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.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZMH1-ERB-Z (04))
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Kozak, Michael J
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University of Pennsylvania
Schools of Medicine
United States
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