Unravelling socio-motor biomarkers in schizophrenia
Slowinski, PM; Alderisio, F; Zhai, C; et al.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
Date: 1 February 2017
Article
Journal
npj Schizophrenia
Publisher
Nature Publishing Group
Publisher DOI
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 ...
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.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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