Can pupil size and pupil responses during visual scanning contribute to the diagnosis of autism spectrum disorder in children?

International audienc

Back to Basic: Do Children with Autism Spontaneously Look at Screen Displaying a Face or an Object?

International audienceEye-tracking studies on exploration of faces and objects in autism provided important knowledge but only in a constraint condition (chin rest, total time looking at screen not reported), without studying potential differences between subjects with autism spectrum disorder (ASD) and controls in spontaneous visual attention toward a screen presenting these stimuli. This study used eye tracking to compare spontaneous visual attention to a screen displaying a face or an object between children with autism and controls in a nonconstraint condition and to investigate the relationship with clinical characteristics in autism group. Time exploring screen was measured during passive viewing of static images of faces or objects. Autistic behaviors were assessed by the CARS and the BSE-R in autism group. In autism group, time exploring face screen and time exploring object screen were lower than in controls and were not correlated with degree of distractibility. There was no interaction between group and type of image on time spent exploring screen. Only time exploring face screen was correlated with autism severity and gaze impairment. Results highlight particularities of spontaneous visual attention toward a screen displaying faces or objects in autism, which should be taken into account in future eye-tracking studies on face exploration

The pupil: a window on social automatic processing in autism spectrum disorder children

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The power of combining oculometric and pupillometric parameters for autism screening in children

Autism Spectrum Disorder is a neurodevelopmental trouble for which no objective biomarker has yet been discovered. The search for an accessible biomarker aims, in particular, for early autism screening, in order to optimize tailored intervention when necessary. In this context, eye-tracking has been now used for numerous years in the field of research on autism as it allows for non-intrusive, no-contact recordings even in very young children. However, individual oculometric parameters, while showing significant differences between groups of autistic and non-autistic individuals, are not discriminative enough for individual screening. In this study, we combined oculometric measures with pupillary parameters obtained simultaneously by the eye-tracker, and used a machine-learning approach to estimate the discriminative performance of such combinations of parameters. Data were obtained in 72 autistic and 93 neurotypical 2-13 years old children observing objects and faces during less than a minute. We used the Weka datamining software, testing 36 machine-learning algorithms without any a priori, in order to describe robust and convergent performance. Moreover, we chose to report only performance associated with high sensitivity, specificity and accuracy. We showed that oculo-pupillometric combinations of parameters allowed to reach outstanding discriminative performance in young children, paving the way for a clinical use