Department of Adult Psychiatry, Jagiellonian University Medical College, Cracow, Poland.
Sorbonne Université, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Hôpital de la Pitie´ -Salpêtrière, 75013 Paris, France.
Prog Neuropsychopharmacol Biol Psychiatry. 2021 Dec 20;111:110137. doi: 10.1016/j.pnpbp.2020.110137. Epub 2020 Oct 11.
Schizophrenia (SZ) and bipolar disorder (BD) patients share deficits in motor functions in the form of neurological (NSS) and cerebellar soft signs (CSS), and implicit motor learning disturbances. Here, we use cluster analysis method to assess (1) the relationship between those abnormalities in SZ and BD and (2) the differences between those groups.
33 SZ patients, 33 BD patients as well as 31 healthy controls (HC) took part in the study. We assessed CSS with the International Cooperative Ataxia Rating Scale (ICARS) and NSS with the Neurological Evaluation Scale (NES). Implicit motor learning was evaluated with the Serial Reaction Time Task (SRTT). Participants were divided into clusters (Ward's method) based on the mean response time and mean error rate in SRTT. The difference in ICARS and NES scores, and SRTT variables between clusters were evaluated. We have measured associations between SRTT parameters and both ICARS and NES total scores and subscores.
Cluster analysis based on the SRTT parameters allowed to extract three clusters. Those were characterized by the increasing disruption of motor functioning (psychomotor retardation, the severity of NSS and CSS) regardless of the diagnosis. Cluster 1 covered almost all of HC and was characterized by faster reaction times and small number of errors. BD and SZ patients represented in cluster 1, although fully functional in performing the SRTT, showed higher rates of NSS and CSS. Patients with BD and SZ were set apart in clusters 2 and 3 in a similar proportion. Cluster 2 presented significantly slower reaction times but with the comparable number of errors to cluster 1. Cluster 3 consisted of participants with normal or decreased reaction time and significantly increased number of errors. None of the clusters were predominantly composed of the patients representing one psychiatric diagnosis.
To our best knowledge, we are presenting the first data indicating the relationship between implicit motor learning and NSS and CSS in SZ and BD patients' groups. Lack of clusters predominantly represented by patients with the diagnosis of SZ or BD may refer to the model of schizophrenia-bipolar disorder boundary, pointing out the similarities between those two disorders.
精神分裂症（SZ）和双相情感障碍（BD）患者在运动功能方面存在神经学（NSS）和小脑软体征（CSS）以及内隐运动学习障碍等缺陷。在这里，我们使用聚类分析方法来评估：（1）SZ 和 BD 患者之间这些异常的关系，以及（2）这些组之间的差异。
33 名 SZ 患者、33 名 BD 患者和 31 名健康对照者（HC）参加了这项研究。我们使用国际合作共济失调评定量表（ICARS）评估 CSS，使用神经学评估量表（NES）评估 NSS。我们使用序列反应时间任务（SRTT）评估内隐运动学习。参与者根据 SRTT 的平均反应时间和平均错误率进行聚类（Ward 法）。评估聚类之间的 ICARS 和 NES 评分以及 SRTT 变量的差异。我们测量了 SRTT 参数与 ICARS 和 NES 总分和子分之间的相关性。
基于 SRTT 参数的聚类分析允许提取三个聚类。这些聚类的特点是运动功能障碍逐渐加重（精神运动迟缓、NSS 和 CSS 的严重程度），而与诊断无关。聚类 1 涵盖了几乎所有的 HC，其特点是反应时间更快，错误更少。BD 和 SZ 患者也包含在聚类 1 中，尽管在执行 SRTT 时完全正常，但 NSS 和 CSS 的发生率更高。BD 和 SZ 患者在聚类 2 和聚类 3中的比例相似。聚类 2 的反应时间明显较慢，但与聚类 1 的错误数相当。聚类 3 由反应时间正常或降低且错误数明显增加的参与者组成。没有一个聚类主要由患有一种精神科诊断的患者组成。
据我们所知，我们首次提供了 SZ 和 BD 患者群体内隐运动学习与 NSS 和 CSS 之间关系的数据。缺乏主要由 SZ 或 BD 患者诊断组成的聚类可能是指向两种疾病相似之处的精神分裂症-双相障碍边界模型的参考。
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