Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach

Georgiades, Stelios; Szatmári, Péter; Boyle, Michael; Hanna, Steven; Duku, Eric; Zwaigenbaum, Lonnie; Bryson, Susan E.; Fombonne, Éric; Volden, Joanne; Mirenda, Pat; Smith, Isabel M.; Roberts, Wendy; Vaillancourt, Tracy; Waddell, Charlotte; Bennett, Teresa; Thompson, Ann

doi:10.1111/j.1469-7610.2012.02588.x

Cited by 229 publications

(141 citation statements)

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“…It is likely that widely varied brain dysfunctions in family members with BAP result in varied constellations of social, language, cognitive, and psychiatric symptoms. Researchers have proposed that ASD includes biologically valid subgroups (Aldinger et al 2015;Brandler and Sebat 2015;Ellegood et al 2015;Georgiades et al 2013;Whitehouse and Stanley 2013;Williams and Bowler 2014). predicted that ASD would eventually become a set of biologically valid subgroups defined Bby genetic and/or neurological…findings^(p. 339), and Brandler and Sebat (2015) predicted that ASD would naturally disband into valid Bquanta of separate genetic disorders^(p. 502).…”

Section: Rates Of Recovery From Asd Are Variedmentioning

confidence: 99%

ASD Validity

Waterhouse

London

Gillberg

2016

Rev J Autism Dev Disord

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ASD research is at an important crossroads. The ASD diagnosis is important for assigning a child to early behavioral intervention and explaining a child's condition. But ASD research has not provided a diagnosis-specific medical treatment, or a consistent early predictor, or a unified life course. If the ASD diagnosis also lacks biological and construct validity, a shift away from studying ASD-defined samples would be warranted. Consequently, this paper reviews recent findings for the neurobiological validity of ASD, the construct validity of ASD diagnostic criteria, and the construct validity of ASD spectrum features. The findings reviewed indicate that the ASD diagnosis lacks biological and construct validity. The paper concludes with proposals for research going forward.Keywords DSM-5 . ASD . Autism . Diagnosis . Validity . Comorbidity . HeterogeneityThe goal of the DSM-3 nosology (American Psychiatric Association 1980) was to create reliable and standard categorical psychiatric diagnoses (Robins and Guze 1970). However, in the past 30 years, clinical, genetics, and neuroscience findings have revealed that the DSM diagnoses are not biologically valid. The National Institutes of Mental Health (NIMH) responded by proposing the Research Domain Criteria (RDoC) framework for a brain-based transdiagnostic psychiatric symptom nosology (Cuthbert and Insel 2013;Insel et al. 2010;Lilienfeld and Treadway 2016). Peterson (2015) and Weinberger et al. (2015) argued that the RDoC could not replace the DSM-5 psychiatric nosology (American Psychiatric Association 2013) or the parallel International Classification of Diseases (ICD) psychiatric nosology (World Health Organization 2012). But BFor the foreseeable future, RDoC is not envisioned as a system of psychiatric classification in its own right. Instead, in the near term, RDoC and DSM-ICD are expected to coexist. Nevertheless, RDoC is intended to provide scaffolding for a large-scale research program that will ultimately yield an alternative to DSM-ICD^ (Lilienfeld and Treadway 2016, p. 445).RDoC advocates accept that DSM-5/ICD psychiatric categories remain necessary in clinical practice, but argue that researchers should shift to RDoC study designs immediately. They assert that studying psychiatric categories lacking biological validity blocks the discovery of brain bases for psychopathology and thus cannot lead to effective medical treatments for specific psychiatric symptoms (Cuthbert and Insel 2013;Insel et al. 2010;Lilienfeld and Treadway 2016;Yee et al. 2015). Against this RDoC imperative for biological validity, Weinberger et al. (2015) countered that current DSM-5 psychiatric behavioral diagnoses were valid when they yielded effective medical treatment, clear prognosis, and a life course specific to a diagnosis.Autism spectrum disorder (ASD) research has been productive (Dawson 2016;de la Torre-Ubieta et al. 2016;Szatmari et al. 2016), but no ASD research findings have met the validity criteria of Weinberger et al. (2015). DSM-5 ASD research has found no s...

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Section: Rates Of Recovery From Asd Are Variedmentioning

confidence: 99%

ASD Validity

Waterhouse

London

Gillberg

2016

Rev J Autism Dev Disord

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“…These statistical techniques have the potential to provide additional information about cognitive and behavioural subtypes of ASD. For example, Georgiades et al (2013) used factor-mixture modelling to suggest that the two ASD symptom domains of social-communicative impairments and RRBIs may be independent. The differing symptom profiles of severity suggested support for the existence of three homogeneous subgroups of ASD.…”

Section: Towards a Multifaceted Cognitive Account Of Asd: Questions Amentioning

confidence: 99%

Exploring the ‘fractionation’ of autism at the cognitive level

Brunsdon

Happé

2013

Autism

167

118

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Autism spectrum disorders are defined by difficulties across a range of areas: social and communication difficulties and restricted and repetitive behaviours and interests. It has been suggested that this triad of symptoms cannot be explained by a single cause at the genetic, neural or cognitive level. This article reviews the evidence for a 'fractionable' autism triad at the cognitive level, highlighting questions for future research.

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“…The definition admits substantial behavioural heterogeneity (Georgiades et al, 2013); ASD is, in fact, a family of developmental disorders with unique, but related, phenotypes, with a variety of genetic associations (Devlin and Scherer, 2012). Moreover, ASDs are developmental disorders, and the behavioural abnormalities evolve over time (Gotham et al, 2012;Szatmari et al, 2015), adding to the apparent heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

et al. 2017

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Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multiprotocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a wholePreprint submitted to NeuroImage September 4, 2017 brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

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Investigating phenotypic heterogeneity in children with autism spectrum disorder: a factor mixture modeling approach

Abstract: Study findings suggest that the two symptom dimensions of SCD and FIRB proposed for the DSM 5 can be used in FMM to stratify children with ASD empirically into three relatively homogeneous subgroups.

Cited by 229 publications

References 36 publications

ASD Validity

ASD Validity

Exploring the ‘fractionation’ of autism at the cognitive level

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

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