1,720,985 research outputs found
Investigating the Determinants of Social Communication Differences in Neurodevelopmental Disorders
Get PDFSocial communication differences can negatively impact long-term outcomes for children and youth with autism spectrum disorder (ASD). There is evidence to suggest that demographic factors, IQ, sensory processing, attention, language, and repetitive, restrictive behaviours contribute to social communication. However, the determinants of social communication are studied independently of each other, and there is little consideration for the interplay between them. To address this literature gap, we used structural equation modelling techniques to understand the differential contribution of these factors to social communication in a combined sample of individuals with ASD, attention deficit hyperactivity disorder, as well as typically developing individuals. We found that a combination of factors, rather than one singular factor, contributed to social communication differences. Ultimately, by understanding the factors that contribute to social communication, we hope to be able to target specific skills or behaviours and to create personalized supports for individuals with ASD.M.A.S
Development of a real-time algorithm for the detection of physiological arousal in the presence of motion among children with autism
Get PDFAnxiety is a clinical concern among some children with autism, for whom such an emotional onset may exacerbate core symptoms. Current treatment options are challenged as many of these children are unaware of their emotional state, and due to autism some children also have difficulties with language and communication which further complicates anxiety diagnoses. In this thesis, we proposed a novel automated anxiety detection algorithm capable of interpreting heart rate arousal in the presence of physical movements , and intuitively notifying the user to engage in anxiety management techniques. The novelty of our proposed algorithm is based on its ability to detect anxiety in presence of motion; this is a challenge as motion and anxiety influence the heart rate similarly. This algorithm was able to account for the changes in heart rate due to motion and detection anxiety with an accuracy, specificity and sensitivity of 92.7% in children with autism.M.H.Sc
Examining Safety and Usability of Virtual Reality for Children with Autism Spectrum Disorder (ASD)
No full textAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication difficulties and the presence of repetitive behaviors and restricted interests. Virtual Reality (VR) systems have highly desirable features for delivering interventions to individuals with ASD. In particular, they can offer high levels of authenticity and realism, which in turn, can improve the ecological validity of interventions. They also provide a relatively inexpensive way to practice and learn skills in a personalized, controlled, and safe setting. There is, however, a significant gap in understanding how VR environments are experienced by individuals with ASD. To address this, the objectives of this study were to (1) evaluate the safety and usability of head-mounted VR for children with ASD, and (2) discover demographic and phenotypic predictors of VR usability and safety in ASD. Ultimately, the findings can inform the design of personalized VR-based interventions for this population.M.A.S
Characterizing the Association between Brain Morphology and Behavioural Symptomatology in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder
No full textAutism spectrum disorder (ASD) and attention decit/hyperactivity disorder (ADHD) are prevalent and highly co-occurring neurodevelopmental disorders. The brain correlates of these disorders remain mostly unknown. This is partly due to the limitations of existing analytic methods in coping with the large within disorder variability and overlap between disorders. To address this challenges, we propose a new method called Bagged-Regression clustering for data-driven discovery of diagnosis-agnostic subgroups that may share brain-behaviour associations. This approach clusters the sample data into K groups, each with its own linear regression function. Using both simulated data and a real-dataset of brain-behaviour associations in ASD and ADHD, we show that the proposed method is able to recover multiple regression lines in the data.M.A.S
Development of a real-time algorithm for the detection of physiological arousal in the presence of motion among children with autism
No full textAnxiety is a clinical concern among some children with autism, for whom such an emotional onset may exacerbate core symptoms. Current treatment options are challenged as many of these children are unaware of their emotional state, and due to autism some children also have difficulties with language and communication which further complicates anxiety diagnoses. In this thesis, we proposed a novel automated anxiety detection algorithm capable of interpreting heart rate arousal in the presence of physical movements , and intuitively notifying the user to engage in anxiety management techniques. The novelty of our proposed algorithm is based on its ability to detect anxiety in presence of motion; this is a challenge as motion and anxiety influence the heart rate similarly. This algorithm was able to account for the changes in heart rate due to motion and detection anxiety with an accuracy, specificity and sensitivity of 92.7% in children with autism.M.H.Sc
Physiological Detection of Emotional States in Children with Autism Spectrum Disorder (ASD)
Get PDFAutism spectrum disorder (ASD) is associated with difficulties in emotion processing including attributing emotional states to others and processing of oneâ s own emotional experiences. These difficulties are linked to core social impairments and increased severity of psychiatric co-morbidities such as depression in ASD. The nature of these difficulties has remained largely unknown. This is partially due to limitations in obtaining reliable self report of emotional experiences in this population.
Emotion detection using physiological signals is a promising direction in addressing this limitation. Physiological signals can provide an objective language free method for understanding emotional states in ASD. Despite this promise the use of this approach has not been studied in ASD.
To this end we develop a physiological approach to detection of emotion in children with ASD. We showed that emotional states can be classified with accuracies>80% in a sample of children with ASD which affirms the feasibility of discriminating affective states in this population.M.A.S
Investigating the Contributions of Physiological Arousal and Emotion Regulation to Anxiety in Autism Spectrum Disorder
Get PDFUp to 84% of children with autism spectrum disorder (ASD) experience clinically significant anxiety, negatively impacting their academic achievement, employment outcomes, and health. Despite its significance, few evidence-based interventions exist for anxiety in ASD. A key challenge is that underlying factors remain largely unknown. Two mechanisms suggested to underlie anxiety in ASD are difficulties with emotion regulation (ER) and atypical physiological arousal.I investigate physiological arousal, indexing overall arousal and parasympathetic nervous system activity, during resting state and emotionally-arousing tasks in a sample of children with ASD as well as a typically developing (TD) sample. I then explore associations between arousal and ER in these samples. Finally, I test potential relational models explaining associations between arousal, ER, and anxiety. Results display differences in arousal within the ASD sample, associations between arousal and ER, as well as initial support for the proposed models explaining anxiety in ASD.M.A.S
Characterizing the Association between Brain Morphology and Behavioural Symptomatology in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder
Get PDFAutism spectrum disorder (ASD) and attention decit/hyperactivity disorder (ADHD) are prevalent and highly co-occurring neurodevelopmental disorders. The brain correlates of these disorders remain mostly unknown. This is partly due to the limitations of existing analytic methods in coping with the large within disorder variability and overlap between disorders. To address this challenges, we propose a new method called Bagged-Regression clustering for data-driven discovery of diagnosis-agnostic subgroups that may share brain-behaviour associations. This approach clusters the sample data into K groups, each with its own linear regression function. Using both simulated data and a real-dataset of brain-behaviour associations in ASD and ADHD, we show that the proposed method is able to recover multiple regression lines in the data.M.A.S
Examining Safety and Usability of Virtual Reality for Children with Autism Spectrum Disorder (ASD)
Get PDFAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication difficulties and the presence of repetitive behaviors and restricted interests. Virtual Reality (VR) systems have highly desirable features for delivering interventions to individuals with ASD. In particular, they can offer high levels of authenticity and realism, which in turn, can improve the ecological validity of interventions. They also provide a relatively inexpensive way to practice and learn skills in a personalized, controlled, and safe setting. There is, however, a significant gap in understanding how VR environments are experienced by individuals with ASD. To address this, the objectives of this study were to (1) evaluate the safety and usability of head-mounted VR for children with ASD, and (2) discover demographic and phenotypic predictors of VR usability and safety in ASD. Ultimately, the findings can inform the design of personalized VR-based interventions for this population.M.A.S
Physiological Detection of Emotional States in Children with Autism Spectrum Disorder (ASD)
No full textAutism spectrum disorder (ASD) is associated with difficulties in emotion processing including attributing emotional states to others and processing of oneâ s own emotional experiences. These difficulties are linked to core social impairments and increased severity of psychiatric co-morbidities such as depression in ASD. The nature of these difficulties has remained largely unknown. This is partially due to limitations in obtaining reliable self report of emotional experiences in this population.
Emotion detection using physiological signals is a promising direction in addressing this limitation. Physiological signals can provide an objective language free method for understanding emotional states in ASD. Despite this promise the use of this approach has not been studied in ASD.
To this end we develop a physiological approach to detection of emotion in children with ASD. We showed that emotional states can be classified with accuracies>80% in a sample of children with ASD which affirms the feasibility of discriminating affective states in this population.M.A.S
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