Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States of America.
Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States of America.
Schizophr Res. 2022 May;243:489-499. doi: 10.1016/j.schres.2021.11.027. Epub 2021 Dec 7.
Affective and non-affective psychotic disorders are associated with variable levels of impairment in affective processing, but this domain typically has been examined via presentation of static facial images. We compared performance on a dynamic facial expression identification task across six emotions (sad, fear, surprise, disgust, anger, happy) in individuals with psychotic disorders (bipolar with psychotic features [PBD] = 113, schizoaffective [SAD] = 163, schizophrenia [SZ] = 181) and healthy controls (HC; n = 236) derived from the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP). These same individuals with psychotic disorders were also grouped by B-SNIP-derived Biotype (Biotype 1 [B1] = 115, Biotype 2 [B2] = 132, Biotype 3 [B3] = 158), derived from a cluster analysis applied to a large biomarker panel that did not include the current data. Irrespective of the depicted emotion, groups differed in accuracy of emotion identification (P < 0.0001). The SZ group demonstrated lower accuracy versus HC and PBD groups; the SAD group was less accurate than the HC group (Ps < 0.02). Similar overall group differences were evident in speed of identifying emotional expressions. Controlling for general cognitive ability did not eliminate most group differences on accuracy but eliminated almost all group differences on reaction time for emotion identification. Results from the Biotype groups indicated that B1 and B2 had more severe deficits in emotion recognition than HC and B3, meanwhile B3 did not show significant deficits. In sum, this characterization of facial emotion recognition deficits adds to our emerging understanding of social/emotional deficits across the psychosis spectrum.
情感和非情感性精神病性障碍与情感处理障碍的程度有关，但该领域通常通过呈现静态面部图像来进行检查。我们比较了精神病性障碍患者（伴精神病特征的双相障碍 [PBD] = 113，分裂情感性障碍 [SAD] = 163，精神分裂症 [SZ] = 181）和健康对照组（HC；n = 236）在识别六种情绪（悲伤、恐惧、惊讶、厌恶、愤怒、快乐）的动态面部表情识别任务中的表现，这些精神病性障碍患者来自于中间表型的双相 - 精神分裂症网络（Bipolar-Schizophrenia Network on Intermediate Phenotypes，B-SNIP）。这些精神病性障碍患者还根据 B-SNIP 衍生的生物型（生物型 1 [B1] = 115，生物型 2 [B2] = 132，生物型 3 [B3] = 158）进行分组，这些生物型是基于对一个大型生物标志物面板的聚类分析得到的，该面板不包括当前数据。无论所描绘的情绪如何，各组在情绪识别的准确性上都存在差异（P < 0.0001）。与 HC 和 PBD 组相比，SZ 组的准确性较低；与 HC 组相比，SAD 组的准确性较低（P < 0.02）。在识别情绪表达的速度方面也存在类似的总体组间差异。控制一般认知能力并不能消除大多数组在准确性上的差异，但消除了情绪识别反应时间上的大多数组间差异。生物型组的结果表明，B1 和 B2 比 HC 和 B3 有更严重的情绪识别缺陷，而 B3 则没有明显的缺陷。总之，对面部情绪识别缺陷的这种描述增加了我们对精神病谱中社会/情绪缺陷的理解。
