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Słowiński P, Alderisio F, Zhai C, Shen Y, Tino P, Bortolon C, Capdevielle D, Cohen L, Khoramshahi M, Billard A, Salesse R, Gueugnon M, Marin L, Bardy BG, di Bernardo M, Raffard S, Tsaneva-Atanasova K. Unravelling socio-motor biomarkers in schizophrenia. NPJ SCHIZOPHRENIA 2017; 3:8. [PMID: 28560254 PMCID: PMC5441525 DOI: 10.1038/s41537-016-0009-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 12/06/2016] [Accepted: 12/15/2016] [Indexed: 12/24/2022]
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 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.
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Affiliation(s)
- Piotr Słowiński
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF UK
| | - Francesco Alderisio
- Department of Engineering Mathematics, University of Bristol, Merchant Venturers’ Building, Exeter, BS8 1UB UK
| | - Chao Zhai
- Department of Engineering Mathematics, University of Bristol, Merchant Venturers’ Building, Exeter, BS8 1UB UK
| | - Yuan Shen
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Peter Tino
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Catherine Bortolon
- University Department of Adult Psychiatry, Hôpital de la Colombière, CHU Montpellier, Montpellier-1 University, Montpellier, France
| | - Delphine Capdevielle
- University Department of Adult Psychiatry, Hôpital de la Colombière, CHU Montpellier, Montpellier-1 University, Montpellier, France
- INSERM U-1061, Montpellier, France
| | - Laura Cohen
- LASA Laboratory, School of Engineering, Ecole Polytechnique Federale de Lausanne—EPFL, Station 9, Lausanne, 1015 Switzerland
| | - Mahdi Khoramshahi
- LASA Laboratory, School of Engineering, Ecole Polytechnique Federale de Lausanne—EPFL, Station 9, Lausanne, 1015 Switzerland
| | - Aude Billard
- LASA Laboratory, School of Engineering, Ecole Polytechnique Federale de Lausanne—EPFL, Station 9, Lausanne, 1015 Switzerland
| | - Robin Salesse
- EuroMov, Montpellier University, 700 Avenue du Pic Saint-Loup, Montpellier, 34090 France
| | - Mathieu Gueugnon
- EuroMov, Montpellier University, 700 Avenue du Pic Saint-Loup, Montpellier, 34090 France
| | - Ludovic Marin
- EuroMov, Montpellier University, 700 Avenue du Pic Saint-Loup, Montpellier, 34090 France
| | - Benoit G. Bardy
- EuroMov, Montpellier University, 700 Avenue du Pic Saint-Loup, Montpellier, 34090 France
- Institut Universitaire de France, Paris, France
| | - Mario di Bernardo
- Department of Engineering Mathematics, University of Bristol, Merchant Venturers’ Building, Exeter, BS8 1UB UK
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80125 Italy
| | - Stephane Raffard
- University Department of Adult Psychiatry, Hôpital de la Colombière, CHU Montpellier, Montpellier-1 University, Montpellier, France
- Epsylon Laboratory Dynamic of Human Abilities & Health Behaviors, Montpellier-3 University, Montpellier, France
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, EX4 4QJ UK
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The acquisition of socio-motor improvisation in the mirror game. Hum Mov Sci 2015; 46:117-28. [PMID: 26741257 DOI: 10.1016/j.humov.2015.12.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 11/02/2015] [Accepted: 12/15/2015] [Indexed: 11/20/2022]
Abstract
Socio-motor improvisation is defined as the creative action of two or more people without a script or anticipated preparation. It is evaluated through two main parameters: movement synchronization and movement richness. Experts in art (e.g., dance, theater or music) are known to exhibit higher synchronization and to perform richer movements during interpersonal improvisation, but how these competences evolve over time is largely unknown. In the present study, we investigated whether performing more synchronized and richer movements over time can promote the acquisition of improvisation. Pairs of novice participants were instructed to play an improvisation mirror game in three different sessions. Between sessions, they performed an unintended interpersonal coordination task in which synchronization and richness were manipulated, resulting in four different groups of dyads. Our results demonstrate that synchronization during improvisation improved for all groups whereas movement richness only enhanced for dyads that performed synchronized movements during unintended coordination tasks. Our findings suggest that movement synchrony contributes more than movement richness to the acquisition of socio-motor improvisation in the mirror game.
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