Machine Learning to Study Social Interaction Difficulties in ASD

Abstract

Autism Spectrum Disorder (ASD) is a spectrum of neurodevelopmental conditions characterized by difficulties in social communication and social interaction as well as repetitive behaviors and restricted interests. Prevalence rates have been rising, and existing diagnostic methods are both extremely time and labor consuming. There is an urgent need for more economic and objective automatized diagnostic tools that are independent of language and experience of the diagnostician and that can help deal with the complexity of the autistic phenotype. Technological advancements in machine learning are offering a potential solution, and several studies have employed computational approaches to classify ASD based on phenomenological, behavioral or neuroimaging data. Despite of being at the core of ASD diagnosis and having the potential to be used as a behavioral marker for machine learning algorithms, only recently have movement parameters been used as features in machine learning classification approaches. In a proof-of-principle analysis of data from a social interaction study we trained a classification algorithm on intrapersonal synchrony as an automatically and objectively measured phenotypic feature from 29 autistic and 29 typically developed individuals to differentiate those individuals with ASD from those without ASD. Parameters included nonverbal motion energy values from 116 videos of social interactions. As opposed to previous studies to date, our classification approach has been applied to non-verbal behavior objectively captured during naturalistic and complex interactions with a real human interaction partner assuring high external validity. A machine learning approach lends itself particularly for capturing heterogeneous and complex behavior in real social interactions and will be essential in developing automatized and objective classification methods in ASD.

Children ASD Evaluation Through Joint Analysis of EEG and Eye-Tracking Recordings With Graph Convolution Network

“…This means that these three features differ the most in children with ASD and TD, and (2) among the eye-tracking features, ASD children and TD children have the largest difference in the feature of strange faces in their home countries, which coincides with previous studies. Second, there are obvious differences in social interaction between ASD and TD children, which is consistent with the existing researches, that is, ASD is characterized by difficulties in social communication and social interaction as well as repetitive behaviors and restricted interests (Georgescu et al, 2019 ). The proposed method used the adjacency matrix (see Figure 4 ) to input of network structure and perform GCN model training, where the ratio of training set against test samples is 8:2.…”

INTRApersonal Synchrony as Constituent of INTERpersonal Synchrony and Its Relevance for Autism Spectrum Disorder

“…Time series data of motion patterns may be used for diagnostic purposes, e.g. supported by machine learning (Georgescu et al, 2019 ). Our recent work shows that automatized classification of ASD from non-ASD is possible on the mere basis of motion energy assessed using video analysis (ibid.).…”

Automating autism assessment: What AI can bring to the diagnostic process

“…Given these complications, a lot of power may be ceded to the programming of the AI device and its capacity to draw robust inferences about behaviour, normal variation and pathology. 41,42 In this kind of context, a space is opened that would diminish the role of the clinical psychiatrist. 43 What's more, as we note above, where assessment of adults is at stake, matters are further complicated in that they are likely to have developed behavioural strategies such that clinically relevant factors (eg, attention, verbosity, affect) might be masked through experiential learning.…”

“…Given these complications, a lot of power may be ceded to the programming of the AI device and its capacity to draw robust inferences about behaviour, normal variation and pathology 41,42 . In this kind of context, a space is opened that would diminish the role of the clinical psychiatrist 43 .…”