Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium (CNTRACS): Study 1 (CNTRACS)
Cognitive Neurocomputational Task Reliability & Clinical Applications Consortium
In Study 1, investigators will test the ability of each of the seven paradigms to differentiate between schizophrenia (SZ)/schizoaffective disorder (SZA) and matched healthy control subjects (HC) and to predict individual differences in clinical measures of hallucinations and delusions (as well as measures of function). Investigators will also directly compare the tasks in terms of predictive ability and psychometric properties and determine whether the tasks share variance or explain unique variance. This will include determining whether hallucinations and delusions are best explained by separate underlying processes or by a single process. Investigators will also test whether the model-based parameters from these studies are more precise and sensitive than standard behavioral methods for assessing deficits across psychotic disorders and for predicting hallucinations and delusions (Aim 1). Investigators will also validate key model-based parameters against well-characterized neurophysiological measures using electroencephalogram (EEG) (Aim 4). The data from Study 1 will also be used to optimize (e.g., length, floor, and ceiling effects) tasks for Study 2. To generate a sample with sufficient variation in hallucinations and delusions, investigators will recruit the following groups that balance feasibility with clinical relevance: A) healthy controls (HC) [HC, N=125]; B) schizophrenia and schizoaffective disorder [SZ/SZA, N=200]). For Study 1, investigators will ensure that 40 patients are unmedicated. Investigators anticipate a ~20% screen fail/drop-out rate based on our prior Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia Consortium (CNTRACS) studies. Thus, investigators will run an additional 40 SZ/SZA and 15 HC. All sites have experience recruiting patients in each of these groups as indicated by our past successful studies, and the fact that investigators have been able to recruit patients with significant hallucinations and delusions in all groups. Investigators will also validate key model-based parameters against well-characterized neurophysiological measures using electroencephalogram (EEG) (Aim 4). The data from Study 1 will also be used to optimize (e.g., length, floor, and ceiling effects) tasks for Study 2. To generate a sample with sufficient variation in hallucinations and delusions, investigators will recruit the following groups that balance feasibility with clinical relevance: A) healthy controls (HC) [HC, N=125]; B) schizophrenia and schizoaffective disorder [SZ/SZA, N=200]). For Study 1, investigators will ensure that 40 patients are unmedicated. Investigators anticipate a ~20% screen fail/drop-out rate based on our prior Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia Consortium (CNTRACS) studies. Thus, investigators will run an additional 40 SZ/SZA and 15 HC. All sites have experience recruiting patients in each of these groups as indicated by our past successful studies, and the fact that investigators have been able to recruit patients with significant hallucinations and delusions in all groups.
研究概览
地位
详细说明
Each participant will complete 4 sessions (~3 hours each) within 3 weeks, as in our prior studies. The first session includes clinical, self-report, proxy function, and community function assessments. Sessions 2-4 are for task and electroencephalogram (EEG) measurements. Each participant will complete 7 tasks across these sessions, while EEG is recorded. Task order will be counterbalanced across sessions. Clinical Assessment and Self-Report: A trained assessor will diagnose participants using the Structured Clinical Interview (SCID-5) for Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) Text Revision 59 (TR59). Clinicians will rate hallucination and delusion symptom severity with the Chicago Hallucinations Assessment Tool (CHAT) and the Brief Psychiatric Rating Scale (BPRS). Investigators will use the Psychotic Symptom Rating Scales (PSYRATS) to evaluate the degree of preoccupation, conviction, distress, and life disruption associated with hallucinations and delusions elicited on the BPRS and CHAT. To address specificity, investigators will administer the Clinical Assessment Interview for Negative Symptoms (CAINS). For self-report, investigators will use the Beliefs about Voices Scale, which assesses attributions about the origin and power of voices, and the Yale Control Over Perceptual Experiences (COPE) scale, which evaluates the extent to which people feel they can exert control over their voices. Investigators also will use the Peters Delusion Inventory to assess the extent of delusional thinking, supplemented by the Green Paranoia Scale to provide additional coverage of persecutory beliefs. To assess specificity, investigators will use the Patient Health Questionnaire (PHQ-9) to assess depression, the Generalized Anxiety Disorder Scale (GAD-7) to assess anxiety, and the Motivation and Pleasure Scale-Self-Report (MAP-SR) to assess anhedonia and motivation. Investigators will conduct a principal components analysis (PCA) on measures of the same construct (e.g., delusions) and perform primary analyses using the first principal component, followed by secondary analyses of specific measures False Discovery Rate (FDR) correction. Community Function: Patients will complete the Specific Levels of Functioning (SLOF) scale, a validated scale of work and community function. National Institute of Mental Health (NIMH) Common Data Elements: Investigators will acquire the additional NIMH Tier 1 Common Data elements (e.g., World Health Organization Disability Assessment Schedule 2 (WHODAS 2.0) & DSM-5 Symptom Measure). General Cognitive Ability. Investigators will include standard general cognitive measures to assess the specificity of effects beyond general cognitive impairment. Specifically, investigators will include validated online measures from TestMyBrain (https://www.testmybrain.org/using-tmb/research.html; Digit Symbol Coding, Vocabulary, and Matrix Reasoning), as in our prior work in both clinical and healthy populations. Electroencephalogram (EEG) Procedures: During the above tasks, 64-channel EEG will be recorded from Direct Current (DC)-130 Hertz (Hz) using a 500 Hz sampling rate. Offline data preprocessing will be conducted using Matlab toolboxes EEGLAB and ERPLAB, including segmenting, baseline correction, Independent Component Analysis (ICA)-based artifact correction, artifact rejection, and filtering.
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名:Deanna Barch, Ph.D.
- 电话号码:314-935-8729
- 邮箱:dbarch@wustl.edu
研究联系人备份
- 姓名:Clinical Coordinator
- 电话号码:314-747-2160
- 邮箱:ingram@wustl.edu
学习地点
-
-
California
-
Irvine、California、美国、92868
- 招聘中
- University of California, Irvine
-
接触:
- Cameron Carter, MD
- 电话号码:916-734-2972
- 邮箱:cscarte1@hs.uci.edu
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Illinois
-
Chicago、Illinois、美国、60637
- 招聘中
- The University of Chicago
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接触:
- Molly Erickson, Ph.D.
- 电话号码:(773) 702-5855
- 邮箱:merickson1@yoda.bsd.uchicago.edu
-
接触:
- Nicole Ogbuagu, B.S.
- 电话号码:773-795-2633
- 邮箱:nicoleogbuagu@uchicago.edu
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Maryland
-
Baltimore、Maryland、美国、21201
- 招聘中
- University of Maryland, Baltimore
-
接触:
- James Gold, MD
- 电话号码:(410) 402-7871
- 邮箱:jgold@som.umaryland.edu
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Minnesota
-
Minneapolis、Minnesota、美国、55455
- 招聘中
- University of Minnesota
-
接触:
- Angus Macdonald III, Ph.D.
- 电话号码:612-624-3813
- 邮箱:angus@umn.edu
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接触:
- Alyssa Zimmerman, BA
- 电话号码:651-558-1275
- 邮箱:zimme857@umn.edu
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Missouri
-
St Louis、Missouri、美国、63108
- 招聘中
- Washington University
-
接触:
- Deanna Barch, Ph.D.
- 电话号码:314-935-8729
- 邮箱:dbarch@wustl.edu
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接触:
- Lori Ingram
- 电话号码:314-747-2160
- 邮箱:ingram@wustl.edu
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-
参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
描述
Inclusion Criteria:
General Inclusion
- age 18 to 59
- no substance disorder in past six months (confirmed with urine drug screens and breathalyzers);
- no clinically significant head injury or neurological disease. 4. Wechsler Test of Adult Reading (WTAR) score =16+.
Group Specific Criteria: Schizophrenia/Schizoaffective Disorder (SZ):
- Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) diagnosis of schizophrenia or schizoaffective;
- no medication changes in prior month or anticipated in the next month or not taking medications
- outpatient or partial hospital if unmedicated
Healthy Controls (HC):
- no Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) Psychosis or Bipolar Disorder;
- no family history of psychosis or bipolar
Exclusion Criteria:
Healthy Controls (HC):
- Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) Psychosis or Bipolar Disorder diagnosis
- Family history of psychosis or bipolar
Schizophrenia/Schizoaffective Disorder (SZ/SZA): additional exclusion criteria for the unmedicated individuals are:
- not in acute exacerbation requiring immediate hospitalization or treatment
- self-reported medication non-compliance for at least the four previous weeks 3)never having started medications
学习计划
研究是如何设计的?
设计细节
- 主要用途:基础科学
- 分配:不适用
- 介入模型:单组作业
- 屏蔽:无(打开标签)
研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Motion Perception Task Performance
大体时间:1 hour
|
With 15 degrees of initial or changed direction vs. the probability of failing to update. |
1 hour
|
|
Sine Wave Speech Task Performance
大体时间:1 hour
|
Percentage of trials coded as speech or not vs Dprime |
1 hour
|
|
Volatility Task Performance
大体时间:1 hour
|
Percent correct bag captures during periods of stability vs. instability - patients worse in both, but particularly impaired in instability vs. Environmental volatility belief parameter |
1 hour
|
|
Choice Probabilistic Reversal Performance Task
大体时间:1 hour
|
in-switch rate - higher in patients, particularly paranoid patients vs. prior on volatility belief updating in response to volatility |
1 hour
|
|
Kamin Blocking Task Performance
大体时间:1 hour
|
number of times they predict allergy following blocked cue vs. prediction errors and learning rates |
1 hour
|
|
Minnesota Trust Game Performance
大体时间:1 hour
|
Percent of trusting choices depending on decision-agent and temptation vs. spite-guilt beliefs estimate |
1 hour
|
|
Dynamic Trust Game Performance
大体时间:1 hour
|
Trust indexed by mean investment versus learning rates |
1 hour
|
合作者和调查者
合作者
调查人员
- 首席研究员:Deanna Barch, Ph.D.、Washington University School of Medicine
出版物和有用的链接
一般刊物
- Li C, Boudewyn MA, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW 3rd, Ragland JD, Silverstein SM, Yonelinas A, Luck SJ, Erickson MA. Impacted and preserved sub-domains of cognitive control in schizophrenia. Neuropsychologia. 2026 Sep 9;230:109503. doi: 10.1016/j.neuropsychologia.2026.109503. Epub 2026 May 25.
- Boudewyn MA, Erickson MA, Winsler K, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW 3rd, Ragland JD, Silverstein SM, Yonelinas AP, Luck SJ. Assessing Trial-by-Trial Electrophysiological and Behavioral Markers of Attentional Control and Sensory Precision in Psychotic and Mood Disorders. Schizophr Bull. 2025 Mar 14;51(2):543-555. doi: 10.1093/schbul/sbae038.
- MacDonald AW, Patzelt E, Kurth-Nelson Z, Barch DM, Carter CS, Gold JM, Ragland JD, Silverstein SM. Computational modeling of reversal learning impairments in schizophrenia and bipolar disorder reveals shared failure to exploit rewards. J Psychopathol Clin Sci. 2025 Apr;134(3):262-271. doi: 10.1037/abn0000944. Epub 2025 Mar 10.
- Erickson MA, Boudewyn MA, Winsler K, Li C, Barch DM, Carter CS, Frank MJ, Gold JM, MacDonald AW 3rd, Ragland JD, Silverstein SM, Yonelinas A, Luck SJ. Dysfunctional Alpha Modulation as a Mechanism of Working Memory Impairment in Serious Mental Illness. Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Dec;9(12):1271-1280. doi: 10.1016/j.bpsc.2024.07.022. Epub 2024 Aug 6.
- Barch DM, Boudewyn MA, Carter CC, Erickson M, Frank MJ, Gold JM, Luck SJ, MacDonald AW 3rd, Ragland JD, Ranganath C, Silverstein SM, Yonelinas A. Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions. Curr Top Behav Neurosci. 2023;63:19-60. doi: 10.1007/7854_2022_391.
- Boudewyn MA, Erickson MA, Winsler K, Ragland JD, Yonelinas A, Frank M, Silverstein SM, Gold J, MacDonald AW 3rd, Carter CS, Barch DM, Luck SJ. Managing EEG studies: How to prepare and what to do once data collection has begun. Psychophysiology. 2023 Nov;60(11):e14365. doi: 10.1111/psyp.14365. Epub 2023 Jun 14.
- Rac-Lubashevsky R, Cremer A, Collins AGE, Frank MJ, Schwabe L. Neural Index of Reinforcement Learning Predicts Improved Stimulus-Response Retention under High Working Memory Load. J Neurosci. 2023 Apr 26;43(17):3131-3143. doi: 10.1523/JNEUROSCI.1274-22.2023. Epub 2023 Mar 17.
- Kwashie AN, Ma Y, Barch DM, Chafee M, Ragland JD, Silverstein SM, Carter CS, Gold JM, MacDonald AW 3rd. Comparing the functional neuroanatomy of proactive and reactive control between patients with schizophrenia and healthy controls. Cogn Affect Behav Neurosci. 2023 Feb;23(1):203-215. doi: 10.3758/s13415-022-01036-6. Epub 2022 Nov 23.
- Liu RG, Frank MJ. Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning. Artif Intell. 2022 Nov;312:103770. doi: 10.1016/j.artint.2022.103770. Epub 2022 Aug 5.
- Pratt DN, Barch DM, Carter CS, Gold JM, Ragland JD, Silverstein SM, MacDonald AW. Reliability and Replicability of Implicit and Explicit Reinforcement Learning Paradigms in People With Psychotic Disorders. Schizophr Bull. 2021 Apr 29;47(3):731-739. doi: 10.1093/schbul/sbaa165.
- Geana A, Barch DM, Gold JM, Carter CS, MacDonald AW 3rd, Ragland JD, Silverstein SM, Frank MJ. Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022 Oct;7(10):1035-1046. doi: 10.1016/j.bpsc.2021.03.017. Epub 2021 Apr 18.
- Hamid AA, Frank MJ, Moore CI. Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell. 2021 May 13;184(10):2733-2749.e16. doi: 10.1016/j.cell.2021.03.046. Epub 2021 Apr 15.
- Moran EK, Gold JM, Carter CS, MacDonald AW 3rd, Ragland JD, Silverstein SM, Luck SJ, Barch DM. Both unmedicated and medicated individuals with schizophrenia show impairments across a wide array of cognitive and reinforcement learning tasks. Psychol Med. 2022 Apr;52(6):1115-1125. doi: 10.1017/S003329172000286X. Epub 2020 Aug 17.
- Pedersen ML, Frank MJ. Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data. Comput Brain Behav. 2020 Dec;3(4):458-471. doi: 10.1007/s42113-020-00084-w. Epub 2020 May 26.
- Ray KL, Ragland JD, MacDonald AW, Gold JM, Silverstein SM, Barch DM, Carter CS. Dynamic reorganization of the frontal parietal network during cognitive control and episodic memory. Cogn Affect Behav Neurosci. 2020 Feb;20(1):76-90. doi: 10.3758/s13415-019-00753-9.
- Smucny J, Iosif AM, Eaton NR, Lesh TA, Ragland JD, Barch DM, Gold JM, Strauss ME, MacDonald AW, Silverstein SM, Carter CS. Latent Profiles of Cognitive Control, Episodic Memory, and Visual Perception Across Psychiatric Disorders Reveal a Dimensional Structure. Schizophr Bull. 2020 Jan 4;46(1):154-162. doi: 10.1093/schbul/sbz025.
- Gold JM, Barch DM, Feuerstahler LM, Carter CS, MacDonald AW, Ragland JD, Silverstein SM, Strauss ME, Luck SJ. Working Memory Impairment Across Psychotic disorders. Schizophr Bull. 2019 Jun 18;45(4):804-812. doi: 10.1093/schbul/sby134.
- Smucny J, Barch DM, Gold JM, Strauss ME, MacDonald AW 3rd, Boudewyn MA, Ragland JD, Silverstein SM, Carter CS. Cross-diagnostic analysis of cognitive control in mental illness: Insights from the CNTRACS consortium. Schizophr Res. 2019 Jun;208:377-383. doi: 10.1016/j.schres.2019.01.018. Epub 2019 Jan 28.
- Ermel J, Carter CS, Gold JM, MacDonald AW 3rd, Daniel Ragland J, Silverstein SM, Strauss ME, Barch DM. Self versus informant reports on the specific levels of functioning scale: Relationships to depression and cognition in schizophrenia and schizoaffective disorder. Schizophr Res Cogn. 2017 May 9;9:1-7. doi: 10.1016/j.scog.2017.04.001. eCollection 2017 Sep.
- Barch DM, Carter CS, Gold JM, Johnson SL, Kring AM, MacDonald AW, Pizzagalli DA, Ragland JD, Silverstein SM, Strauss ME. Explicit and implicit reinforcement learning across the psychosis spectrum. J Abnorm Psychol. 2017 Jul;126(5):694-711. doi: 10.1037/abn0000259. Epub 2017 Apr 13.
- Lopez-Garcia P, Lesh TA, Salo T, Barch DM, MacDonald AW 3rd, Gold JM, Ragland JD, Strauss M, Silverstein SM, Carter CS. The neural circuitry supporting goal maintenance during cognitive control: a comparison of expectancy AX-CPT and dot probe expectancy paradigms. Cogn Affect Behav Neurosci. 2016 Feb;16(1):164-75. doi: 10.3758/s13415-015-0384-1.
- Ragland JD, Ranganath C, Harms MP, Barch DM, Gold JM, Layher E, Lesh TA, MacDonald AW 3rd, Niendam TA, Phillips J, Silverstein SM, Yonelinas AP, Carter CS. Functional and Neuroanatomic Specificity of Episodic Memory Dysfunction in Schizophrenia: A Functional Magnetic Resonance Imaging Study of the Relational and Item-Specific Encoding Task. JAMA Psychiatry. 2015 Sep;72(9):909-16. doi: 10.1001/jamapsychiatry.2015.0276.
- Silverstein SM, Harms MP, Carter CS, Gold JM, Keane BP, MacDonald A 3rd, Ragland JD, Barch DM. Cortical contributions to impaired contour integration in schizophrenia. Neuropsychologia. 2015 Aug;75:469-80. doi: 10.1016/j.neuropsychologia.2015.07.003. Epub 2015 Jul 6.
- Sheffield JM, Repovs G, Harms MP, Carter CS, Gold JM, MacDonald AW 3rd, Daniel Ragland J, Silverstein SM, Godwin D, Barch DM. Fronto-parietal and cingulo-opercular network integrity and cognition in health and schizophrenia. Neuropsychologia. 2015 Jul;73:82-93. doi: 10.1016/j.neuropsychologia.2015.05.006. Epub 2015 May 13.
- Barch DM, Sheffield JM. Cognitive impairments in psychotic disorders: common mechanisms and measurement. World Psychiatry. 2014 Oct;13(3):224-32. doi: 10.1002/wps.20145.
- Strauss ME, McLouth CJ, Barch DM, Carter CS, Gold JM, Luck SJ, MacDonald AW 3rd, Ragland JD, Ranganath C, Keane BP, Silverstein SM. Temporal stability and moderating effects of age and sex on CNTRaCS task performance. Schizophr Bull. 2014 Jul;40(4):835-44. doi: 10.1093/schbul/sbt089. Epub 2013 Jul 1.
- Owoso A, Carter CS, Gold JM, MacDonald AW 3rd, Ragland JD, Silverstein SM, Strauss ME, Barch DM. Cognition in schizophrenia and schizo-affective disorder: impairments that are more similar than different. Psychol Med. 2013 Dec;43(12):2535-45. doi: 10.1017/S0033291713000536. Epub 2013 Mar 25.
- Keane BP, Silverstein SM, Barch DM, Carter CS, Gold JM, Kovacs I, MacDonald AW 3rd, Ragland JD, Strauss ME. The spatial range of contour integration deficits in schizophrenia. Exp Brain Res. 2012 Aug;220(3-4):251-9. doi: 10.1007/s00221-012-3134-4. Epub 2012 Jun 19.
- Barch DM, Mathalon DH. Using brain imaging measures in studies of procognitive pharmacologic agents in schizophrenia: psychometric and quality assurance considerations. Biol Psychiatry. 2011 Jul 1;70(1):13-8. doi: 10.1016/j.biopsych.2011.01.004. Epub 2011 Feb 21.
研究记录日期
研究主要日期
学习开始 (估计的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他相关的 MeSH 术语
其他研究编号
- 201904012: Study 1
- 2R01MH084840 (美国 NIH 拨款/合同)
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)?
IPD 计划说明
Our plan is to share de-identified participant data through the National Data Archive (NDA) as required by the National Institute of Mental Health (NIMH)
药物和器械信息、研究文件
研究美国 FDA 监管的药品
研究美国 FDA 监管的设备产品
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