Dataset Information

Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data.

ABSTRACT:

Background

Heterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, to subclassify ASD. We will assess whether behavior-based subgrouping yields clinically, genetically, and neurologically distinct ASD groups.

Methods

We propose a study involving 60 individuals with ASD recruited from a specialized psychiatric clinic to perform joint attention tasks. Through the examination of gaze patterns in social contexts, we will conduct a semi-supervised clustering analysis, yielding two primary clusters: good gaze response group and poor gaze response group. Subsequent comparison will occur across these clusters, scrutinizing neuroanatomical structure and connectivity using structural as well as functional brain imaging studies, genetic predisposition through single nucleotide polymorphism data, and assorted socio-demographic and clinical information.

Conclusions

The aim of the study is to investigate the discriminative properties and the validity of the joint attention-based subclassification of ASD using multi-modality data.

Trial registration

Clinical trial, KCT0008530, Registered 16 June 2023, https://cris.nih.go.kr/cris/index/index.do .

SUBMITTER: Ko C

PROVIDER: S-EPMC10426216 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data.

Ko Chanyoung C Kang Soyeon S Hong Soon-Beom SB Park Yu Rang YR

BMC psychiatry 20230815 1

<h4>Background</h4>Heterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, t ...[more]

PMID: 37582781

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Project description:ImportanceJoint attention, composed of complex behaviors, is an early-emerging social function that is deficient in children with autism spectrum disorder (ASD). Currently, no methods are available for objectively quantifying joint attention.ObjectiveTo train deep learning (DL) models to distinguish ASD from typical development (TD) and to differentiate ASD symptom severities using video data of joint attention behaviors.Design, setting, and participantsIn this diagnostic study, joint attention tasks were administered to children with and without ASD, and video data were collected from multiple institutions from August 5, 2021, to July 18, 2022. Of 110 children, 95 (86.4%) completed study measures. Enrollment criteria were 24 to 72 months of age and ability to sit with no history of visual or auditory deficits.ExposuresChildren were screened using the Childhood Autism Rating Scale. Forty-five children were diagnosed with ASD. Three types of joint attention were assessed using a specific protocol.Main outcomes and measuresCorrectly distinguishing ASD from TD and different levels of ASD symptom severity using the DL model area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall.ResultsThe analytical population consisted of 45 children with ASD (mean [SD] age, 48.0 [13.4] months; 24 [53.3%] boys) vs 50 with TD (mean [SD] age, 47.9 [12.5] months; 27 [54.0%] boys). The DL ASD vs TD models showed good predictive performance for initiation of joint attention (IJA) (AUROC, 99.6% [95% CI, 99.4%-99.7%]; accuracy, 97.6% [95% CI, 97.1%-98.1%]; precision, 95.5% [95% CI, 94.4%-96.5%]; and recall, 99.2% [95% CI, 98.7%-99.6%]), low-level response to joint attention (RJA) (AUROC, 99.8% [95% CI, 99.6%-99.9%]; accuracy, 98.8% [95% CI, 98.4%-99.2%]; precision, 98.9% [95% CI, 98.3%-99.4%]; and recall, 99.1% [95% CI, 98.6%-99.5%]), and high-level RJA (AUROC, 99.5% [95% CI, 99.2%-99.8%]; accuracy, 98.4% [95% CI, 97.9%-98.9%]; precision, 98.8% [95% CI, 98.2%-99.4%]; and recall, 98.6% [95% CI, 97.9%-99.2%]). The DL-based ASD symptom severity models showed reasonable predictive performance for IJA (AUROC, 90.3% [95% CI, 88.8%-91.8%]; accuracy, 84.8% [95% CI, 82.3%-87.2%]; precision, 76.2% [95% CI, 72.9%-79.6%]; and recall, 84.8% [95% CI, 82.3%-87.2%]), low-level RJA (AUROC, 84.4% [95% CI, 82.0%-86.7%]; accuracy, 78.4% [95% CI, 75.0%-81.7%]; precision, 74.7% [95% CI, 70.4%-78.8%]; and recall, 78.4% [95% CI, 75.0%-81.7%]), and high-level RJA (AUROC, 84.2% [95% CI, 81.8%-86.6%]; accuracy, 81.0% [95% CI, 77.3%-84.4%]; precision, 68.6% [95% CI, 63.8%-73.6%]; and recall, 81.0% [95% CI, 77.3%-84.4%]).Conclusions and relevanceIn this diagnostic study, DL models for identifying ASD and differentiating levels of ASD symptom severity were developed and the premises for DL-based predictions were visualized. The findings suggest that this method may allow digital measurement of joint attention; however, follow-up studies are necessary for further validation.

Project description:Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) are two of the most represented neurodevelopmental conditions in childhood. The diagnostic shift introduced by the DSM-5, allowing a combined diagnosis of ADHD and ASD, poses different clinical challenges related to diagnostic overshadowing, accuracy of clinical judgment and potential delay in an ASD diagnosis in children presenting with ADHD. Here we tried to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autism traits and empathy, by comparing children with ASD with and without comorbid ADHD with children presenting ADHD only and children with typical development. The child versions of the Autism Quotient (C-AQ) and Empathy Quotient (C-EQ) were administered to a total sample of 198 male children between 6 and 14 years old with age appropriate language skills and normal intelligence. Univariate analysis demonstrated no significant differences in the C-AQ total and subscale scores as well as the C-EQ between children with ASD and children with ASD + ADHD, while children with ADHD alone presented an intermediate phenotype between ASD and TD. Furthermore, a receiver operating characteristic (ROC) analysis was applied to discriminate among the different phenotypes. We found that the C-AQ and C-EQ were accurate at distinguishing with satisfactory reliability between: (a) ASD vs. non- ASD (N-ASD) groups comprising both ADHD and TD children (Area Under the Curve AUC 88% for C-AQ and 81% for C-EQ); (b) ASD and TD (AUC 92% for C-AQ and 95% for C-EQ); (c) ASD and ADHD (AUC 80% for C-AQ and 68% for C-EQ). Our data confirm the reliability of the C-AQ and C-EQ as behavioral markers to differentiate ASD (regardless of comorbid ADHD) from an ADHD condition and TD. Interestingly, in our sample an ADHD condition does not increase the severity of the clinical phenotype in terms of autism traits distribution and empathy, suggesting that the psychological measures detected by the two quantitative instruments are independent of ADHD traits. This evidence will contribute to the translational efforts in developing better tailored treatments and preventive strategies.

Project description:Although reduced social attention and increased nonsocial attention have been reported in individuals with autism spectrum disorder (ASD), the studies have relied on predominantly male samples and have been underpowered to examine sex differences. These processes may differ for females with ASD, who have been shown to be dissimilar to males in social motivation and nonsocial features, including circumscribed interests (CI). The goal of this study was to compare social and nonsocial visual attention between males and females with ASD on a validated eye-tracking paradigm. Eighty-five school-aged (6-10 years) males and females with and without ASD completed a paired preference task of face and object stimuli (half of which related to common CI). After covarying for chronological and mental age, the presence of concurrently presented CI images reduced prioritization and attention to faces for males more than females, replicating previous findings. ASD females maintained comparable attention patterns to typically developing females, suggesting that previous findings of reduced social attention and increased attention to CI-related objects in autism may be specific to males. These findings are also inconsistent with the "extreme male brain" theory of autism. The more normative orienting and attention to social stimuli for females with ASD may indicate distinct phenotypic characteristics relative to males and possibly serve as a protective effect. Autism Res 2018, 11: 1264-1275. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: As autism is more commonly diagnosed in males, less is known about females with autism. Two areas of interest include the interests held by individuals with autism and how socially motivated they are. We used eye tracking as a way to understand these two areas. Our data reveal that elementary school-aged females (6-10 years) with autism attended to faces comparatively to females without autism, suggesting that (1) they were more socially motivated than males with autism and (2) the images of common interests were less motivating to them.

Project description:Background and aimsResponse to joint attention (RJA) and initiation of joint attention (IJA) are impaired in pre-schoolers with autism spectrum disorder (ASD). Children's response to joint attention may depend on the presence of the target in the child's field of vision or on the type of deictic index (head and eye orientation, pointing and verbalisations) used by the adult to initiate joint attention.MethodsThis study deals with 50 ASD children aged from 2 years 8 months to 11 years 7 months, with a communicative level comparable to children under 31 months of age, according to the French version of the Early Social Communication Scales (ECSP, by its French acronym). We compared the aforementioned ASD children with 50 typically developing (TD) children, aged 9 to 30 months who had no communication disorders according to the ECSP. During the ECSP test, we analysed joint attention behaviours on three posters present or absent from the children's visual field.ResultsWe did not observe any difference in the number of IJAs between groups, but ASD children were less responsive than TD children were. Our results showed a developmental progression in the responses of children with ASD if several deictic cues were used by an adult simultaneously (looking and pointing, or looking and verbalising to indicate a target), whether the referent was present or absent from the child's visual field. In addition, we observed developmental progression when the referent was behind the child and the adult only used their gaze to refer to it.ConclusionThus, we argue that the type of cues used affects ASD children's response behaviours during joint attention towards a referent that may be present or absent from their visual field.Implications: Regarding the social and the sensory difficulties of children with ASD, many therapeutic approaches focus their intervention methods on non-verbal communication skills and joint attention. This fundamental research makes it possible to identify the most appropriate type of deictic index for children with ASD with developmental delay, depending on the presence or absence of the referent in the child's visual field.

Dataset Information

Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data.

Background

Methods

Conclusions

Trial registration

Publications

Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data.

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