5/5-Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium

5/5-认知神经计算任务可靠性

• 批准号: 10459392
• 负责人: ANGUS W MACDONALD
• 金额: $ 36.35万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10459392
• 关键词: Address; Affective; Anhedonia; Assessment tool; Attention; Behavior; Behavioral; Big Data; Brain; Clinic; Clinical; Cognition; Cognitive; Communities; Complement; Data; Data Collection; Decision Making; Dimensions; Electroencephalography; Episodic memory; Evaluation Studies; Floor; Functional disorder; Genetic; Genetic study; Goals; Gold; Human; Impaired cognition; Impairment; Individual; Internet; Intervention; Laboratories; Learning; Life; Longitudinal Studies; Mathematics; Measurement; Measures; Mental Depression; Mental disorders; Methods; Modeling; Mood Disorders; Motivation; Parameter Estimation; Patient Recruitments; Patients; Pattern; Perception; Performance; Population; Positive Valence; Productivity; Property; Psychiatry; Psychological reinforcement; Psychometrics; Psychopathology; Psychotic Disorders; Research; Research Domain Criteria; Research Personnel; Resources; Sampling; Short-Term Memory; Site; Specific qualifier value; Specificity; Symptoms; System; Task Performances; Testing; Translating; Variant; Visual Perception; Work; behavior measurement; clinical application; clinical predictors; cognitive function; cognitive neuroscience; cognitive process; cognitive system; cognitive testing; computerized tools; discounting; flexibility; functional outcomes; interest; neural; neuromechanism; neurophysiology; novel; population based; precision medicine; psychologic; psychotic-like experiences; recruit; severe mental illness; spatiotemporal; therapy development; tool; tool development; web-based tool

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.

计算精神病学的进步使我们能够分离出多种特定的认知机制

项目成果

Functional network changes and cognitive control in schizophrenia.

• DOI: 10.1016/j.nicl.2017.05.001
• 发表时间: 2017
• 期刊: NeuroImage. Clinical
• 作者: Ray KL;Lesh TA;Howell AM;Salo TP;Ragland JD;MacDonald AW;Gold JM;Silverstein SM;Barch DM;Carter CS
• 通讯作者: Carter CS

What is not working in working memory?

工作记忆中什么不起作用？

• DOI: 10.1016/j.biopsych.2010.08.005
• 发表时间: 2010
• 期刊: Biological psychiatry
• 影响因子: 10.6
• 作者: MacDonald3rd,AngusW

Theories of psychopathology: Introduction to a special section.

精神病理学理论：特殊部分简介。

• DOI: 10.1037/abn0000824
• 发表时间: 2023
• 期刊: Journal of psychopathology and clinical science
• 作者: Kent,JerillynS;Markon,Kristian;MacDonald,AngusW
• 通讯作者: MacDonald,AngusW

Self versus informant reports on the specific levels of functioning scale: Relationships to depression and cognition in schizophrenia and schizoaffective disorder.

• DOI: 10.1016/j.scog.2017.04.001
• 发表时间: 2017-09-01
• 期刊: Schizophrenia research. Cognition
• 作者: Ermel, Julia;Carter, Cameron S;Barch, Deanna M
• 通讯作者: Barch, Deanna M

Explicit and implicit reinforcement learning across the psychosis spectrum.

• DOI: 10.1037/abn0000259
• 发表时间: 2017-07
• 期刊: Journal of abnormal psychology
• 影响因子: 4.6
• 作者: Barch DM;Carter CS;Gold JM;Johnson SL;Kring AM;MacDonald AW;Pizzagalli DA;Ragland JD;Silverstein SM;Strauss ME
• 通讯作者: Strauss ME