic activity.

Functional near-infrared spectroscopy (fNIRS) was used to investigate spontaneous hemodynamic activity in the temporal cortex for typically developing (TD) children and children with autism spectrum disorder (ASD). Forty-seven children participated in the experiments including twenty-five with ASD. Compared with TD children, children with ASD showed weaker bilateral resting-state functional connectivity (RSFC), but much stronger fluctuation magnitude in terms of oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb). Differentiating between ASD and TD based on a support vector machine (SVM) model including bilateral RSFC and the fluctuation power of HbO2 and Hb as variables could achieve high accurate classification with sensitivity of 81.6% and specificity of 94.6%. This study demonstrates optical brain imaging has the potential for screening children with risk of ASD.

Social neuroscience in psychiatry: pathways to discovering neurobiological risk and resilience.

Cacioppo et al 1 provide a thoughtful overview of the ways in which social neuroscience can significantly advance our understanding and treatment of mental disorders. They touch on the way gene regulation, epigenetics and the environment can alter neurodevelopmental trajectories and in turn influence social behavior and social functioning. We would like to emphasize here the importance of future research exploring dynamic social brain changes taking place during adolescence 2,3 as a possible key to understanding the emergence of mental disorders at this critical stage of maturation, when risk for such disorders peaks. Recent work in our laboratory shows that attenuated growth of the hippocampus and attenuated reduction in putamen volume during age 12 to 16 years are associated with the onset of depression 4. Sex is also a significant factor, since exaggerated amygdala growth in females and attenuated growth in males seems to increase the risk of depression. Taking account of the neurodevelopmental background for boys and girls, relevant to all aspects of cognition, including social cognition, is a necessary prerequisite to understanding mental disorders and their neurobiology. The prominence of social themes in the characterization of autism spectrum disorders (ASD) also warrants brief discussion. Our recent work on ASD 5 is relevant in being an interdisciplinary study involving biology and engineering, and in linking statistical approaches to biology. Importantly, while the emphasis of most studies has been on discovery of neurobiological risk markers for such disorders, in our study we also identified several single-nucleotide polymorphisms (SNPs) that protected against ASD, that is, might be associated with resilience to development of the disorder. For example, we found that the SNP rs12317962 protected against ASD 5. This SNP lies in the gene KCNMB4, encoding a potassium channel involved in neuronal excitability, which is highly expressed in the fusiform gyrus and key social brain regions, namely the superior temporal, cingulate and orbitofrontal cortices. Other SNPs, such as variation in rs3796863 in CD38, a gene linked to ASD and known to be involved in oxytocin secretion, has also been linked with activation of the amygdala and in particular the fusiform gyrus, during visual processing of social stimuli in healthy young men 6. We are in the processes of furthering this research in ASD by examining the influence of allelic variation on brain regions using neuroimaging. Schizophrenia is another disorder that involves deficits in social cognition. Recent theories propose that aberrations in dopaminergic and glutamatergic subcortical-amygdala-prefrontal circuits give rise to dysregulation of salience signaling, causing impairments in emotion-related perception, learning and memory 7, similar to the model of Phillips et al 8 described by Cacioppo et al 1 in the context of depression. Interdisciplinary research of Walter et al 9 shows that carriers for the psychosis risk variant of the SNP rs1344706 (gene ZNF804A) have abnormal neural activation in the medial prefrontal and left temporo-parietal cortex, as well as in regions of the mirror neuron system, during a theory of mind task. This potential intermediate phenotype derived from functional imaging may have implications for biological treatment of social cognitive impairments in schizophrenia. One thing that is clear from Cacioppo et al's paper 1 is that, across disorders, common social brain regions are dysfunctional: the amygdala, orbitofrontal cortex, medial prefrontal cortex, superior temporal sulcus, anterior insula and anterior cingulate. Yet, the way in which they deviate from normal functioning (hypo- vs. hyper-activation/mixture of both) depends on the illness being studied and the social processes in question. Therefore, we should study not only across disciplines but also across mental disorders, and in the context of carefully controlled treatment interventions, to provide insights into both risk and resilience factors associated with developing particular disorders. Oxytocin has been proposed as a potential adjunctive treatment for the social cognitive and behavioural deficits common in social anxiety disorder, ASD, schizophrenia, and borderline personality disorder 10. While there are several oxytocin-related risk alleles that have been linked to social brain functioning, there is little insight into the mechanisms of the actions of oxytocin in the brain. This is a promising avenue for future research in psychiatry. In conclusion, Cacioppo et al's paper on social neuroscience and mental disorders provides us much food for thought. There is a need for dynamic interdisciplinary (rather than just multidisciplinary) exchange between biological and other sciences; assessing changes in trajectories of brain structure and function; linking these dynamic changes to genes that may bestow risk or resilience to development of illness; and examining the impact of interventions that modulate social cognition. All of these approaches and their combination present exciting ways forward in understanding some of the most challenging and complex disorders affecting human beings.

Towards a molecular characterization of autism spectrum disorders: an exome sequencing and systems approach.

The hypothetical ‘AXAS' gene network model that profiles functional patterns of heterogeneous DNA variants overrepresented in autism spectrum disorder (ASD), X-linked intellectual disability, attention deficit and hyperactivity disorder and schizophrenia was used in this current study to analyze whole exome sequencing data from an Australian ASD cohort. An optimized DNA variant filtering pipeline was used to identify loss-of-function DNA variations. Inherited variants from parents with a broader autism phenotype and de novo variants were found to be significantly associated with ASD. Gene ontology analysis revealed that putative rare causal variants cluster in key neurobiological processes and are overrepresented in functions involving neuronal development, signal transduction and synapse development including the neurexin trans-synaptic complex. We also show how a complex gene network model can be used to fine map combinations of inherited and de novo variations in families with ASD that converge in the L1CAM pathway. Our results provide an important step forward in the molecular characterization of ASD with potential for developing a tool to analyze the pathogenesis of individual affected families.

The need for a comprehensive molecular characterization of autism spectrum disorders

Autism spectrum disorders (ASD) are a heterogeneous group of disorders which have complex behavioural phenotypes. Although ASD is a highly heritable neuropsychiatric disorder, genetic research alone has not provided a profound understanding of the underlying causes. Recent developments using biochemical tools such as transcriptomics, proteomics and cellular models, will pave the way to gain new insights into the underlying pathological pathways. This review addresses the state-of-the-art in the search for molecular biomarkers for ASD. In particular, the most important findings in the biochemical field are highlighted and the need for establishing streamlined interaction between behavioural studies, genetics and proteomics is stressed. Eventually, these approaches will lead to suitable translational ASD models and, therefore, a better disease understanding which may facilitate novel drug discovery efforts in this challenging field.

Economic probes of mental function and the extraction of computational phenotypes

Economic games are now routinely used to characterize human cognition across multiple dimensions. These games allow for effective computational modeling of mental function because they typically come equipped with notions of optimal play, which provide quantitatively prescribed target functions that can be tracked throughout an experiment. The combination of these games, computational models, and neuroimaging tools open up the possibility for new ways to characterize normal cognition and associated brain function. We propose that these tools may also be used to characterize mental dysfunction, such as that found in a range of psychiatric illnesses. We describe early efforts using a multi-round trust game to probe brain responses associated with healthy social exchange and review how this game has provided a novel and useful characterization of autism spectrum disorder. Lastly, we use the multi-round trust game as an example to discuss how these kinds of games could produce novel bases for representing healthy behavior and brain function and thus provide objectively identifiable subtypes within a broad spectrum of mental function.