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"Dawson, Geraldine"
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Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder
Advances in the fields of cognitive and affective developmental neuroscience, developmental psychopathology, neurobiology, genetics, and applied behavior analysis have contributed to a more optimistic outcome for individuals with autism spectrum disorder (ASD). These advances have led to new methods for early detection and more effective treatments. For the first time, prevention of ASD is plausible. Prevention will entail detecting infants at risk before the full syndrome is present and implementing treatments designed to alter the course of early behavioral and brain development. This article describes a developmental model of risk, risk processes, symptom emergence, and adaptation in ASD that offers a framework for understanding early brain plasticity in ASD and its role in prevention of the disorder.
Journal Article
A parent's guide to high-functioning autism spectrum disorder : how to meet the challenges and help your child thrive
\"Packed with real-life stories and everyday problem-solving ideas, this book has given many tens of thousands of parents the facts they need about high-functioning autism spectrum disorder (ASD), including Asperger syndrome. The authors are leading experts who describe ways to work with these kids' unique impairments and capabilities so they can grow into happy, self-sufficient adults. Parents learn practical strategies for helping their son or daughter relate more comfortably to peers, learn the rules of appropriate behavior, and succeed in school. The book also discusses what scientists currently know about ASD and how it is diagnosed, as well as what treatments and educational supports have been shown to work. Updated with the latest research, resources, and clinical strategies, the second edition clearly explains the diagnostic changes in DSM-5\"-- Provided by publisher.
Book
A quarter century of progress on the early detection and treatment of autism spectrum disorder
The last 25 years have witnessed tremendous changes in our ability to detect autism very early in life and provide interventions that can significantly influence children's outcomes. It was once questioned whether autism could be recognized before children had developed language and symbolic play skills; now changes in early behaviors, as well as structural brain changes, have been documented in infants 6–12 months of age who later develop autism. Advances in brain imaging and genetics offer the possibility of detecting autism before the syndrome is fully manifest, thereby reducing or preventing symptoms from developing. Whereas the primary mode of behavioral intervention a few decades ago relied on operant conditioning, recent approaches integrate the methods of applied behavioral analysis within a developmental, relationship-focused intervention model that are implemented by both parents and clinicians. These interventions have been found to have positive effects on children's developmental trajectory, as measured by both behavioral and neurophysiological assessments. Future approaches will likely combine both behavioral and pharmacological treatments for children who have less robust responses to behavioral interventions. There has been a paradigm shift in the way that autism is viewed, evolving from a lifelong condition with a very poor prognosis to one in which significant gains and neuroplasticity is expected, especially when the condition is detected early and appropriate interventions are provided. The grand challenge for the future is to bridge the tremendous gap between research and the implementation of evidence-based practices in the broader community, both in the United States and worldwide. Significant disparities in access to appropriate health care for children with autism exist that urgently require advocacy and more resources.
Journal Article
Naturalistic Developmental Behavioral Interventions: Empirically Validated Treatments for Autism Spectrum Disorder
Earlier autism diagnosis, the importance of early intervention, and development of specific interventions for young children have contributed to the emergence of similar, empirically supported, autism interventions that represent the merging of applied behavioral and developmental sciences. “Naturalistic Developmental Behavioral Interventions (NDBI)” are implemented in natural settings, involve shared control between child and therapist, utilize natural contingencies, and use a variety of behavioral strategies to teach developmentally appropriate and prerequisite skills. We describe the development of NDBIs, their theoretical bases, empirical support, requisite characteristics, common features, and suggest future research needs. We wish to bring parsimony to a field that includes interventions with different names but common features thus improving understanding and choice-making among families, service providers and referring agencies.
Journal Article
Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions
One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving the identification of high-specificity spatial proteomes. The resulting native proximity proteomes are enriched for human genes dysregulated in the brain of autistic individuals, and reveal proximity interactions between proteins from high-confidence risk genes with those of lower-confidence that may provide new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.
Protein interactions are essential for neural signaling and often perturbed in brain conditions. Here, the authors developed a CRISPR-based chemical-genetic approach to identify endogenous proximity proteomes that inform mechanism and phenotypic rescue strategies in mouse models of autism.
Journal Article
Patterns of Visual Attention to Faces and Objects in Autism Spectrum Disorder
This study used eye-tracking to examine visual attention to faces and objects in adolescents with autism spectrum disorder (ASD) and typical peers. Point of gaze was recorded during passive viewing of images of human faces, inverted human faces, monkey faces, three-dimensional curvilinear objects, and two-dimensional geometric patterns. Individuals with ASD obtained lower scores on measures of face recognition and social-emotional functioning but exhibited similar patterns of visual attention. In individuals with ASD, face recognition performance was associated with social adaptive function. Results highlight heterogeneity in manifestation of social deficits in ASD and suggest that naturalistic assessments are important for quantifying atypicalities in visual attention.
Journal Article
Use of computer vision analysis for labeling inattention periods in EEG recordings with visual stimuli
Electroencephalography (EEG) recordings with visual stimuli require detailed coding to determine the periods of participant’s attention. Here we propose to use a supervised machine learning model and off-the-shelf video cameras only. We extract computer vision-based features such as head pose, gaze, and face landmarks from the video of the participant, and train the machine learning model (multi-layer perceptron) on an initial dataset, then adapt it with a small subset of data from a new participant. Using a sample size of 23 autistic children with and without co-occurring ADHD (attention-deficit/hyperactivity disorder) aged 49–95 months, and training on additional 2560 labeled frames (equivalent to 85.3 s of the video) of a new participant, the median area under the receiver operating characteristic curve for inattention detection was 0.989 (IQR 0.984–0.993) and the median inter-rater reliability (Cohen’s kappa) with a trained human annotator was 0.888. Agreement with human annotations for nine participants was in the 0.616–0.944 range. Our results demonstrate the feasibility of automatic tools to detect inattention during EEG recordings, and its potential to reduce the subjectivity and time burden of human attention coding. The tool for model adaptation and visualization of the computer vision features is made publicly available to the research community.
Journal Article