scholarly journals Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder

2020 ◽  
Vol 11 ◽  
Author(s):  
Eleni A. Demetriou ◽  
Shin H. Park ◽  
Nicholas Ho ◽  
Karen L. Pepper ◽  
Yun J. C. Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Differential Diagnosis ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Conditions ◽  
Social Difficulty

scholarly journals Neurodevelopmental Disorder traits in Taijin-Kyofu-Sho and Social Anxiety Disorder: A Cross-Sectional Study Among University Students

2021 ◽  
Author(s):  
Kosuke Kajitani ◽  
Rikako Tsuchimoto ◽  
Yusaku Omodaka ◽  
Tomoko Matsushita ◽  
Hideaki Fukumori ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Attention Deficit ◽  
Odds Ratio ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Hyperactivity Disorder

Abstract Background: Taijin-Kyofu-sho, an East Asian culture-bound anxiety disorder, has been likened to social anxiety disorder. However, few studies have examined these two disorders from the perspective of neurodevelopmental disorders. This study aimed to examine the association of Taijin-Kyofu-sho and social anxiety disorder with attention-deficit/hyperactivity disorder and autism spectrum disorder.Methods: The Liebowitz Social Anxiety, Taijin-Kyofu-sho, and Adult Attention-Deficit/Hyperactivity Disorder Self-Report scales and the 16-item Autism-Spectrum Quotient were administered to 818 university students. Participants were divided into four groups: control (neither Taijin-Kyofu-sho ­­nor social anxiety disorder), pure Taijin-Kyofu-sho (Taijin-Kyofu-sho alone), pure social anxiety disorder (social anxiety disorder alone), and Taijin-Kyofu-sho-social anxiety disorder mixed (both Taijin-Kyofu-sho ­­and social anxiety disorder). We used logistic regression analysis to examine whether attention-deficit/hyperactivity disorder and autism spectrum disorder were associated with Taijin-Kyofu-sho and social anxiety disorder.Results: Autism spectrum disorder was significantly associated with pure Taijin-Kyofu-sho (p = 0.006, odds ratio: 3.99). Female sex and attention-deficit/hyperactivity disorder were significantly associated with pure social anxiety disorder (sex: p = 0.013, odds ratio: 2.61; attention-deficit/hyperactivity disorder: p = 0.012, odds ratio: 2.46). Female sex, attention-deficit/hyperactivity disorder, and autism spectrum disorder were significantly associated with Taijin-Kyofu-sho-social anxiety disorder mixed (sex: p = 0.043, odds ratio: 2.16; attention-deficit/hyperactivity disorder: p = 0.003, odds ratio: 2.75; autism spectrum disorder: p < 0.001, odds ratio: 16.93). Conclusions: Neurodevelopmental disorder traits differed between individuals with Taijin-Kyofu-sho and those with social anxiety disorder. Japanese individuals with attention-deficit/hyperactivity disorder or autism spectrum disorder traits are at a risk of developing Taijin-Kyofu-sho or social anxiety disorder in the future.

scholarly journals Selective Mutism and Its Relations to Social Anxiety Disorder and Autism Spectrum Disorder

2021 ◽  
Author(s):  
Peter Muris ◽  
Thomas H. Ollendick
Keyword(s):  
Autism Spectrum Disorder ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Selective Mutism ◽  
Autism Spectrum ◽  
Classification Systems ◽  
Spectrum Disorder ◽  
Special Role ◽  
Social Difficulties

AbstractIn current classification systems, selective mutism (SM) is included in the broad anxiety disorders category. Indeed, there is abundant evidence showing that anxiety, and social anxiety in particular, is a prominent feature of SM. In this article, we point out that autism spectrum problems in addition to anxiety problems are sometimes also implicated in SM. To build our case, we summarize evidence showing that SM, social anxiety disorder (SAD), and autism spectrum disorder (ASD) are allied clinical conditions and share communalities in the realm of social difficulties. Following this, we address the role of a prototypical class of ASD symptoms, restricted and repetitive behaviors and interests (RRBIs), which are hypothesized to play a special role in the preservation and exacerbation of social difficulties. We then substantiate our point that SM is sometimes more than an anxiety disorder by addressing its special link with ASD in more detail. Finally, we close by noting that the possible involvement of ASD in SM has a number of consequences for clinical practice with regard to its classification, assessment, and treatment of children with SM and highlight a number of directions for future research.

Modular Cognitive-Behavioral Therapy for a Young Adult With Autism Spectrum Disorder, Comorbid Obsessive Compulsive Disorder, and Social Anxiety Disorder

2018 ◽  
Vol 17 (3) ◽  
pp. 136-149 ◽  
Author(s):  
D. Luis Ordaz ◽  
Adam B. Lewin ◽  
Eric A. Storch
Keyword(s):  
Autism Spectrum Disorder ◽  
Case Report ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Symptom Severity ◽  
Behavioral Therapy ◽  
Autism Spectrum ◽  
Obsessive Compulsive ◽  
Compulsive Disorder

This case report outlines the use of a modular cognitive-behavioral therapy (CBT) protocol used to treat obsessive compulsive disorder (OCD) and social anxiety disorder (SAD) in an 18-year-old female, “Jaina,” with autism spectrum disorder (ASD). Jaina completed 16 weekly CBT sessions that lasted approximately 60 min each. On completing the program, Jaina’s symptom severity for treatment targets of OCD and SAD had reduced based on the Anxiety and Related Disorders Interview Schedule for Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM- 5; ADIS-5). In addition, overall anxiety symptom severity reduction was evident at posttreatment based on the Clinical Global Impression of Severity (CGI-S). This case report supports the use of a modular CBT program (developed for late adolescents and young adults with ASD and comorbid anxiety/OCD) for OCD and SAD.

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.

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