Unravelling socio-motor biomarkers in schizophrenia.
Unravelling socio-motor biomarkers in schizophrenia.
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DOI：
10.1038/s41537-016-0009-x
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发表时间：
2017
影响因子：
5.4
通讯作者：
Tsaneva-Atanasova K
中科院分区：
文献类型：
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作者：
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
我们提出了新的、低成本和非侵入性的潜在精神分裂症诊断生物标志物。他们基于“镜像游戏”，这是一种协调任务，要求两个伙伴模仿对方的手部动作。特别是，我们使用患者在没有伴侣的情况下记录的单独运动，以及在与人工代理、计算机化身或类人机器人互动时记录的运动。为了区分患者和对照组，我们使用了统计学习技术，我们将其应用于从参与者的运动数据中得出的非语言同步和神经运动特征。该分类器具有93%的准确率和100%的特异度。我们的结果提供了证据，表明统计学习技术、非语言运动协调和神经运动特征可以形成决策支持工具的基础，以帮助临床医生在诊断不确定的情况下。一项新的运动和社交测试可以检测精神分裂症的标志，并帮助诊断和管理这种情况。为了建立精神分裂症的可靠指标，英国埃克塞特大学的Piotr Slowinski和他的同事开发了一种测试，可以检测运动和社会互动方面的缺陷，这两个方面都是精神分裂症的特征。他们要求人们单独执行动作，并模仿电脑化身或类人机器人的动作。对运动的自动分析能够以略好于临床采访的准确性和特异性区分精神分裂症患者和健康参与者，并与基于昂贵得多的神经成像方法的测试相媲美。这项技术可以帮助诊断精神分裂症并监测患者对治疗的反应，但在应用于临床实践之前，需要在临床试验中进行测试。
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. 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.
影响因子：
3.7
作者：
Del-Monte J;Raffard S;Capdevielle D;Salesse RN;Schmidt RC;Varlet M;Bardy BG;Boulenger JP;Gély-Nargeot MC;Marin L
通讯作者：
Marin L