Modeling the autism spectrum disorder phenotype

Executive Functions in Children and Adolescents with Autism Spectrum Disorder in Family and School Environment

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by the presence of difficulties in communication and social interaction, often associated with deficits in executive functions (EF). The EF correct development is related to a more effective functioning in all its daily activities, while being associated with more efficient social relations. The objective of this research is to analyze the level of development of EF in children and adolescents with ASD in school and at home. This is a descriptive, cross-sectional, and multicenter study with 102 participants selected by non-probabilistic sampling, 32 parents of children with ASD, and 70 professionals in the field of education of students with ASD. The study confirms that although children and adolescents with ASD have problems in executive functioning, the perception of informants, parents, and education professionals is similar but not the same in the different contexts: school and home.

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives.

METHODS

To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method.

RESULTS

Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders.

CONCLUSION

Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.

Molecular Mechanisms of Aberrant Neuroplasticity in Autism Spectrum Disorders (Review)

This review presents the analysis and systematization of modern data on the molecular mechanisms of autism spectrum disorders (ASD) development. Polyetiology and the multifactorial nature of ASD have been proved. The attempt has been made to jointly review and systematize current hypotheses of ASD pathogenesis at the molecular level from the standpoint of aberrant brain plasticity. The mechanism of glutamate excitotoxicity formation, the effect of imbalance of neuroactive amino acids and their derivatives, neurotransmitters, and hormones on the ASD formation have been considered in detail. The strengths and weaknesses of the proposed hypotheses have been analyzed from the standpoint of evidence-based medicine. The conclusion has been drawn on the leading role of glutamate excitotoxicity as a biochemical mechanism of aberrant neuroplasticity accompanied by oxidative stress and mitochondrial dysfunction. The mechanism of aberrant neuroplasticity has also been traced at the critical moments of the nervous system development taking into account the influence of various factors of the internal and external environment. New approaches to searching for ASD molecular markers have been considered.

Text mining of gene-phenotype associations reveals new phenotypic profiles of autism-associated genes

Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene-phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene-phenotype associations which are mainly obtained from genetic experiments. However, a large proportion of gene-phenotype associations are still buried in the autism-related literature and there are limited resources to investigate autism-associated gene-phenotype associations. Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods. We have generated a comprehensive database of gene-phenotype associations in the last five years' autism-related literature that can be easily updated as new literature becomes available. We have evaluated our pipeline through several different approaches, and we are able to rank and select top autism-associated genes through their unique and wide spectrum of phenotypic profiles, which could provide a unique resource for the diagnosis and treatment of autism. The data resources and the Autism_genpheno pipeline are available at: https://github.com/maiziezhoulab/Autism_genepheno .

Executive Functioning: A Mediator Between Sensory Processing and Behaviour in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction, executive functioning, sensory-perceptual abilities and behaviour, such as anxious/depressed states, attention problems, aggression, or somatic complains. However, the dynamic relationship between these dimensions remains to be addressed. Therefore, we explored the link between executive functions, sensory processing and behaviour in 79 children and adolescents with ASD. Results showed significant associations between all dimensions-executive functions, sensory processing and behaviour. Furthermore, using structural equation modelling methods, we observed a mediation effect of executive functioning, specifically the domain pertaining to emotion regulation and control, and in the relationship between sensory processing abnormalities and behavioural problems. We discuss the importance of emotion regulation as a mediator between sensory processing and behavioural impairments and its impact in social competence in ASD.

Data-Driven Diagnostics and the Potential of Mobile Artificial Intelligence for Digital Therapeutic Phenotyping in Computational Psychiatry

Data science and digital technologies have the potential to transform diagnostic classification. Digital technologies enable the collection of big data, and advances in machine learning and artificial intelligence enable scalable, rapid, and automated classification of medical conditions. In this review, we summarize and categorize various data-driven methods for diagnostic classification. In particular, we focus on autism as an example of a challenging disorder due to its highly heterogeneous nature. We begin by describing the frontier of data science methods for the neuropsychiatry of autism. We discuss early signs of autism as defined by existing pen-and-paper-based diagnostic instruments and describe data-driven feature selection techniques for determining the behaviors that are most salient for distinguishing children with autism from neurologically typical children. We then describe data-driven detection techniques, particularly computer vision and eye tracking, that provide a means of quantifying behavioral differences between cases and controls. We also describe methods of preserving the privacy of collected videos and prior efforts of incorporating humans in the diagnostic loop. Finally, we summarize existing digital therapeutic interventions that allow for data capture and longitudinal outcome tracking as the diagnosis moves along a positive trajectory. Digital phenotyping of autism is paving the way for quantitative psychiatry more broadly and will set the stage for more scalable, accessible, and precise diagnostic techniques in the field.

A Census of Disease Ontologies

For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.

Development and Validation of a Functional Behavioural Assessment Ontology to Support Behavioural Health Interventions

Background

In the cognitive-behavioral approach, Functional Behavioural Assessment is one of the most effective methods to identify the variables that determine a problem behavior. In this context, the use of modern technologies can encourage the collection and sharing of behavioral patterns, effective intervention strategies, and statistical evidence about antecedents and consequences of clusters of problem behaviors, encouraging the designing of function-based interventions.

Objective

The paper describes the development and validation process used to design a specific Functional Behavioural Assessment Ontology (FBA-Ontology). The FBA-Ontology is a semantic representation of the variables that intervene in a behavioral observation process, facilitating the systematic collection of behavioral data, the consequential planning of treatment strategies and, indirectly, the scientific advancement in this field of study.

Methods

The ontology has been developed deducing concepts and relationships of the ontology from a gold standard and then performing a machine-based validation and a human-based assessment to validate the Functional Behavioural Assessment Ontology. These validation and verification processes were aimed to verify how much the ontology is conceptually well founded and semantically and syntactically correct.

Results

The Pellet reasoner checked the logical consistency and the integrity of classes and properties defined in the ontology, not detecting any violation of constraints in the ontology definition. To assess whether the ontology definition is coherent with the knowledge domain, human evaluation of the ontology was performed asking 84 people to fill in a questionnaire composed by 13 questions assessing concepts, relations between concepts, and concepts’ attributes. The response rate for the survey was 29/84 (34.52%). The domain experts confirmed that the concepts, the attributes, and the relationships between concepts defined in the FBA-Ontology are valid and well represent the Functional Behavioural Assessment process.

Conclusions

The new ontology developed could be a useful tool to design new evidence-based systems in the Behavioral Interventions practices, encouraging the link with other Linked Open Data datasets and repositories to provide users with new models of eHealth focused on the management of problem behaviors. Therefore, new research is needed to develop and implement innovative strategies to improve the poor reproducibility and translatability of basic research findings in the field of behavioral assessment.

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies.

An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA)

Background

Electronic rating scales represent an important resource for standardized data collection. However, the ability to exploit reasoning on rating scale data is still limited. The objective of this work is to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. We developed an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. In order to address the modeling challenges of the SARA, we propose to combine the best performances from OpenEHR clinical archetypes, guidelines and ontologies.

Methods

A scaled-down version of the Human Phenotype Ontology (HPO) was built, extracting the terms that describe the SARA tests from free-text sources. This version of the HPO was then used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Each unit used the most appropriate technology to formally represent the required knowledge.

Results

Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by Spinocerebellar Ataxia Type 36 (SCA36). When comparing the performance of our prototype with that of two independent experts, weighted kappa scores ranged from 0.62 to 0.86.

Conclusions

The combination of archetypes, phenotype ontologies and electronic information-processing rules can be used to automate the extraction of relevant clinical knowledge from plain scores of rating scales. Our results reveal a substantial degree of agreement between the results achieved by an ontology-aware system and the human experts.

Electronic supplementary material

The online version of this article (10.1186/s12911-017-0568-4) contains supplementary material, which is available to authorized users.

Circumscribed interests in adolescents with Autism Spectrum Disorder: A look beyond trains, planes, and clocks

Adolescence is a unique developmental period, characterized by physical and emotional growth and significant maturation of cognitive and social skills. For individuals with Autism Spectrum Disorder (ASD), it is also a vulnerable period as cognitive and social skills can deteriorate. Circumscribed interests (CIs), idiosyncratic areas of intense interest and focus, are a core symptom of ASD that may be associated with social development. Yet, relatively little is known about the expression of CIs in adolescents with ASD. Many studies investigating CIs have used images depicting items of special interest; however, it is not clear how images should be customized for adolescent studies. The goal of this study was to gain insight into the types of images that may be appropriate for studies of CIs in adolescents with ASD. To this end, we used a mixed methods design that included, 1) one-on-one interviews with 10 adolescents (4 with ASD and 6 TD), to identify categories of images that were High Autism Interest (‘HAI’) or High Typically Developing Interest (‘HTD’), and 2) an online survey taken by fifty-three adolescents with ASD (42 male) and 135 typically developing (TD) adolescents (55 male) who rated how much they liked 105 ‘HAI’ and ‘HTD’ images. Although we found a significant interaction between ‘HAI’ and ‘HTD’ categories and diagnosis, neither group significantly preferred one category over the other, and only one individual category ('Celebrities') showed a significant group effect, favored by TD adolescents. Males significantly preferred ‘HAI’ images relative to females, and TD adolescents significantly preferred images with social content relative to adolescents with ASD. Our findings suggest that studies investigating affective or neural responses to CI-related stimuli in adolescents should consider that stereotypical ASD interests (e.g. trains, gadgets) may not accurately represent individual adolescents with ASD, many of whom show interests that overlap with TD adolescents (e.g. video games).

Can data repositories help find effective treatments for complex diseases?

There are many challenges to developing treatments for complex diseases. This review explores the question of whether it is possible to imagine a data repository that would increase the pace of understanding complex diseases sufficiently well to facilitate the development of effective treatments. First, consideration is given to the amount of data that might be needed for such a data repository and whether the existing data storage infrastructure is enough. Several successful data repositories are then examined to see if they have common characteristics. An area of science where unsuccessful attempts to develop a data infrastructure is then described to see what lessons could be learned for a data repository devoted to complex disease. Then, a variety of issues related to sharing data are discussed. In some of these areas, it is reasonably clear how to move forward. In other areas, there are significant open questions that need to be addressed by all data repositories. Using that baseline information, the question of whether data archives can be effective in understanding a complex disease is explored. The major goal of such a data archive is likely to be identifying biomarkers that define sub-populations of the disease.

Automatic and controlled processing and the Broad Autism Phenotype

Research related to verbal fluency in the Broad Autism Phenotype (BAP) is limited and dated, but generally suggests intact abilities in the context of weaknesses in other areas of executive function (Hughes et al., 1999; Wong et al., 2006; Delorme et al., 2007). Controlled processing, the generation of search strategies after initial, automated responses are exhausted (Spat, 2013), has yet to be investigated in the BAP, and may be evidenced in verbal fluency tasks. One hundred twenty-nine participants completed the Delis-Kaplan Executive Function System Verbal Fluency test (D-KEFS; Delis et al., 2001) and the Broad Autism Phenotype Questionnaire (BAPQ; Hurley et al., 2007). The BAP group (n=53) produced significantly fewer total words during the 2nd 15" interval compared to the Non-BAP (n=76) group. Partial correlations indicated similar relations between verbal fluency variables for each group. Regression analyses predicting 2nd 15" interval scores suggested differentiation between controlled and automatic processing skills in both groups. Results suggest adequate automatic processing, but slowed development of controlled processing strategies in the BAP, and provide evidence for similar underlying cognitive constructs for both groups. Controlled processing was predictive of Block Design score for Non-BAP participants, and was predictive of Pragmatic Language score on the BAPQ for BAP participants. These results are similar to past research related to strengths and weaknesses in the BAP, respectively, and suggest that controlled processing strategy use may be required in instances of weak lower-level skills.

Acquisition, Analysis, and Sharing of Data in 2015 and Beyond: A Survey of the Landscape: A Conference Report From the American Heart Association Data Summit 2015

BACKGROUND

A 1.5-day interactive forum was convened to discuss critical issues in the acquisition, analysis, and sharing of data in the field of cardiovascular and stroke science. The discussion will serve as the foundation for the American Heart Association's (AHA's) near-term and future strategies in the Big Data area. The concepts evolving from this forum may also inform other fields of medicine and science.

METHODS AND RESULTS

A total of 47 participants representing stakeholders from 7 domains (patients, basic scientists, clinical investigators, population researchers, clinicians and healthcare system administrators, industry, and regulatory authorities) participated in the conference. Presentation topics included updates on data as viewed from conventional medical and nonmedical sources, building and using Big Data repositories, articulation of the goals of data sharing, and principles of responsible data sharing. Facilitated breakout sessions were conducted to examine what each of the 7 stakeholder domains wants from Big Data under ideal circumstances and the possible roles that the AHA might play in meeting their needs. Important areas that are high priorities for further study regarding Big Data include a description of the methodology of how to acquire and analyze findings, validation of the veracity of discoveries from such research, and integration into investigative and clinical care aspects of future cardiovascular and stroke medicine. Potential roles that the AHA might consider include facilitating a standards discussion (eg, tools, methodology, and appropriate data use), providing education (eg, healthcare providers, patients, investigators), and helping build an interoperable digital ecosystem in cardiovascular and stroke science.

CONCLUSION

There was a consensus across stakeholder domains that Big Data holds great promise for revolutionizing the way cardiovascular and stroke research is conducted and clinical care is delivered; however, there is a clear need for the creation of a vision of how to use it to achieve the desired goals. Potential roles for the AHA center around facilitating a discussion of standards, providing education, and helping establish a cardiovascular digital ecosystem. This ecosystem should be interoperable and needs to interface with the rapidly growing digital object environment of the modern-day healthcare system.

An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data

OBJECTIVE

Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data.

MATERIALS AND METHODS

Knowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data.

RESULTS

We extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94.

DISCUSSION

The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy.

CONCLUSION

The ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology.

Future directions for research in autism spectrum disorders

This article suggests future directions for research aimed at improving our understanding of the etiology and pathophysiology of autism spectrum disorder (ASD) as well as pharmacologic and psychosocial interventions for ASD across the lifespan. The past few years have witnessed unprecedented transformations in the understanding of ASD neurobiology, genetics, early identification, and early intervention. However, recent increases in ASD prevalence estimates highlight the urgent need for continued efforts to translate novel ASD discoveries into effective interventions for all individuals with ASD. In this article we highlight promising areas for ongoing and new research expected to quicken the pace of scientific discovery and ultimately the translation of research findings into accessible and empirically supported interventions for those with ASD. We highlight emerging research in the following domains as particularly promising and pressing: (a) preclinical models, (b) experimental therapeutics, (c) early identification and intervention, (d) psychiatric comorbidities and the Research Domain Criteria initiative, (e) ecological momentary assessment, (f) neurotechnologies, and (g) the needs of adults with ASD. Increased research emphasis in these areas has the potential to hasten the translation of knowledge on the etiological mechanisms of ASD to psychosocial and biological interventions to reduce the burden of ASD on affected individuals and their families.