Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Nussbaumstrasse 7, 80336 Munich, Germany.
Schizophr Bull. 2012 Nov;38(6):1200-15. doi: 10.1093/schbul/sbr037. Epub 2011 May 16.
Neuropsychological deficits predate overt psychosis and overlap with the impairments in the established disease. However, to date, no single neurocognitive measure has shown sufficient power for a prognostic test. Thus, it remains to be determined whether multivariate neurocognitive pattern classification could facilitate the diagnostic identification of different at-risk mental states (ARMS) for psychosis and the individualized prediction of illness transition.
First, classification of 30 healthy controls (HC) vs 48 ARMS individuals subgrouped into 20 "early," 28 "late" ARMS subjects was performed based on a comprehensive neuropsychological test battery. Second, disease prediction was evaluated by categorizing the neurocognitive baseline data of those ARMS individuals with transition (n = 15) vs non transition (n = 20) vs HC after 4 years of follow-up. Generalizability of classification was estimated by repeated double cross-validation.
The 3-group cross-validated classification accuracies in the first analysis were 94.2% (HC vs rest), 85.0% (early at-risk subjects vs rest), and, 91.4% (late at-risk subjects vs rest) and 90.8% (HC vs rest), 90.8% (converters vs rest), and 89.0% (nonconverters vs rest) in the second analysis. Patterns distinguishing the early or late ARMS from HC primarily involved the verbal learning/memory domains, while executive functioning and verbal IQ deficits were particularly characteristic of the late ARMS. Disease transition was mainly predicted by executive and verbal learning impairments.
Different ARMS and their clinical outcomes may be reliably identified on an individual basis by evaluating neurocognitive test batteries using multivariate pattern recognition. These patterns may have the potential to substantially improve the early recognition of psychosis.
神经认知缺陷先于明显的精神病出现，并与既定疾病的损害重叠。然而，迄今为止，没有单一的神经认知测量显示出足够的预后测试能力。因此，仍需确定多变量神经认知模式分类是否可以促进不同精神病高危精神状态（ARMS）的诊断识别和疾病转变的个体化预测。
首先，基于全面的神经心理学测试组合，对 30 名健康对照者（HC）和 48 名 ARMS 个体亚组（20 名“早期”，28 名“晚期”ARMS 受试者）进行分类。其次，通过对 4 年后随访的具有转换（n = 15）、非转换（n = 20）和 HC 的 ARMS 个体的神经认知基线数据进行分类，评估疾病预测。通过重复双交叉验证来估计分类的泛化能力。
第一次分析中，3 组交叉验证的分类准确率分别为 94.2%（HC 与其余组）、85.0%（早期高危组与其余组）和 91.4%（晚期高危组与其余组）；第二次分析中，90.8%（HC 与其余组）、90.8%（转换者与其余组）和 89.0%（非转换者与其余组）。区分早期或晚期 ARMS 与 HC 的模式主要涉及言语学习/记忆领域，而执行功能和言语智商缺陷则是晚期 ARMS 的特征。疾病转变主要由执行和言语学习障碍预测。
通过使用多变量模式识别评估神经认知测试组合，可以可靠地对个体进行不同的 ARMS 及其临床结果的识别。这些模式有可能大大提高精神病的早期识别。
