Brief Report: Neuroimaging Endophenotypes of Social Robotic Applications in Autism Spectrum Disorder
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BRIEF REPORT
Brief Report: Neuroimaging Endophenotypes of Social Robotic Applications in Autism Spectrum Disorder Antonio Cerasa1,2 · Liliana Ruta3 · Flavia Marino3 · Giuseppe Biamonti1,3 · Giovanni Pioggia3
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract A plethora of neuroimaging studies have focused on the discovery of potential neuroendophenotypes useful to understand the etiopathogenesis of autism and predict treatment response. Social robotics has recently been proposed as an effective tool to strengthen the current treatments in children with autism. However, the high clinical heterogeneity characterizing this disorder might interfere with behavioral effects. Neuroimaging is set to overcome these limitations by capturing the level of heterogeneity. Here, we provide a preliminary evaluation of the neural basis of social robotics and how extracting neural hallmarks useful to design more effective behavioral applications. Despite the endophenotype-oriented neuroimaging research approach is in its relative infancy, this preliminary evidence encourages innovation to address its current limitations. Keywords Autism spectrum disorder · Neuroimaging · Social robot · Neuroendophenotype
Introduction Autism spectrum disorder (ASD) is a set of heterogeneous neurodevelopmental conditions, characterized by early-onset difficulties in social communication and restricted, stereotyped behaviors (Lai et al. 2014). The worldwide estimated prevalence of ASD was 18.5 per 1.000 (one in 54) children aged 8 years (Maenner et al. 2020). Unfortunately, individuals with ASD show marked heterogeneity at genetic, behavioral, aetiological, and pathophysiological levels. Despite ASD is a neurobiological disorder, behavioral trials are currently recognized as the main treatments for such individuals because these are usually focused on maximizing the ability in social skills and communication, reduce disability and comorbidity and promote independence (Fedotchev et al. 2019). However, it has been demonstrated that the well-known clinical heterogeneity characterizing individuals with ASD might interfere with the efficacy of several * Antonio Cerasa [email protected] 1
Institute for Biomedical Research and Innovation (IRIB), National Research Council, C/Da Burga, Cosenza, Mangone 87050, Italy
2
S. Anna Institute, Crotone 88900, Italy
3
Institute for Biomedical Research and Innovation (IRIB), Nationasssl Research Council, Messina 98164, Italy
intervention programs, such as Applied Behavior Therapy (ABA) (Virués-Ortega 2010; Maglione et al. 2012). ABA is considered an evidence-based best practice treatment to improve outcomes for many children with autism (American Psychological Association 2017). In recent years, significant progress has been made in understanding the multidimensional nature and complex mechanisms of ASD, as well as in delineating different trajectories in this condition (Masi et al. 2017). A significant amount of data has been obtained using advanced technologies s
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