scholarly journals Autism spectrum heterogeneity: fact or artifact?

2020 ◽  
Vol 25 (12) ◽  
pp. 3178-3185 ◽  
Author(s):  
Laurent Mottron ◽  
Danilo Bzdok
Keyword(s):  
Autistic Traits ◽  
Fold Increase ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Qualitative Properties ◽  
Diagnostic Practices ◽  
Clinical Intuition ◽  
Dsm 5 ◽  
Diagnostic Threshold ◽  
Categorical Diagnosis

AbstractThe current diagnostic practices are linked to a 20-fold increase in the reported prevalence of ASD over the last 30 years. Fragmenting the autism phenotype into dimensional “autistic traits” results in the alleged recognition of autism-like symptoms in any psychiatric or neurodevelopemental condition and in individuals decreasingly distant from the typical population, and prematurely dismisses the relevance of a diagnostic threshold. Non-specific socio-communicative and repetitive DSM 5 criteria, combined with four quantitative specifiers as well as all their possible combinations, render limitless variety of presentations consistent with the categorical diagnosis of ASD. We propose several remedies to this problem: maintain a line of research on prototypical autism; limit the heterogeneity compatible with a categorical diagnosis to situations with a phenotypic overlap and a validated etiological link with prototypical autism; reintroduce the qualitative properties of autism presentations and of current dimensional specifiers, language, intelligence, comorbidity, and severity in the criteria used to diagnose autism in replacement of quantitative “social” and “repetitive” criteria; use these qualitative features combined with the clinical intuition of experts and machine-learning algorithms to differentiate coherent subgroups in today’s autism spectrum; study these subgroups separately, and then compare them; and question the autistic nature of “autistic traits”

scholarly journals Autistic Traits and Illness Trajectories

2019 ◽  
Vol 15 (1) ◽  
pp. 94-98 ◽  
Author(s):  
Liliana Dell'Osso ◽  
Primo Lorenzi ◽  
Barbara Carpita
Keyword(s):  
Psychiatric Disorders ◽  
Recent Literature ◽  
Autistic Traits ◽  
Human Mind ◽  
Autism Spectrum ◽  
Clinical Population ◽  
Dsm 5 ◽  
Starting Point ◽  
Specific Localization ◽  
Personality Features

In the framework of increasing attention towards autism-related conditions, a growing number of studies have recently investigated the prevalence and features of sub-threshold Autistic Traits (ATs) among adults. ATs span across the general population, being more pronounced in several clinical groups of patients affected by psychiatric disorders. Moreover, ATs seem to be associated with specific personality features in non-clinical population, implying both a higher vulnerability towards psychopathology and extraordinary talents in specific fields. In this framework, the DSM-5’s Autism Spectrum Disorder (ASD) presentations may be considered as the tip of an iceberg that features several possible clinical and non-clinical phenotypes. Globally, the autism spectrum may be considered as a trans-nosographic dimension, which may not only represent the starting point for the development of different psychopathological trajectories but also underlie non-psychopathological personality traits. These different trajectories might be shaped by the specific localization and severity of the neurodevelopmental alteration and by its interaction with the environment and lifetime events. In this wider framework, autistic-like neurodevelopmental alterations may be considered as a general vulnerability factor for different kinds of psychiatric disorders, but also the neurobiological basis for the development of extraordinary abilities, eventually underlying the concept of geniality. Moreover, according to recent literature, we hypothesize that ATs may also be involved in the functioning of human mind, featuring the peculiar sense of “otherness” which can be found, with different grades of intensity, in every human being.

scholarly journals Defining the Optimal Threshold Scores for Adult Autism Subthreshold Spectrum (AdAS Spectrum) in Clinical and General Population

2020 ◽  
Vol 16 (1) ◽  
pp. 204-211
Author(s):  
Liliana Dell’Osso ◽  
Claudia Carmassi ◽  
Ivan Mirko Cremone ◽  
Dario Muti ◽  
Antonio Salerni ◽  
...  
Keyword(s):  
General Population ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Autism Spectrum Quotient ◽  
Dsm 5 ◽  
Diagnostic Thresholds ◽  
Psychometric Tool ◽  
Autistic Symptoms ◽  
Adult Autism ◽  
Clinically Significant

Background: The Adult Autism Subthreshold Spectrum (AdAS Spectrum) is a recently developed instrument tailored to assess the broad range of full-threshold as well as sub-threshold manifestations related to the autism spectrum. Although it has proved to be a valuable instrument for quantitative assessment of autistic symptoms, the AdAS Spectrum still lacks validated diagnostic thresholds. Objective: The aim of this study was to define the best cut-off scores of the AdAS Spectrum for determining the presence of subthreshold autistic traits as well as a clinically significant autism spectrum disorder (ASD). Methods: Our sample was composed of 39 patients with full-blown ASD, 73 subjects with autistic traits, and 150 healthy controls. Subjects were evaluated by trained psychiatrists, who performed a clinical diagnosis according to DSM-5 and then assessed with the AdAS Spectrum and the Autism Spectrum Quotient. Results: Our results showed that the most discriminant cut-off scores were 70 for identifying subjects with full-blown ASD, and 43 for determining the presence of significant autistic traits. Conclusion: The threshold values proposed here showed satisfying levels of specificity and sensibility, as well as a good agreement with the diagnosis according to DSM-5 criteria, confirming the validity of the AdAS Spectrum as a psychometric tool for measuring ASD-related conditions in the clinical and general population.

scholarly journals From Asperger's Autistischen Psychopathen to DSM-5 Autism Spectrum Disorder and Beyond: A Subthreshold Autism Spectrum Model

2016 ◽  
Vol 12 (1) ◽  
pp. 120-131 ◽  
Author(s):  
Liliana Dell’Osso ◽  
Riccardo Dalle Luche ◽  
Camilla Gesi ◽  
Ilenia Moroni ◽  
Claudia Carmassi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Asperger’S Disorder ◽  
Autistic Traits ◽  
Asperger's Disorder ◽  
Behavioral Model ◽  
Threshold Level ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Dsm 5 ◽  
Spectrum Model

Growing interest has recently been devoted to partial forms of autism, lying at the diagnostic boundaries of those conditions previously diagnosed as Asperger’s Disorder. This latter includes an important retrieval of the European classical psychopathological concepts of adult autism to which Hans Asperger referred in his work. Based on the review of Asperger's Autistische Psychopathie, from first descriptions through the DSM-IV Asperger’s Disorder and up to the recent DSM-5 Autism Spectrum Disorder, the paper aims to propose a Subthreshold Autism Spectrum Model that encompasses not only threshold-level manifestations but also mild/atypical symptoms, gender-specific features, behavioral manifestations and personality traits associated with Autism Spectrum Disorder. This model includes, but is not limited to, the so-called broad autism phenotype spanning across the general population that does not fully meet Autism Spectrum Disorder criteria. From this perspective, we propose a subthreshold autism as a unique psychological/behavioral model for research that could help to understand the neurodevelopmental trajectories leading from autistic traits to a broad range of mental disorders.

scholarly journals An accessible and efficient autism screening method for behavioural data and predictive analyses

2018 ◽  
Vol 25 (4) ◽  
pp. 1739-1755 ◽  
Author(s):  
Fadi Thabtah
Keyword(s):  
Autism Spectrum Disorder ◽  
Waiting Times ◽  
Screening Method ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Screening Methods ◽  
Autism Screening ◽  
Predictive Analyses

Autism spectrum disorder is associated with significant healthcare costs, and early diagnosis can substantially reduce these. Unfortunately, waiting times for an autism spectrum disorder diagnosis are lengthy due to the fact that current diagnostic procedures are time-consuming and not cost-effective. Overall, the economic impact of autism and the increase in the number of autism spectrum disorder cases across the world reveal an urgent need for the development of easily implemented and effective screening methods. This article proposes a new mobile application to overcome the problem by offering users and the health community a friendly, time-efficient and accessible mobile-based autism spectrum disorder screening tool called ASDTests. The proposed ASDTests app can be used by health professionals to assist their practice or to inform individuals whether they should pursue formal clinical diagnosis. Unlike existing autism screening apps being tested, the proposed app covers a larger audience since it contains four different tests, one each for toddlers, children, adolescents and adults as well as being available in 11 different languages. More importantly, the proposed app is a vital tool for data collection related to autism spectrum disorder for toddlers, children, adolescent and adults since initially over 1400 instances of cases and controls have been collected. Feature and predictive analyses demonstrate small groups of autistic traits improving the efficiency and accuracy of screening processes. In addition, classifiers derived using machine learning algorithms report promising results with respect to sensitivity, specificity and accuracy rates.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Developmental trajectories of autistic social traits in the general population

2021 ◽  
pp. 1-9
Author(s):  
Richard Pender ◽  
Pasco Fearon ◽  
Beate St Pourcain ◽  
Jon Heron ◽  
Will Mandy
Keyword(s):  
General Population ◽  
Full Range ◽  
Population Sample ◽  
Autistic Traits ◽  
Communication Disorders ◽  
Autism Spectrum ◽  
Growth Mixture Modelling ◽  
Autistic People ◽  
Social Difficulties ◽  
Autism Trait

Abstract Background Autistic people show diverse trajectories of autistic traits over time, a phenomenon labelled ‘chronogeneity’. For example, some show a decrease in symptoms, whilst others experience an intensification of difficulties. Autism spectrum disorder (ASD) is a dimensional condition, representing one end of a trait continuum that extends throughout the population. To date, no studies have investigated chronogeneity across the full range of autistic traits. We investigated the nature and clinical significance of autism trait chronogeneity in a large, general population sample. Methods Autistic social/communication traits (ASTs) were measured in the Avon Longitudinal Study of Parents and Children using the Social and Communication Disorders Checklist (SCDC) at ages 7, 10, 13 and 16 (N = 9744). We used Growth Mixture Modelling (GMM) to identify groups defined by their AST trajectories. Measures of ASD diagnosis, sex, IQ and mental health (internalising and externalising) were used to investigate external validity of the derived trajectory groups. Results The selected GMM model identified four AST trajectory groups: (i) Persistent High (2.3% of sample), (ii) Persistent Low (83.5%), (iii) Increasing (7.3%) and (iv) Decreasing (6.9%) trajectories. The Increasing group, in which females were a slight majority (53.2%), showed dramatic increases in SCDC scores during adolescence, accompanied by escalating internalising and externalising difficulties. Two-thirds (63.6%) of the Decreasing group were male. Conclusions Clinicians should note that for some young people autism-trait-like social difficulties first emerge during adolescence accompanied by problems with mood, anxiety, conduct and attention. A converse, majority-male group shows decreasing social difficulties during adolescence.

scholarly journals Diachronic Tendency and Thinking Style Across the Broader Autism Spectrum: Sources of Individual Differences?

2021 ◽  
Author(s):  
Stian Orm ◽  
Ella Holt Holmberg ◽  
Paul L. Harris ◽  
Maria Nunez ◽  
Francisco Pons
Keyword(s):  
Autistic Traits ◽  
Broader Autism Phenotype ◽  
Autism Spectrum ◽  
Inner Speech ◽  
Autism Spectrum Quotient ◽  
Present Moment ◽  
Thinking Style ◽  
Mental Images ◽  
High School Degree ◽  
Future Events

Abstract Objectives First, to see whether previous studies showing a limited capacity to spontaneously evoke the past and the future of a present moment (diachronic tendency) and a prevalence of mental images over inner speech (thinking style) in individuals with autism spectrum disorder could be replicated in individuals belonging to the broader autism phenotype. Second, to test the hypothesis that individuals thinking with mental images have a more limited diachronic tendency compared with individuals thinking with inner speech. Methods Adults (N = 309, Mage = 31.5 years, 76% women) with at least a high school degree were assessed with the Autism Spectrum Quotient, a test of diachronic tendency comprising four pictures varying in social interactivity and dynamicity, and a thinking style scale comprising three items representing three different everyday situations. Results The results showed that adults with many autistic traits have a limited diachronic tendency but only when the situation is socially interactive and dynamic, think more in mental images than individuals with no or few autistic traits but nevertheless still think more with inner speech than with mental images, and the more the participants reported thinking in inner speech, the more they evoked past and future events when describing a socially interactive and dynamic situation. Conclusions More autistic traits are associated with a limited diachronic tendency in socially interactive and dynamic situations and more thinking in mental images, and thinking style could be one of the determinants of diachronic tendency in socially interactive and dynamic situations.

scholarly journals Correction to: Identifying Subgroups of Toddlers with DSM‑5 Autism Spectrum Disorder Based on Core Symptoms

2021 ◽  
Author(s):  
Holly K. Harris ◽  
Collin Lee ◽  
Georgios D. Sideridis ◽  
William J. Barbaresi ◽  
Elizabeth Harstad
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Dsm 5 ◽  
Core Symptoms

scholarly journals Dysfunctions in Infants’ Statistical Learning are Related to Parental Autistic Traits

2021 ◽  
Author(s):  
Bettoni Roberta ◽  
Valentina Riva ◽  
Chiara Cantiani ◽  
Elena Maria Riboldi ◽  
Massimo Molteni ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Skills ◽  
Statistical Learning ◽  
Crucial Role ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Statistical Structure ◽  
The Relationship ◽  
Visual Habituation

AbstractStatistical learning refers to the ability to extract the statistical relations embedded in a sequence, and it plays a crucial role in the development of communicative and social skills that are impacted in the Autism Spectrum Disorder (ASD). Here, we investigated the relationship between infants’ SL ability and autistic traits in their parents. Using a visual habituation task, we tested infant offspring of adults (non-diagnosed) who show high (HAT infants) versus low (LAT infants) autistic traits. Results demonstrated that LAT infants learned the statistical structure embedded in a visual sequence, while HAT infants failed. Moreover, infants’ SL ability was related to autistic traits in their parents, further suggesting that early dysfunctions in SL might contribute to variabilities in ASD symptoms.

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