Adapting Experimental Cognitive and Affective Tasks for Schizophrenia

描述（由申请方提供）：本申请是对RFA-MH-08-090的回应，该申请要求对认知科学研究的实验任务进行调整，以用于精神分裂症的临床评估和干预研究。我们建议分三个阶段进行调查。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化电池的可用数据，该中心已经采用了基于神经科学的措施。该电池在我们的中心和大规模的合作临床和遗传研究中得到了应用，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。该数据集将允许在健康人和患者的心理测量特性的严格调查，并确定如何表现与精神分裂症的临床特征。根据这些分析，我们将修剪项目，以最大限度地减少给药时间和优化产量。我们将用传统和新颖的方法来研究这些数据，以指导构建有效的替代形式。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诊断灵敏度或特异性或与临床特征的相关性（具体目标1）。在第二阶段，我们建议采取几个新的任务，通过适应和验证的过程中，并选择那些产生最有希望的结果，包括在最终电池。将评估每个测试的表面效度、内部一致性、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的初步样本进行管理，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，纳入韵律（具体目标2）。在第三阶段，我们将应用替代等效形式的最终电池（在平衡的顺序）的精神分裂症患者和人口统计学平衡的健康社区对照的新样本。这将能够评估其对诊断的全局和特定领域的灵敏度和特异性。我们将建立调节变量的影响，如性别差异，年龄，教育和父母的教育。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局）相关联来检查神经行为测量的临床相关性（具体目标3）。数据将被置于公共领域，电池将可通过网络界面下载和实施。我们希望由此产生的电池是用户友好和平台独立的，需要最少的培训管理员，包括详细的实施程序，并具有自动评分和数据库功能。精神分裂症是一种复杂的大脑疾病，具有影响功能结果的显著认知缺陷。基础和临床神经科学的整合是理解缺陷的神经基础的关键，并且是开发可以改善认知障碍的有针对性的干预措施所必需的。拟议研究的目标是调整应用于功能性神经影像学研究的神经行为任务，以用作可应用于大规模治疗研究的电池中的测试。我们将评估现有电池的数据集，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.