1McLean Hospital,Schizophrenia and Bipolar Disorder Program,Belmont,Massachusetts.
J Int Neuropsychol Soc. 2018 Apr;24(4):382-390. doi: 10.1017/S1355617717001047. Epub 2017 Oct 18.
Cognitive dysfunction is a core symptom dimension that cuts across the psychoses. Recent findings support classification of patients along the cognitive dimension using cluster analysis; however, data-derived groupings may be highly determined by sampling characteristics and the measures used to derive the clusters, and so their interpretability must be established. We examined cognitive clusters in a cross-diagnostic sample of patients with psychosis and associations with clinical and functional outcomes. We then compared our findings to a previous report of cognitive clusters in a separate sample using a different cognitive battery.
Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, and clinical and community functioning assessments. Cluster analyses were performed on cognitive variables, and clusters were compared on demographic, cognitive, and clinical measures. Results were compared to findings from our previous report.
A four-cluster solution provided a good fit to the data; profiles included a neuropsychologically normal cluster, a globally impaired cluster, and two clusters of mixed profiles. Cognitive burden was associated with symptom severity and poorer community functioning. The patterns of cognitive performance by cluster were highly consistent with our previous findings.
We found evidence of four cognitive subgroups of patients with psychosis, with cognitive profiles that map closely to those produced in our previous work. Clusters were associated with clinical and community variables and a measure of premorbid functioning, suggesting that they reflect meaningful groupings: replicable, and related to clinical presentation and functional outcomes. (JINS, 2018, 24, 382-390).
认知功能障碍是贯穿精神病学的核心症状维度。最近的研究结果支持使用聚类分析根据认知维度对患者进行分类；然而，数据衍生的分组可能高度取决于采样特征和用于衍生聚类的测量，因此必须确定其可解释性。我们在精神分裂症患者的跨诊断样本中检查了认知聚类，以及与临床和功能结果的关联。然后，我们将我们的发现与使用不同认知电池的另一个样本中的认知聚类的先前报告进行了比较。
对有情感或非情感性精神病的参与者（n=120）和健康对照组（n=31）进行了 MATRICS 共识认知电池以及临床和社区功能评估。对认知变量进行聚类分析，并对人口统计学、认知和临床指标进行聚类比较。结果与我们之前的报告进行了比较。
四聚类解决方案为数据提供了良好的拟合；这些特征包括神经认知正常的聚类、整体受损的聚类和两种混合特征的聚类。认知负担与症状严重程度和较差的社区功能有关。按聚类划分的认知表现模式与我们之前的发现高度一致。
我们发现精神分裂症患者存在四个认知亚组的证据，其认知特征与我们之前的工作产生的特征非常吻合。聚类与临床和社区变量以及认知前功能测量有关，这表明它们反映了有意义的分组：可复制的，与临床表现和功能结果相关。（JINS，2018，24，382-390）。
