Idiopathic REM Sleep Behavior Disorder: Implications for the Pathogenesis of Lewy Body Diseases

Cardiac Autonomic Dysfunction Is Associated with Severity of REM Sleep without Atonia in Isolated REM Sleep Behavior Disorder

123I-metaiodobenzylguanidine (MIBG) cardiac scintigraphy was performed to assess cardiac autonomic dysfunction and demonstrate its correlation with clinical and polysomnographic characteristics in patients with isolated rapid eye movement (REM) sleep behavior disorder. All subjects including 39 patients with isolated REM sleep behavior disorder and 17 healthy controls underwent MIBG cardiac scintigraphy for cardiac autonomic dysfunction assessment. The isolated REM sleep behavior disorder was confirmed by in-lab overnight polysomnography. A receiver operating curve was constructed to determine the cut-off value of the early and delayed heart-to-mediastinum ratio in patients with isolated REM sleep behavior disorder. Based on each cut-off value, a comparison analysis of REM sleep without atonia was performed by dividing isolated REM sleep behavior disorder patients into two groups. MIBG uptake below the cut-off value was associated with higher REM sleep without atonia. The lower heart-to-mediastinum ratio had significantly higher REM sleep without atonia (%), both with cut-off values of early (11.0 ± 5.6 vs. 29.3 ± 23.2%, p = 0.018) and delayed heart-to-mediastinum ratio (9.1 ± 4.3 vs. 30.0 ± 22.9%, p = 0.011). These findings indicate that reduced MIBG uptake is associated with higher REM sleep without atonia in isolated REM sleep behavior disorder.

Semi-supervised clustering methods

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.