The dynamics of Autism therapy with preschool children: quantitative observation and computational methods

Clinical and research practice in the context of Autism rapidly evolved in the last decades. Finer diagnostic procedures, evidence-based models of intervention and higher social inclusivity significantly improved the possibility for autistic children to participate in the fabric of social life. In terms of health best practices, gold-standard procedures still need to be improved, and bridging research and clinical practice still presents several challenges. From the clinical standpoint, the role of process variables, predictors, mechanisms, and timing of change still requires extensive investigation in order to explain response variability and design optimized interventions, tailored to individual needs and maximally effective. Observational techniques represent the elective research methods in child development, especially in clinical contexts, due to their non-invasiveness. However, they still suffer from limited objectivity and poor quantification. Further, their main disadvantage is that they are highly time-consuming and labor-intensive. The aim of this thesis was moving forward to promote translational research in clinical practice of Autism intervention with preschool children. At first, we tried to design and apply quantitative observational techniques to longitudinally study treatment response trajectories during developmental intervention. We tried to characterize different response profiles, and which baseline predictors were able to predict the response over time. Secondly, we investigated mechanisms of change. In particular, we focused on the role of the child-therapist interaction dynamics as a possible active mediator of the process of intervention, especially in the developmental framework that stresses the importance of interpersonal aspects. We also aimed at understanding whether certain time-windows during the intervention were particularly predictive of the response, as well as which specific interaction aspects played a role. Finally, to promote the translational application of observational methods and to improve objective quantification, we proposed and validated an Artificial Intelligence (AI) system to automate data annotation in unconstrained clinical contexts, remaining completely non-invasive and dealing with the specific noisy data that characterize them, for the analysis of the child-therapist acoustic interaction. This effort represents a base building block enabling to employ downstream computational techniques greatly reducing the need for human annotation that usually prevents the application of observational research to large amounts of data . We discuss our findings stressing the importance of assuming a developmental framework in Autism, the key role of the interpersonal experience also in the clinical context, the importance of focusing on trajectories of change and the important need to promote the acquisition of large amounts of quantitative data from the clinical contexts exploiting AI-based systems to assist clinicians, improving objectivity, enabling treatment monitoring, and producing precious data-driven knowledge on treatment efficacy.