Department of Psychiatry and Behavioral Sciences, University of California, Davis, Davis, CA.
Department of Psychology, State University of New York Stony Brook, Stony Brook, NY.
Schizophr Bull. 2020 Jan 4;46(1):154-162. doi: 10.1093/schbul/sbz025.
Although meta-analyses suggest that schizophrenia (SZ) is associated with a more severe neurocognitive phenotype than mood disorders such as bipolar disorder, considerable between-subject heterogeneity exists in the phenotypic presentation of these deficits across mental illnesses. Indeed, it is unclear whether the processes that underlie cognitive dysfunction in these disorders are unique to each disease or represent a common neurobiological process that varies in severity. Here we used latent profile analysis (LPA) across 3 distinct cognitive domains (cognitive control, episodic memory, and visual integration; using data from the CNTRACS consortium) to identify distinct profiles of patients across psychotic illnesses. LPA was performed on a sample of 223 psychosis patients (59 with Type I bipolar disorder, 88 with SZ, and 76 with schizoaffective disorder). Seventy-three healthy control participants were included for comparison but were not included in sample LPA. Three latent profiles ("Low," "Moderate," and "High" ability) were identified as the underlying covariance across the 3 domains. The 3-profile solution provided highly similar fit to a single continuous factor extracted by confirmatory factor analysis, supporting a unidimensional structure. Diagnostic ratios did not significantly differ between profiles, suggesting that these profiles cross diagnostic boundaries (an exception being the Low ability profile, which had only one bipolar patient). Profile membership predicted Brief Psychiatric Rating Scale and Young Mania Rating Scale symptom severity as well as everyday communication skills independent of diagnosis. Biological, clinical and methodological implications of these findings are discussed.
尽管荟萃分析表明精神分裂症（SZ）与心境障碍（如双相情感障碍）相比，具有更严重的神经认知表型，但这些精神疾病之间的认知缺陷在个体表现上存在相当大的异质性。事实上，目前还不清楚这些疾病中认知功能障碍的潜在过程是每种疾病所特有的，还是代表一种共同的神经生物学过程，只是严重程度不同。在这里，我们使用潜在剖面分析（LPA）对 3 个不同的认知领域（认知控制、情景记忆和视觉整合；使用来自 CNTRACS 联盟的数据）进行分析，以确定精神疾病患者的不同特征。LPA 对 223 名精神病患者（59 名 I 型双相情感障碍患者、88 名 SZ 患者和 76 名分裂情感障碍患者）进行了分析。为了进行比较，纳入了 73 名健康对照参与者，但他们未被纳入样本 LPA。确定了三个潜在的特征（“低”、“中”和“高”能力）作为三个领域之间的潜在协方差。三特征解决方案与通过验证性因素分析提取的单一连续因子提供了高度相似的拟合度，支持了单维结构。特征剖面之间的诊断比率没有显著差异，表明这些特征跨越了诊断边界（一个例外是低能力特征，其中只有一名双相患者）。特征剖面成员预测简明精神病评定量表和 Young 躁狂评定量表的症状严重程度以及日常沟通技能，而与诊断无关。讨论了这些发现的生物学、临床和方法学意义。
