Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)

Hanna Drimalla; Tobias Scheffer; Niels Landwehr; Irina Baskow; Stefan Roepke; Behnoush Behnia; Isabel Dziobek

doi:10.1038/s41746-020-0227-5

Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)

NPJ Digit Med. 2020 Feb 28:3:25. doi: 10.1038/s41746-020-0227-5. eCollection 2020.

Authors

Hanna Drimalla^{1

2

3}, Tobias Scheffer⁴, Niels Landwehr^{4

5}, Irina Baskow^{1

6}, Stefan Roepke⁶, Behnoush Behnia^#⁶, Isabel Dziobek^#^{1

2}

Affiliations

¹ 1Department of Psychology, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.
² 2Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.
³ 3Digital Health Center, Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany.
⁴ 4Institute of Computer Science, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany.
⁵ Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany.
⁶ 6Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin, Germany.

^# Contributed equally.

Abstract

Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings.

Keywords: Autism spectrum disorders; Diagnosis; Signs and symptoms.