An exploration of concomitant psychiatric disorders in children with autism spectrum disorder

Lecavalier, Luc; McCracken, Courtney; Aman, Michael G.; McDougle, Christopher J.; McCracken, James T.; Tierney, Elaine; Smith, Tristram; Johnson, Cynthia R.; King, Bryan H.; Handen, Benjamin L.; Swiezy, Naomi B.; Arnold, L. Eugene; Bearss, Karen; Vitiello, Benedetto; Scahill, Lawrence

doi:10.1016/j.comppsych.2018.10.012

Cited by 84 publications

(62 citation statements)

References 41 publications

(59 reference statements)

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“…Numerous studies have found that youth with ASD are vulnerable to a wide range of psychiatric symptoms [e.g., Lecavalier et al, 2019;Rosen et al, 2018] and disorders [Simonoff et al, 2008]. Our findings suggest that elevations in psychiatric symptom cooccurrence [e.g., among individuals with ASD compared with non-ASD individuals might result from differences in core transdiagnostic factors between these groups.…”

Section: Discussionmentioning

confidence: 63%

“…Youth with autism spectrum disorder (ASD) experience symptoms of a wide range of psychiatric disorders [Gadow, DeVincent, Pomeroy, & Azizian, ; Lecavalier et al, ; Simonoff et al, ]; in fact, the rate and severity of many co‐occurring (i.e., comorbid) disorders and symptoms are comparable to non‐ASD psychiatry referrals [Gadow et al, ; Weber & Gadow, ]. Clinically, it is important not to underestimate their significance as they contribute to the burden of health care [Gurney, McPheeters, & Davis, ], result in considerable personal and family distress [Lecavalier, Leone, & Wiltz, ], are the primary reason for prescribing psychotropic medication [Mandell et al, ; Witwer & Lecavalier, ], and are a potential contributor to mortality [Schendel et al, ].…”

Section: Introductionmentioning

confidence: 99%

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A transdiagnostic model of psychiatric symptom co‐occurrence and autism spectrum disorder

et al. 2019

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Understanding whether the co‐occurrence of psychiatric symptoms within autism spectrum disorder (ASD) are specific to the ASD diagnosis or reflect similar higher‐order patterns observed in both ASD and non‐ASD samples, or a confluence of the two, is of critical importance. If similar, it would suggest that comorbid psychiatric conditions among individuals with ASD are not symptoms of specific, non‐ASD psychiatric disorders per se, but reflect a general liability to psychopathology associated with ASD. To this end, the current study examined whether the higher‐order structure of co‐occurring psychiatric symptoms was the same within ASD and non‐ASD youth. Parents of clinic‐referred youth with (n = 280) and without (n = 943) ASD completed a DSM‐IV‐referenced psychiatric symptom rating scale. A confirmatory factor analytic framework was used to examine four levels of measurement invariance across groups to determine the extent to which transdiagnostic factors were comparable. Transdiagnostic factors were characterized by symptoms of the same disorders (configural invariance) and the same factor loadings across groups (metric invariance). Furthermore, both groups evidenced equivalent numbers of symptoms of most psychiatric conditions with the notable exceptions of attention deficit hyperactivity disorder (ADHD) and social anxiety (partial strong invariance), which were higher in the ASD sample. It was concluded that disparities in the co‐occurrence of psychiatric symptoms between youth with and without ASD may be largely reflective of transdiagnostic factor level differences associated with ASD and not indicative of the ASD diagnosis per se. However, for ADHD and social anxiety, there appears to be some specific associations with the ASD diagnosis. Autism Res 2020, 13: 579–590. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Few transdiagnostic dimensions relate common mental disorder diagnoses with one another. These dimensions explain psychiatric comorbidity (i.e., the finding that many persons possess several disorder diagnoses simultaneously). However, it is unclear if these dimensions differ among children with autism spectrum disorder (ASD), compared with their non‐ASD counterparts. The results of this study demonstrate that underlying transdiagnostic dimensions are similar in both ASD and non‐ASD children. However, there appear to be ASD‐specific differences when it comes to social anxiety and attention deficit hyperactivity disorder.

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Section: Discussionmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

A transdiagnostic model of psychiatric symptom co‐occurrence and autism spectrum disorder

et al. 2019

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“…Further, high rates of co-occurrence of disruptive behavior and anxiety are common in children with ASD. For example, in a recent study of co-occurring psychopathology in children with ASD seeking treatment for disruptive behavior, 44% of children met criteria for one or more internalizing disorders (69). In the current study, children with ASD+DB were recruited based on clinically significant levels of disruptive behavior, and all met criteria for oppositional defiant disorder as a primary diagnosis.…”

Section: Discussionmentioning

confidence: 94%

Reduced Amygdala–Prefrontal Functional Connectivity in Children With Autism Spectrum Disorder and Co-occurring Disruptive Behavior

Ibrahim

Eilbott

Ventola

et al. 2019

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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BACKGROUND: Disruptive behaviors are prevalent in children with autism spectrum disorder (ASD) and often cause substantial impairments. However, the underlying neural mechanisms of disruptive behaviors remain poorly understood in ASD. In children without ASD, disruptive behavior is associated with amygdala hyperactivity and reduced connectivity with the ventrolateral prefrontal cortex (vlPFC). This study examined amygdala reactivity and connectivity in children with ASD with and without co-occurring disruptive behavior disorders. We also investigated differential contributions of externalizing behaviors and callous-unemotional traits to variance in amygdala connectivity and reactivity. METHODS: This cross-sectional study involved behavioral assessments and neuroimaging in three groups of children 8 to 16 years of age: 18 children had ASD and disruptive behavior, 20 children had ASD without disruptive behavior, and 19 children were typically developing control

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“…Internalizing and externalizing symptoms in childhood are both frequently associated with higher risk of mental illness diagnosis later in life [5,8]. Specifically, impulsivity has been associated with a susceptibility of developing mental illnesses and suicide [9,10]. Moreover, neurodevelopmental disorders, such as autism or ADHD, indicate lifelong diagnosis and frequent psychiatric comorbidities [11].…”

Section: Introductionmentioning

confidence: 99%

Predicting mental health problems in adolescence using machine learning techniques

Tate

McCabe²,

Larsson

et al. 2020

PLoS ONE

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BackgroundPredicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms have been flagged as indicators, there is currently no model created to screen the general population for the risk of developing mental health problems. Additionally, the advent of machine learning techniques represents an exciting way to potentially improve upon the standard prediction modelling technique, logistic regression. Therefore, we aimed to I.) develop a model that can predict mental health problems in mid-adolescence II.) investigate if machine learning techniques (random forest, support vector machines, neural network, and XGBoost) will outperform logistic regression. MethodsIn 7,638 twins from the Child and Adolescent Twin Study in Sweden we used 474 predictors derived from parental report and register data. The outcome, mental health problems, was determined by the Strengths and Difficulties Questionnaire. Model performance was determined by the area under the receiver operating characteristic curve (AUC). ResultsAlthough model performance varied somewhat, the confidence interval overlapped for each model indicating non-significant superiority for the random forest model (AUC = 0.739, 95% CI 0.708-0.769), followed closely by support vector machines (AUC = 0.735, 95% CI 0.707-0.764). PLOS ONEPLOS ONE | https://doi.org/10.1371/journal.pone.

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An exploration of concomitant psychiatric disorders in children with autism spectrum disorder

Cited by 84 publications

References 41 publications

A transdiagnostic model of psychiatric symptom co‐occurrence and autism spectrum disorder

A transdiagnostic model of psychiatric symptom co‐occurrence and autism spectrum disorder

Reduced Amygdala–Prefrontal Functional Connectivity in Children With Autism Spectrum Disorder and Co-occurring Disruptive Behavior

Predicting mental health problems in adolescence using machine learning techniques

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