Characterization Of Neuropsychological Impairment In Schizophrenia

精神分裂症神经心理损伤的特征

基本信息

批准号：
8556919
负责人：
Karen FAITH Berman
金额：
$ 150.57万
依托单位：
NATIONAL INSTITUTE OF MENTAL HEALTH
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8556919
关键词：
Age of Onset Asia Back Behavior Behavior assessment Behavioral Behavioral Mechanisms Characteristics Clinical Cognitive Cognitive deficits Collection Consensus Data Data Aggregation Data Analyses Data Set Development Dimensions Disease Europe Factor Analysis Family Future Genes Genetic Genetic Code Genetic Polymorphism Genome Genotype Geographic Locations Growth Heritability Heterogeneity Impaired cognition Impairment Individual Japan Journals Link Literature Manuscripts Memory Meta-Analysis Methodology Methods Modafinil Modeling Motivation Neuropsychological Tests Neuropsychology North America Outcome Paper Patients Pattern Performance Personality Personality Disorders Pharmaceutical Preparations Phenotype Preparation Protocols documentation Psychiatry Psychopathology Publications Quality Control Questionnaires Recording of previous events Reporting Research Risk Sampling Schizophrenia Short-Term Memory Siblings Social Adjustment Sodium Channel Sorting - Cell Movement Stream Structure Symptoms Syndrome Techniques Testing Update Variant Work base case control data management depressive symptoms design falls genetic analysis genome wide association study genome-wide improved information processing interest memory process neuroimaging neuropsychological normal aging novel phenomics processing speed sex sound symposium tolcapone trait treatment trial visual memory

项目摘要

Current and recent work generally has focused on understanding and improving cognitive and behavioral phenotypes and applying these in analyses of whole genome association data. In Dickinson et al (2011) we extended earlier analyses of cognitive structure in schizophrenia cases and controls (Dickinson et al., 2006; Genderson, Dickinson et al., 2007). To achieve greater cognitive phenotype reliability and avoid redundant statistical comparisons, we used exploratory and confirmatory factor analyses separately in the CBDB samples (schizophrenia n=496, unaffected siblings n= 504, and controls n=823) and identified six positively correlated cognitive factors (for verbal memory, visual memory, n-back working memory, processing speed, card sorting, and span working memory). Data also supported a higher-order factor reflecting general cognitive ability, so-called g. We found that this structure was reasonably consistent across schizophrenia cases and controls, as had been shown previously (Dickinson et al., 2006), and extended this finding to include unaffected siblings. These findings guided construction of cognitive composite scores, which are in wide use in CBDB data analyses. One application of these composite scores has been to explore genome-wide associations in the CBDB samples. One exciting finding has been presented at genetics and psychiatry conferences and will be submitted for publication in the near future. We have identified an exciting and novel genome-wide significant association between our global cognitive composite and a sodium channel gene polymorphism that explains substantial variance in the cognitive impairment in our sample of people with schizophrenia and in their unaffected siblings an association that would not have been detected without the cognitive data aggregation strategy and composite scores developed by the Neuropsychology Lab. To gauge better which cognitive variables are best suited for genetics analysis, separate work has taken different approaches to estimate the heritability of these variables. Recently, a novel technique (GCTA) has been developed that permits estimates of heritability based on genome-wide genotype data from samples of unrelated people. The technique correlates pair-wise distances between genotypes for all possible pairs from a sample, with pair-wise differences in performance on a trait of interest (in our case, cognitive variables). We will present our first results contrasting GCTA methods with more traditional family-based methods at an upcoming conference and are beginning work on an associated manuscript. Wallwork et al. (2011) describes our efforts to determine a more empirically sound dimensional structure for patient data from the Positive and Negative Syndrome Scale (PANSS). The 30-item scale was developed with three syndrome scores, but previous analyses suggest that the scale really captures 5 or more dimensions of schizophrenia-associated symptomatology. We used existing literature to build a consensus five-dimension model of PANSS data then tested variations of this model using confirmatory factor analysis in the CBDB schizophrenia data and in an independent data set from Japan, supporting construction of new PANSS composite scores for positive, negative, depressive, agitated, and concrete/disorganized symptoms. Analogous lines of work are examining the dimensional structure of typical and abnormal personality in the CBDB data. We have elaborated and are refining five-dimension models for the Tri-dimensional Personality Questionnaire (TPQ) and for the SCID-II Personality Questionnaire (SCID-II). The TPQ targets personality more in the non-clinical range. The SCID-II is used to assess maladaptive personality disorder symptomatology. All of this work has been presented at scientific conferences and a paper on the SCID-II analyses is nearing completion. We design cognitive batteries for and collect, score and manage data from pharmacological trials seeking evidence of treatment-related changes in cognitive performance in schizophrenia patients and healthy controls. Recently, we have helped to develop the assessment and analysis strategy for a protocol examining an experimental agent (LX6171) and have worked on data analyses of information from tolcapone and modafinil treatment trials. We have completed an update of earlier meta-analyses of cognitive impairment in schizophrenia (Dickinson et al., 2007), showing that the cognitive impairment seen in people with schizophrenia has been consistent in magnitude and pattern over the past 30 years, and across different geographic regions around the world (i.e., North America, Europe and Asia). An early version of this work was presented at a conference and is accepted for publication as a chapter in an edited collection (Dickinson et al., in press), and a journal manuscript describing the final analysis is in preparation. Illness heterogeneity is a major challenge to the development of improved treatments in schizophrenia. As described in Cole et al. (2012), we have utilized latent class growth analysis (LCGA) in an effort to derive illness subtypes based on pre-morbid academic and social adjustment. We found evidence supporting three developmental trajectory subtypes: good/stable, insidious onset, and poor/deteriorating. These classes differed significantly in terms of age of onset, processing speed, and functioning after onset. The finding illustrates a potentially powerful methodology to attack heterogeneity challenges in schizophrenia research. Higher degrees of intra-individual variability (IIV) across neuropsychological tests have been recently linked to risk for schizophrenia, other clinical disorders, and normal aging. Cole et al (2011) is the first report showing decreasing IIV across cognitive domains in people with schizophrenia, their siblings and controls, respectively. Ongoing work is examining whether IIV might be useful as an intermediate phenotype. We have a large sample of unaffected siblings in the CBDB dataset, who, on average, share half their genetic code with an ill sibling, but do not share confounds, such as medication history and low motivation. In Wisner et al. (2011) we further characterized this sample, showing how sex and psychopathology history associate with sibling cognitive performance.

当前和最近的工作通常侧重于理解和改善认知和行为表型，并将其应用于全基因组关联数据的分析。在 Dickinson 等人（2011）中，我们扩展了早期对精神分裂症病例和对照认知结构的分析（Dickinson 等人，2006；Genderson、Dickinson 等人，2007）。为了实现更高的认知表型可靠性并避免冗余的统计比较，我们在 CBDB 样本（精神分裂症 n=496、未受影响的兄弟姐妹 n= 504 和对照 n=823）中分别使用探索性和验证性因素分析，并确定了六个正相关的认知因素（语言记忆、视觉记忆、n-back工作记忆、处理速度、卡片分类和跨度工作记忆）。数据还支持反映一般认知能力的高阶因素，即所谓的 g。我们发现这种结构在精神分裂症病例和对照之间相当一致，正如之前所表明的那样（Dickinson 等，2006），并将这一发现扩展到包括未受影响的兄弟姐妹。这些发现指导了认知综合评分的构建，该评分在 CBDB 数据分析中广泛使用。这些综合评分的一项应用是探索 CBDB 样本中的全基因组关联。一项令人兴奋的发现已在遗传学和精神病学会议上提出，并将在不久的将来提交出版。我们已经确定了我们的整体认知复合材料和钠通道基因多态性之间存在令人兴奋的、新颖的全基因组显着关联，这解释了我们的精神分裂症患者样本及其未受影响的兄弟姐妹中认知障碍的巨大差异，这种关联是不可能的。在没有神经心理学实验室开发的认知数据聚合策略和综合评分的情况下检测到。为了更好地衡量哪些认知变量最适合遗传学分析，单独的工作采取了不同的方法来估计这些变量的遗传力。最近，开发了一种新技术（GCTA），可以根据来自不相关人群样本的全基因组基因型数据来估计遗传力。该技术将样本中所有可能对的基因型之间的成对距离与感兴趣特征（在我们的例子中为认知变量）的性能成对差异相关联。我们将在即将召开的会议上展示我们的第一个结果，将 GCTA 方法与更传统的基于家庭的方法进行对比，并开始编写相关的手稿。墙壁工作等。 (2011) 描述了我们为从阳性和阴性综合症量表 (PANSS) 中确定患者数据的更实证合理的维度结构所做的努力。该量表由 30 项组成，包含三种综合征评分，但之前的分析表明，该量表确实捕获了精神分裂症相关症状的 5 个或更多维度。我们利用现有文献构建了 PANSS 数据的共识五维模型，然后使用 CBDB 精神分裂症数据和来自日本的独立数据集的验证性因子分析测试了该模型的变体，支持构建新的 PANSS 阳性、阴性综合评分、抑郁、烦躁和具体/混乱的症状。类似的工作正在检查 CBDB 数据中典型和异常人格的维度结构。我们已经详细阐述并正在完善三维人格问卷 (TPQ) 和 SCID-II 人格问卷 (SCID-II) 的五维模型。 TPQ 在非临床范围内更多地针对人格。 SCID-II 用于评估适应不良型人格障碍症状。所有这些工作均已在科学会议上发表，一篇关于 SCID-II 分析的论文即将完成。我们为药理学试验设计认知电池，并收集、评分和管理数据，寻找精神分裂症患者和健康对照者认知表现与治疗相关变化的证据。最近，我们帮助制定了检查实验药物（LX6171）的方案的评估和分析策略，并对托卡朋和莫达非尼治疗试验的信息进行了数据分析。我们已经完成了对精神分裂症认知障碍的早期荟萃分析的更新（Dickinson 等，2007），表明精神分裂症患者的认知障碍在过去 30 年中的程度和模式上是一致的，并且在不同的人群中世界各地的地理区域（即北美、欧洲和亚洲）。这项工作的早期版本在一次会议上提出，并被接受作为编辑集的一章出版（Dickinson 等人，正在出版），描述最终分析的期刊手稿正在准备中。疾病异质性是精神分裂症改进治疗方法开发的主要挑战。正如科尔等人所描述的。 (2012)，我们利用潜在类别增长分析 (LCGA) 来根据病前的学业和社会适应推导出疾病亚型。我们发现了支持三种发育轨迹亚型的证据：良好/稳定、隐匿性发作和较差/恶化。这些类别在发病年龄、处理速度和发病后功能方面存在显着差异。这一发现说明了一种潜在的强大方法来应对精神分裂症研究中的异质性挑战。最近，神经心理学测试中较高程度的个体内变异性（IIV）与精神分裂症、其他临床疾病和正常衰老的风险有关。 Cole 等人 (2011) 是第一份报告，显示精神分裂症患者、其兄弟姐妹和对照组的跨认知领域 IIV 分别下降。正在进行的工作正在研究 IIV 是否可用作中间表型。我们在 CBDB 数据集中有大量未受影响的兄弟姐妹样本，平均而言，他们与患病的兄弟姐妹共享一半的遗传密码，但不存在混杂因素，例如用药史和低积极性。在威斯纳等人。（2011）我们进一步描述了这个样本，显示了性别和精神病理学史与兄弟姐妹认知表现的关系。