Advancements in computational psychiatry allow us to isolate multiple, specific cognitive mechanisms that determine human behavior. This formal modeling framework generates quantitative parameter estimates that can serve as bridges between pathophysiology and psychopathology. A major goal of computational psychiatry is to translate these laboratory tools so that they can be used in the clinic. Two critical hurdles need to be overcome. First, the enhanced validity and sensitivity of computational metrics needs to be established relative to standard behavioral performance metrics in key psychiatric and nonpsychiatric populations. We propose to do that by addressing a range of cognitive and motivational domains that have been strongly implicated in psychopathology, including working and episodic memory, visual perception, reinforcement learning, and effort based decision making. Second, we need to establish and optimize the psychometrics of these computational metrics so that they can be used as tools in treatment development, treatment evaluation, longitudinal, and genetic studies. These powerful metrics must have adequate test-retest reliability, and not be limited by ceiling and floor effects. We propose to develop these methods using an open, flexible, and scalable framework and demonstrate that they provide valid data both in the laboratory and in large-scale Internet-based data collection, facilitating ?big data? studies of cognitive processes. To this end, the current project will leverage the expertise of Cognitive Neuroscience Task Reliability and Clinical applications in Serious mental illness (CNTRACS) consortium, a multi-site research group with an established record of rapid cognitive tool development and dissemination.
Aim 1 is to establish that model based parameters for the measurement of cognitive function are more sensitive than standard behavioral methods in assessing deficits across a range of common mental disorders, and have an enhanced capacity to predict clinical symptoms and real-world functioning, with a sample of 180 patients with psychotic and affective disorders (both medicated and unmedicated) and 100 healthy controls.
Aim 2 is to measure and optimize the psychometric properties (test re-test reliability, internal validity, floor and absence of ceiling and practice effects) of computational parameters described in Aim 1, in a new sample of 180 psychiatric patients and 100 healthy controls.
Aim 3 is to establish the feasibility and replicability of model-based analytic approaches outside the laboratory for assessing RDoC dimensions of interest, and to assess their relationships to variation in psychotic-like experience, depression and anhedonia, as well as real- world functioning in a community sample of 10,000 recruited over the Internet.
Aim 4 is to validate key model based parameters against well-characterized neurophysiological measures acquired using EEG recordings during task performance. Successful completion of these Aims will significantly advance the field by providing easily administered and scalable web-based tools for estimating the integrity of key neural systems that underlie normal cognition and motivation and form the basis of common forms of cognitive and affective psychopathology.

Public Health Relevance

Advancements in computational psychiatry allow us to isolate the contributions of multiple, specific, cognitive mechanisms that contribute to the behavior that we see in everyday life. This approach fills a critical unmet need in psychiatry by generating mathematically precise variables that can bridge the gap between pathophysiology and psychopathology. The main goals of the current project are to: 1) test the idea that computational metrics have enhanced validity and sensitivity relative to standard behavioral performance metrics in key psychiatric and nonpsychiatric populations and to relate these metrics to EEG markers of brain function; and 2) establish and optimize the psychometrics of these computational metrics so that they can be used as tools in treatment development, longitudinal studies, and genetic studies. !

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Morris, Sarah E
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University of Maryland Baltimore
Schools of Medicine
United States
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