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
Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF UK.
Department of Engineering Mathematics, University of Bristol, Merchant Venturers' Building, Exeter, BS8 1UB UK.
NPJ Schizophr. 2017 Feb 1;3:8. doi: 10.1038/s41537-016-0009-x. eCollection 2017.
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.
我们提出了用于精神分裂症的新型、低成本且非侵入性的潜在诊断生物标志物。它们基于“镜像游戏”，这是一种协调任务，要求两名参与者模仿彼此的手部动作。具体而言，我们使用患者在没有伙伴的情况下记录的单独动作，以及与人工代理（计算机虚拟形象或类人机器人）互动期间记录的动作。为了区分患者和对照组，我们采用统计学习技术，并将其应用于从参与者运动数据中得出的非语言同步性和神经运动特征。所提出的分类器准确率为93%，特异性为100%。我们的结果表明，统计学习技术、非语言运动协调和神经运动特征可为决策支持工具奠定基础，在诊断不确定的情况下帮助临床医生。
