Adapting Experimental Cognitive and Affective Tasks for Schizophrenia

描述（由申请人提供）：该申请是对RFA-MH-08-090的回应，要求将认知科学研究的实验任务改编用于精神分裂症的临床评估和干预研究。我们建议进行三个阶段的调查。在第一阶段，我们将检查宾夕法尼亚精神分裂症研究中心开发的计算机电池的可用数据，该中心已经采用了基于神经科学的测量方法。该电池已应用于我们的中心以及大规模的临床和遗传合作研究，产生了丰富的数据集，可以指导改进和优化其在精神分裂症中的应用。该数据集将允许对健康人和患者的心理测量特性进行严格的调查，并确定表现与精神分裂症的临床特征之间的关系。基于这些分析，我们将以最小化管理时间和优化产量为目标修剪项目。我们将用传统和新颖的方法来研究这些数据，以指导高效替代形式的构建。在这个阶段，我们还将探索从测试结果中获得的更详细的理论驱动参数，以确定它们是否提高了诊断敏感性或特异性或与临床特征的相关性（Specific Aim 1）。在第二阶段，我们建议通过适应和验证过程采取几个新的任务，并选择产生最有希望结果的任务纳入最终电池。每个测试将评估面部效度，内部一致性，测试重测信度和结构效度（收敛和发散）。还将对精神分裂症患者和健康对照者进行初步抽样，以确定这些人群的耐受性和基本心理测量特性。我们将特别扩大现有的测试集，增加额外的措施，挖掘信息处理级联的早期阶段，并扩大社会认知的措施，以纳入韵律（具体目标2）。在第三阶段，我们将最终电池的替代等效形式（以平衡顺序）应用于精神分裂症患者和人口统计学平衡的健康社区控制的新样本。这将使评估其整体和特定领域的敏感性和特异性诊断。我们将建立性别差异、年龄、教育和父母教育等调节变量的影响。我们将通过将表现与精神分裂症的临床特征（包括症状维度和功能结果）相关联来检验神经行为测量的临床相关性（具体目标3）。数据将放在公共领域，电池将可通过网络界面下载和实施。我们希望最终的电池是用户友好和平台独立的，对管理员的培训要求最低，包括详细的实现过程，并具有自动评分和数据库功能。精神分裂症是一种复杂的脑部疾病，具有显著的认知缺陷，影响功能预后。基础神经科学和临床神经科学的结合是理解认知缺陷的神经基础的关键，也是开发有针对性的干预措施以改善认知障碍的必要条件。该研究的目标是将功能神经成像研究中的神经行为任务应用于可应用于大规模治疗研究的电池测试中。我们将评估来自现有电池的数据集，以微调可用的措施，并适应新的任务来增强电池。

DESCRIPTION (provided by applicant): The application is a response to RFA-MH-08-090 requesting the adaptation of experimental tasks from cognitive science research for use in clinical assessment and intervention studies in schizophrenia. We propose a three-phase investigation. In the first phase we will examine available data from a computerized battery developed by the Penn Schizophrenia Research Center, which has already adapted neuroscience- based measures. The battery was applied in our center and in large-scale collaborative clinical and genetic studies, yielding a rich data set that can guide efforts to refine and optimize its application in schizophrenia. The data set will permit a rigorous investigation of the psychometric properties in healthy people and patients and determining how performance relates to clinical features of schizophrenia. Based on these analyses, we will prune the items with the aim of minimizing administration time and optimizing the yield. We will examine these data with traditional and novel approaches to guide the construction of efficient alternative forms. At this stage we will also explore more detailed theoretically driven parameters obtained from the test results to determine whether they improve diagnostic sensitivity or specificity or correlations with clinical features (Specific Aim 1). In the second phase we propose to take several new tasks through the process of adaptation and validation, and select those yielding the most promising results for inclusion in the final battery. Each test will be assessed for face validity, internal consistency, test-retest reliability, and construct validity (both convergent and divergent). It will also be administered to a preliminary sample of patients with schizophrenia and healthy controls to establish tolerance and basic psychometric properties in these populations. We will specifically amplify the existing set of tests with additional measures that tap earlier stages in the information-processing cascade, and expand the measures of social cognition to incorporate prosody (Specific Aim 2). In the third phase we will apply the alternate equivalent forms of the final battery (in counterbalanced order) to a new sample of patients with schizophrenia and demographically balanced healthy community controls. This will enable the assessment of its global and domain-specific sensitivity and specificity to diagnosis. We will establish effects of moderating variables such as sex differences, age, education and parental education. We will examine the clinical relevance of the neurobehavioral measures by correlating performance with clinical features of schizophrenia including symptom dimensions and functional outcome (Specific Aim 3). Data will be placed in the public domain and the battery will be available for downloading and implementation through a web interface. We expect the resulting battery to be user friendly and platform independent, require minimal training for administrators, include detailed implementation procedures, and have automated scoring and databasing features. Project Narrative Schizophrenia is a complex brain disorder with significant cognitive deficits that affect functional outcome. Integration of basic and clinical neuroscience is key to understanding the neural basis of the deficits and is required for developing targeted interventions that can ameliorate cognitive impairment. The goal of the proposed study is to adapt neurobehavioral tasks applied in functional neuroimaging research to use as tests in a battery that can be applied in large-scale treatment studies. We will evaluate a dataset from an existing battery to fine tune available measures and adapt new tasks to augment the battery.