• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Schizophrenia Bulletin >Early Recognition and Disease Prediction in the At-Risk Mental States for Psychosis Using Neurocognitive Pattern Classification
【2h】

Early Recognition and Disease Prediction in the At-Risk Mental States for Psychosis Using Neurocognitive Pattern Classification

机译：使用神经认知模式分类的精神病风险心理状态下的早期识别和疾病预测

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

>Background: 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. >Methods: 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. >Results: 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. >Conclusions: 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.
机译：>背景：神经心理学缺陷早于明显的精神病，并且与已确诊疾病的损伤重叠。然而，迄今为止，没有任何一种神经认知措施显示出足够的能力进行预后测试。因此，仍然需要确定多变量神经认知模式分类是否可以促进对精神病的不同高危精神状态（ARMS）的诊断鉴定以及疾病转变的个性化预测。 >方法：首先，基于全面的神经心理学测试，对30位健康对照（HC）与48位ARMS个体进行了分类，亚健康个体分为20个“早期”，28位“晚期” ARMS受试者。其次，通过对随访4年后有过渡（n = 15），无过渡（n = 20）vs HC的那些ARMS个体的神经认知基线数据进行分类来评估疾病预测。分类的普遍性通过重复的双重交叉验证来估计。 >结果：在首次分析中，三组交叉验证的分类准确性为94.2％（HC vs休息），85.0％（高危受试者vs休息）和91.4％（晚期-高风险受试者vs休息）和90.8％（HC vs休息），90.8％（转化者vs休息）和89.0％（非转化者vs休息）。将早期或晚期ARMS与HC区分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。疾病过渡主要由执行和言语学习障碍预测。 >结论：通过使用多元模式识别评估神经认知测试电池，可以可靠地个体识别不同的ARMS及其临床结果。这些模式可能具有实质性改善精神病早期识别的潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号