Temporal Behavioral Parameters of On-Going Gaze Encounters in a Virtual Environment

Impaired recognition of interactive intentions in adults with autism spectrum disorder not attributable to differences in visual attention or coordination via eye contact and joint attention

Altered nonverbal communication patterns especially with regard to gaze interactions are commonly reported for persons with autism spectrum disorder (ASD). In this study we investigate and differentiate for the first time the interplay of attention allocation, the establishment of shared focus (eye contact and joint attention) and the recognition of intentions in gaze interactions in adults with ASD compared to control persons. Participants interacted via gaze with a virtual character (VC), who they believed was controlled by another person. Participants were instructed to ascertain whether their partner was trying to interact with them. In fact, the VC was fully algorithm-controlled and showed either interactive or non-interactive gaze behavior. Participants with ASD were specifically impaired in ascertaining whether their partner was trying to interact with them or not as compared to participants without ASD whereas neither the allocation of attention nor the ability to establish a shared focus were affected. Thus, perception and production of gaze cues seem preserved while the evaluation of gaze cues appeared to be impaired. An additional exploratory analysis suggests that especially the interpretation of contingencies between the interactants' actions are altered in ASD and should be investigated more closely.

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses

BACKGROUND

Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders.

METHOD

We used a well-characterized clinical sample of individuals (n = 1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n = 481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n = 122), ADHD (n = 439), and conduct disorder (CD, n = 194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice.

RESULTS

We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses.

CONCLUSIONS

ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult.