Unravelling socio-motor biomarkers in schizophrenia

Piotr Slowiński (1) , Francesco Alderisio (2) , Chao Zhai (2) , Yuan Shen (3) , Peter Tino (3) , Catherine Bortolon (4, 5) , Delphine Capdevielle (6, 4) , Laura Cohen (7) , Mahdi Khoramshahi (7) , Aude Billard (8) , Robin Salesse (9) , Mathieu Gueugnon (9) , Ludovic Marin (9) , Benoît G. Bardy (9) , Mario Di Bernardo (10, 11) , Stephane Raffard (12, 4) , Krasimira Tsaneva-Atanasova (1, 1)

1 University of Exeter
2 University of Bristol [Bristol]
3 University of Birmingham [Birmingham]
4 CHRU Montpellier - Centre Hospitalier Régional Universitaire [Montpellier]
5 UM - Université de Montpellier
6 PSNREC - Neuropsychiatrie : recherche épidémiologique et clinique
7 LASA - Learning Algorithms and Systems Laboratory
8 EPFL - Ecole Polytechnique Fédérale de Lausanne
9 EuroMov - Euromov
10 UWE Bristol - University of the West of England [Bristol]
11 University of Naples Federico II = Università degli studi di Napoli Federico II
12 EPSYLON - Dynamique des capacités humaines et des conduites de santé

We present novel, low-cost and non-invasive potential diagnostic biomarkers of schizophrenia. They are based on the‘mirror-game’, a coordination task in which two partners are asked to mimic each other’s hand movements. In particular, we use thepatient’s solo movement, recorded in the absence of a partner, and motion recorded during interaction with an artificial agent, acomputer avatar or a humanoid robot. In order to discriminate between the patients and controls, we employ statistical learning techniques, which we apply to nonverbal synchrony and neuromotor features derived from the participants’movement data. The proposed classifier has 93% accuracy and 100% specificity. Our results provide evidence that statistical learning techniques, nonverbal movement coordination and neuromotor characteristics could form the foundation of decision support tools aiding clinicians in cases of diagnostic uncertainty.

Domaines: Psychiatrie et santé mentale; Neurosciences; Santé publique et épidémiologie; Logiciel mathématique [cs.MS]

Piotr Slowiński, Francesco Alderisio, Chao Zhai, Yuan Shen, Peter Tino, et al.. Unravelling socio-motor biomarkers in schizophrenia. npj Schizophrenia, 2017, 3, pp.8. ⟨10.1038/s41537-016-0009-x⟩. ⟨hal-01761364⟩