Yuan Shen, Mahdi Khoramshahi, Aude Billard, Peter Tino, Mathieu Gueugnon, Stéphane Raffard, Laura Cohen, Robin N. Salesse, Catherine Bortolon, Delphine Capdevielle, Benoît G. Bardy, Francesco Alderisio, Krasimira Tsaneva-Atanasova, Piotr Słowiński, Chao Zhai, Ludovic Marin, Mario di Bernardo, Dynamique des capacités humaines et des conduites de santé (EPSYLON), Université de Montpellier (UM)-Université Paul-Valéry - Montpellier 3 (UPVM)-Université Montpellier 1 (UM1), Słowiński, Piotr, Alderisio, Francesco, Zhai, Chao, Shen, Yuan, Tino, Peter, Bortolon, Catherine, Capdevielle, Delphine, Cohen, Laura, Khoramshahi, Mahdi, Billard, Aude, Salesse, Robin, Gueugnon, Mathieu, Marin, Ludovic, Bardy, Benoit G., DI BERNARDO, Mario, Raffard, Stephane, Tsaneva Atanasova, Krasimira, University of Exeter, University of Bristol [Bristol], University of Birmingham [Birmingham], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Université de Montpellier (UM), Neuropsychiatrie : recherche épidémiologique et clinique (PSNREC), Université Montpellier 1 (UM1)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Learning Algorithms and Systems Laboratory (LASA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Euromov (EuroMov), University of the West of England [Bristol] (UWE Bristol), University of Naples Federico II, EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), roussel, pascale, University of Naples Federico II = Università degli studi di Napoli Federico II, and Université Montpellier 1 (UM1)-Université Paul-Valéry - Montpellier 3 (UPVM)-Université de Montpellier (UM)
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 the patient’s solo movement, recorded in the absence of a partner, and motion recorded during interaction with an artificial agent, a computer 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., Mirror game test could detect schizophrenia A new test of movement and social interaction could detect markers of schizophrenia, and help to diagnose and manage the condition. In an effort to establish reliable indicators of schizophrenia, Piotr Slowinski at the University of Exeter, UK and colleagues developed a test that could detect deficits in movement and social interactions, both characteristics of the disorder. They asked people to perform movements alone, and to mirror the movements of a computer avatar or a humanoid robot. Automated analysis of the movements allowed to distinguish people with schizophrenia from healthy participants with accuracy and specificity slightly better than clinical interviews and comparable to test based on much more expensive neuroimaging methods. The technique could help with diagnosis of schizophrenia and to monitor patients’ responses to treatment, but needs to be tested in clinical trials before being applied in clincal practice.