Unravelling socio-motor biomarkers in schizophrenia

Piotr Slowinski, Francesco Alderisio, Chao Zhai, Yuan Shen, Peter Tino, Catherine Bortolon, Delphine Capdevielle, Laura Cohen, Mahdi Khoramshahi, Aude Billard, Robin Salesse, Mathieu Gueugnon, Ludovic Marin, Benoit G. Bardy, Mario Di Bernardo, Stephane Raffard, Krasimira T Tsaneva-Atanasova

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)
296 Downloads (Pure)

Abstract

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 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.
Original languageEnglish
Article number8
Number of pages10
Journalnpj Schizophrenia
Volume3
Issue number8
DOIs
Publication statusPublished - 1 Feb 2017

Keywords

  • Journal Article

Fingerprint Dive into the research topics of 'Unravelling socio-motor biomarkers in schizophrenia'. Together they form a unique fingerprint.

Cite this