Cognitive Neurocomputational Task Reliability and Clinical Applications Consortium: Study 3 (CNTRACS)
Cognitive Neurocomputational Task Reliability and Clinical Applications Consortium
In Study 3, all tasks and models from Aim 1 will be evaluated in a large community sample of online participants recruited through Prolific.
Aim 3 will establish 1) requirements for testing computational algorithms in population-based samples, 2) whether task length is optimal, and 3) whether computational parameters help predict psychopathology (hallucinations and delusions in particular) in the general population, and how these predictors may be impacted by demographic variables, such as race, ethnicity, gender, age and other social determinants of health.
Participant demographics will be matched to the U.S. Census 2023 American Community Survey (ACS) data release (updated as available). MPI MacDonald has successfully recruited large online samples for over a decade.
Specifically, researchers will recruit two samples of n=2,000 to preregister hypotheses and then n=3,000 to independently confirm those hypotheses. Multiple methods will affirm the highest quality human self-report and behavioral data for online samples.
Researchers use Prolific, which is known for high quality research participation, maintaining low levels of bots through strategies such as requiring a photo ID and a waiting period. Second, inclusion criteria will require an approval rating of 90-100% from previous researchers. Participants will complete three sessions, each limited to ~60 min to avoid burnout.
調査の概要
状態
詳細な説明
Researchers will recruit two samples of 2,000 to preregister hypotheses and then 3,000 to independently confirm those hypotheses.
Multiple methods will affirm the highest quality human self-report and behavioral data for online samples. First, researchers will use Prolific, which is known for high quality research participation, maintaining low levels of bots through strategies such as requiring a photo ID and a waiting period. Participants will complete 3 sessions, each limited to ~60 min to avoid burnout. Researchers anticipate complete data from 74% of participants, based on data completion rates in the previous online study.
General Cognitive Ability. Researchers will include standard general cognitive measures to assess the specificity of effects beyond general cognitive impairment. Specifically, researchers 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.
Self-Report Questionnaires: Researchers 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. Researchers will also use the Peters Delusion Inventory to assess the extent of delusional thinking, supplemented by the Green Paranoia Scale, which provides additional coverage of persecutory beliefs. To assess specificity, researchers will use the Patient Health Questionnaire (PHQ-9) to assess depression, the Generalized Anxiety Disorder Scale (GAD-7)78 to assess anxiety, and the Motivation and Pleasure Scale-Self-Report (MAP-SR) to assess anhedonia and motivation with the addition of additional measures of psychosis (Aberrant Salience Inventory, Schizotypal Personality Questionnaire - Brief), alcohol use (AUDIT), cannabis use (Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Use Inventory), and tobacco and other substance use (World Health Organization (WHO) Alcohol, Smoking and Substance Involvement Screening Test), as researchers will not have a Stuctured Clinical Interview (SCID) for on-line participants.
Researchers will also measure daily functioning using the World Health Organization Disability Assessment Schedule (WHODAS) 2.0. Validity questions and procedures will be included. These measures will take 30 mins to complete, as researchers found in prior studies. Researchers 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.
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前:Deanna Barch, PhD
- 電話番号:314-935-8729
- メール:dbarch@wustl.edu
研究場所
-
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Minnesota
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Minneapolis、Minnesota、アメリカ、55455
- 募集
- University of Minnesota
-
コンタクト:
- Angus MacDonald III, PhD
- 電話番号:612-624-3813
- メール:angus@umn.edu
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コンタクト:
- UMN Study Team
- 電話番号:651-558-1275
- メール:CNTRACS@umn.edu
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Missouri
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St Louis、Missouri、アメリカ、63130
- 募集
- Washington University
-
コンタクト:
- Deanna Barch, PhD
- 電話番号:314-935-8729
- メール:dbarch@wustl.edu
-
-
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
Inclusion Criteria:
- Residents of the U.S.
- between 18-59 years of age
Exclusion Criteria:
- Resides outside of U.S.
- under 18 or over 59 years of age
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:基礎科学
- 割り当て:なし
- 介入モデル:単一グループの割り当て
- マスキング:なし(オープンラベル)
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Sine Wave Speech Task Performance
時間枠:1 hour
|
Percentage of trials coded as speech or not vs Dprime |
1 hour
|
|
Minnesota Trust Game Performance
時間枠:1 hour
|
Percent of trusting choices depending on decision-agent and temptation vs. spite-guilt beliefs estimate |
1 hour
|
|
Dynamic Trust Game Performance
時間枠:1 hour
|
Trust indexed by mean investment versus learning rates |
1 hour
|
|
Motion Perception Task Performance
時間枠:1 hour
|
With 15 degrees of initial or changed direction vs. the probability of failing to update |
1 hour
|
|
Volatility Task Performance
時間枠:1 hour
|
Percent correct bag captures during periods of stability vs. instability - patients worse in both, but particularly impaired ininstability vs. Environmental volatility belief parameter |
1 hour
|
|
Choice Probabilistic Reversal Performance Task
時間枠:1 hour
|
Win-switch rate (higher in patients, particularly paranoid patients) vs. (prior on volatility belief updating in response tovolatility |
1 hour
|
|
Kamin Blocking Task Performance
時間枠:1 hour
|
Number of times participant predict allergy following blocked cue vs. prediction errors and learning rates |
1 hour
|
協力者と研究者
協力者
捜査官
- 主任研究者:Deanna Barch, PhD、Washington University School of Medicine
出版物と役立つリンク
一般刊行物
- 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.
- 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.
- 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.
- 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.
- 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.
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
追加の関連 MeSH 用語
その他の研究ID番号
- 201904012: Study 3
- 2R01MH084840 (米国 NIH グラント/契約)
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
IPD プランの説明
Our plan is to share de-identified participant data through the National Data Archive (NDA) as required bythe National Institute of Mental Health (NIMH)
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。