Patterns of Restricted and Repetitive Behaviors in Autism Spectrum Disorders: A Cross-Sectional Video Recording Study. Preliminary Report

Grossi, Enzo; Caminada, Elisa; Goffredo, Michela; Vescovo, Beatrice; Castrignano, Tristana; Piscitelli, Daniele; Valagussa, Giulio; Franceschini, Marco; Vanzulli, Franco

doi:10.3390/brainsci11060678

Cited by 19 publications

(15 citation statements)

References 54 publications

(69 reference statements)

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“…When it comes to functionality level, our findings are consistent with previous research (Grossi et al, 2021).…”

Section: Resultssupporting

confidence: 92%

“…However, statistically significant differences were found between participants of different genders (U = 97.00, p = .09), where male participants exhibit more auto stimulatory behaviors (Mdn = 34.00) in comparison with female participants (Mdn = 23.00). Also, statistically significant differences were found between participants with different functionality levels (U = 116.50, p = .03), where participants with low functioning ASD exhibit more auto stimulatory behaviors (Mdn = 41.00) in comparison with participants with high functioning ASD (Mdn = Grossi with his co-workers (Grossi et al, 2021) claim that gender was not associated with prevalence of auto stimulatory behavior in individuals with ASD. Same findings were reported by Freeman et al (2010).…”

Section: Resultsmentioning

confidence: 95%

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Characteristics and predictors of auto-stimulatory behavior in children with autism

Lukić

Arsić

Gajić

2023

Int. J. Edu. Inno. Res.

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The aim of this research was to examine characteristics and predictors of auto stimulatory behavior in children with autism spectrum disorders (ASD), as well as to determine the presence of auto stimulatory behavior exhibited by children with ASD. The sample included 43 participants diagnosed with ASD. The instrument used was Behavior Problems Inventory (BPI-01, Rojahn et al., 2002), subscale for stereotyped behavior. Our results show that children with ASD from our sample have lower rates of auto stimulatory behavior, with the most common ones being repetitive body movements, arm shaking, bouncing around, running and pacing. The school type has not proven to be a predictor of auto stimulatory behaviors. However, male participants and those with low functioning ASD exhibit more auto stimulatory behaviors than females and those with high functioning ASD. The presence of auto stimulatory behaviors persists through age. Auto-stimulatory behaviors exhibited by children with ASD can be reduced or eliminated, however it is important to use evidence-based interventions with proven benefits for the child. Since stereotypy is usually automatically maintained and therefore is one of the behaviors that is often difficult to reduce, it is recommended that parents are taught on how to implement interventions in their home environment.

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“…When it comes to functionality level, our findings are consistent with previous research (Grossi et al, 2021).…”

Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 95%

Characteristics and predictors of auto-stimulatory behavior in children with autism

Lukić

Arsić

Gajić

2023

Int. J. Edu. Inno. Res.

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“…Associations of ADOS and SRS scores with DTI indices were much stronger in adults than in youth with ASD (Figures and S24), suggesting that microstructural tissue disturbances in frontostriatal pathways may contribute to more severe clinical ASD presentations in adulthood. ADOS Restricted and Repetitive Behaviour scores were relatively low in our sample (Table 1), which has been reported previously in higher functioning persons with ASD (Grossi et al., 2021; Leekam, Prior, & Uljarevic, 2011); the low values and restricted range of these scores may have contributed to their weak associations with DTI measures (Figure ).…”

Section: Discussionsupporting

confidence: 73%

Using tissue microstructure and multimodal MRI to parse the phenotypic heterogeneity and cellular basis of autism spectrum disorder

Peterson

Liu

Dantec

et al. 2021

Child Psychology Psychiatry

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Background Identifying the brain bases for phenotypic heterogeneity in Autism Spectrum Disorder (ASD) will advance understanding of its pathogenesis and improve its clinical management. Methods We compared Diffusion Tensor Imaging (DTI) indices and connectome measures between 77 ASD and 88 Typically Developing (TD) control participants. We also assessed voxel‐wise associations of DTI indices with measures of regional cerebral blood flow (rCBF) and N‐acetylaspartate (NAA) to understand how tissue microstructure associates with cellular metabolism and neuronal density, respectively. Results Autism Spectrum Disorder participants had significantly lower fractional anisotropy (FA) and higher diffusivity values in deep white matter tracts, likely representing ether reduced myelination by oligodendrocytes or a reduced density of myelinated axons. Greater abnormalities in these measures and regions were associated with higher ASD symptom scores. Participant age, sex and IQ significantly moderated these group differences. Path analyses showed that reduced NAA levels accounted significantly for higher diffusivity and higher rCBF values in ASD compared with TD participants. Conclusions Reduced neuronal density (reduced NAA) likely underlies abnormalities in DTI indices of white matter microstructure in ASD, which in turn are major determinants of elevated blood flow. Together, these findings suggest the presence of reduced axonal density and axonal pathology in ASD white matter. Greater pathology in turn accounts for more severe symptoms, lower intellectual ability, and reduced global efficiency for measures of white matter connectivity in ASD.

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“…1). Two second duration was considered based on the clinical literature on stimming behavior recommending observation of a subject for a period of 1 to 3 seconds for diagnosing atypical behavior [30][31][32][33][34].…”

Section: Methodsmentioning

confidence: 99%

Automated Anomalous Child Repetitive Head Movement Identification through Transformer Networks

Wedasingha

Samarasinghe

Seneviratne

et al. 2023

Preprint

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The exponential growth of behavioral disorders among human beings is an increasing concern in the medical community due to a lack of medical resources for early identification of atypical behaviors. Many psychological journals which address behavioral disorders indicate that the most prominent way of puzzling out this problem is to identify behavioral disorder characteristics such as repetitive behaviors in early childhood, and recover through therapy. Even though there are many standards presently used in diagnosing behavioral disorder characteristics, due to a lack of facilities and limited number of professionals in the relevant fields, these traditional standards have failed to cater the increasing number of cases with behavioral disorders. Hence, the need for developing automated approaches to overcome the problems in current systems of diagnosing children with behavioral disorders has arisen from the research community. Therefore, the purpose of this study is to develop an automated model that analyzes a video to distinguish typical, and atypical repetitive head movements of children while using different learning methods to mitigate issues that affect the performance of the model due to the scarcity of child datasets. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on the analysis of gender, age, the type of the repetitive head movement along with count, duration, and frequency of each repetitive head movement. Different transfer learning methods were experimented to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD Dataset, SSBD dataset, and head movements in the wild dataset, corroborate our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements of children.

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Patterns of Restricted and Repetitive Behaviors in Autism Spectrum Disorders: A Cross-Sectional Video Recording Study. Preliminary Report

Cited by 19 publications

References 54 publications

Characteristics and predictors of auto-stimulatory behavior in children with autism

Characteristics and predictors of auto-stimulatory behavior in children with autism

Using tissue microstructure and multimodal MRI to parse the phenotypic heterogeneity and cellular basis of autism spectrum disorder

Automated Anomalous Child Repetitive Head Movement Identification through Transformer Networks

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