Cognitive variability in psychotic disorders: a cross-diagnostic cluster analysis.
Cognitive variability in psychotic disorders: a cross-diagnostic cluster analysis.
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精神疾病的认知变异性：跨诊断聚类分析。
DOI：
10.1017/s0033291714000774
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发表时间：
2014-11
影响因子：
6.9
通讯作者：
Ongür D
中科院分区：
文献类型：
--
作者：
Lewandowski KE;Sperry SH;Cohen BM;Ongür D
认知功能障碍是精神病性障碍的核心特征;然而，在功能障碍的认知领域方面，受试者内部和受试者之间都存在很大的变异性，并且任何诊断或精神病作为一个整体的认知优势和弱点特征的明确“概况”尚未出现。聚类分析提供了一个机会，使用数据驱动的方法，而不是预先确定的分组标准对个体进行分组。虽然有几项研究已经确定了精神分裂症有意义的认知集群，但迄今为止还没有研究使用集群方法在精神病患者的交叉诊断样本中检查认知。我们的目的是研究认知变量的样本中的167例精神病患者使用聚类方法。精神分裂症（n=41），情感障碍（n=53）或双相情感障碍与精神病（n=73）的受试者进行了评估，使用一组认知和临床措施。认知数据进行了分析，使用沃德的方法，其次是K-均值聚类方法。然后对诊断和临床症状，人口统计学变量和社区功能的测量进行比较。一个四簇的解决方案，包括一个“神经心理正常”的集群，一个全球性的和显着受损的集群，和两个集群的混合认知配置文件。聚类在几个临床变量上不同;诊断分布在所有聚类中，尽管不是均匀的。识别具有相似神经认知特征的患者群体可能有助于查明这些特征背后的相关神经异常。这种分组也可能加速个性化治疗方法的发展，包括针对患者特定认知特征的认知补救。
Cognitive dysfunction is a core feature of psychotic disorders; however, substantial variability exists both within and between subjects in terms of cognitive domains of dysfunction, and a clear ‘profile’ of cognitive strengths and weaknesses characteristic of any diagnosis or psychosis as a whole has not emerged. Cluster analysis provides an opportunity to group individuals using a data-driven approach rather than predetermined grouping criteria. While several studies have identified meaningful cognitive clusters in schizophrenia, no study to date has examined cognition in a cross-diagnostic sample of patients with psychotic disorders using a cluster approach. We aimed to examine cognitive variables in a sample of 167 patients with psychosis using cluster methods. Subjects with schizophrenia (n=41), schizo-affective disorder (n=53) or bipolar disorder with psychosis (n=73) were assessed using a battery of cognitive and clinical measures. Cognitive data were analysed using Ward’s method, followed by a K-means cluster approach. Clusters were then compared on diagnosis and measures of clinical symptoms, demographic variables and community functioning. A four-cluster solution was selected, including a ‘neuropsychologically normal’ cluster, a globally and significantly impaired cluster, and two clusters of mixed cognitive profiles. Clusters differed on several clinical variables; diagnoses were distributed amongst all clusters, although not evenly. Identification of groups of patients who share similar neurocognitive profiles may help pinpoint relevant neural abnormalities underlying these traits. Such groupings may also hasten the development of individualized treatment approaches, including cognitive remediation tailored to patients’ specific cognitive profiles.