Characterizing State Representation Impairments in People with Early Psychosis
早期精神病患者状态表征障碍的特征
基本信息
- 批准号:10377367
- 负责人:
- 金额:$ 54.88万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-04-01 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:Adolescent and Young AdultAlgorithmsBehaviorBehavioralBrainClinicalDataData CollectionData SetElectroencephalographyEquilibriumFailureFunctional Magnetic Resonance ImagingFunctional disorderGoalsHeterogeneityHumanImpairmentInterventionLinkMapsMeasuresModelingMonkeysMusNeuronal PlasticityNoiseParietalParticipantPatientsPatternPerformancePersonsPhaseProcessPsychophysiologyPsychosesQuality of lifeScanningShort-Term MemorySymptomsTestingTimeValidationVisitagedbaseclinical heterogeneitydensityearly psychosisexperimental studyfunctional magnetic resonance imaging/electroencephalographyguided inquiryindexinginformation processingnetwork modelsneural circuitneurophysiologynonhuman primatenovelrecruitrelating to nervous systemresearch clinical testingtreatment as usual
项目摘要
PROJECT SUMMARY: PROJECT 3
The purpose of PROJECT 3 is to determine how failures in information processing that supports state
representation in neural circuits relate to clinical heterogeneity in early psychosis. To this end, we will: (a)
Recruit people with early psychosis (N=125) and demographically similar healthy controls (N=125) aged 16-30
years; (b) Determine test-retest reliability of the DPX and Bandit tasks as assessments of state representation
processes; (c) Characterize behavioral performance and neurophysiology at baseline using the DPX and
Bandit tasks during simultaneous EEG-fMRI; (d) Follow patients for 6 months while they receive usual care, to
delineate their clinical trajectories; (e) Repeat the behavioral and EEG-fMRI assessments after six months
(N=100 retained per group). The data we acquire will allow us to examine the baseline relationships between
clinical and experimental measures, and also to investigate how changes in clinical and experimental
measures are related over a 6-month time period during a critical phase of illness.
The overall goal of PROJECT 3 is to permit neural macro-circuit links in humans to the behavioral and
neurophysiology experiments in monkeys and mice (PROJECTS 1 & 2). This will allow us to examine how
state representation dysfunctions observed in early psychosis -- along with EEG and fMRI-derived
neurophysiologic indices of activity timing, excitatory-inhibitory (E-I) balance, and noise -- relate to clinical
heterogeneity at baseline, and to heterogeneity in 6-month clinical trajectories. In Aim 1, we compute the retest
reliability of state representation measures. In Aim 2, we characterize and compare the features of behavior,
EEG and fMRI in early psychosis and healthy controls during state representation processes. In Aim 3,we re-
assess performance on the DPX and Bandit tasks during simultaneous EEG-FMRI, in order to characterize the
course of state representation dysfunctions in early psychosis during the critical first 6 months of
treatment. We will determine the extent to which changes in computational parameters derived from the
COMPUTATIONAL CORE, and neurophysiologic measures related to activity timing, E-I balance, and noise
(see TRANSLATIONAL NEUROPHYSIOLOGY CORE), map to distinct trajectories in quality of life using
causal discovery analyses. We will also test whether trajectories can be predicted from baseline features.
Additionally, our healthy control data set will permit us to explore normal patterns of stability vs. change as
observed over 6 months in adolescents and young adults.
项目概要:项目3
项目3的目的是确定支持状态的信息处理中的故障如何
神经回路中的表征与早期精神病的临床异质性有关。为此,我们会:(a)
招募16-30岁的早期精神病患者(N=125)和人口统计学相似的健康对照组(N=125)
(B)确定DPX和Bandit任务的重测信度,作为对州代表性的评估
(c)使用DPX表征基线时的行为表现和神经生理学,
在同步EEG-fMRI过程中进行Bandit任务;(d)在患者接受常规护理的同时,对患者进行6个月的随访,
描述他们的临床轨迹;(e)6个月后重复行为和EEG-fMRI评估
(每组保留100只)。我们获得的数据将使我们能够检查
临床和实验措施,并探讨如何改变临床和实验
这些措施是在疾病的关键阶段的6个月时间内采取的。
项目3的总体目标是允许人类的神经宏回路连接到行为和
猴子和老鼠的神经生理学实验(项目1和2)。这将使我们能够研究如何
在早期精神病中观察到的状态表征功能障碍--沿着EEG和fMRI
活动时间、兴奋-抑制(E-I)平衡和噪声的神经生理学指标-与临床相关
基线异质性和6个月临床轨迹的异质性。在目标1中,我们计算重测
国家代表性措施的可靠性。在目标2中,我们描述和比较行为的特征,
早期精神病和健康对照者在状态表征过程中的EEG和fMRI。在目标3中,我们重新-
在同步EEG-FMRI期间评估DPX和Bandit任务的性能,以表征
早期精神病在关键的前6个月期间的状态表征功能障碍的过程
治疗我们将确定计算参数的变化程度,
与活动时间、E-I平衡和噪声相关的神经生理学指标
(see翻译神经生理学核心),映射到不同的生活质量轨迹,使用
因果发现分析。我们还将测试是否可以从基线特征预测轨迹。
此外,我们的健康对照数据集将允许我们探索稳定与变化的正常模式,
在青少年和年轻人中观察超过6个月。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
ANGUS W MACDONALD其他文献
ANGUS W MACDONALD的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('ANGUS W MACDONALD', 18)}}的其他基金
Characterizing State Representation Impairments in People with Early Psychosis
早期精神病患者状态表征障碍的特征
- 批准号:
10597074 - 财政年份:2020
- 资助金额:
$ 54.88万 - 项目类别:
5/5-Cognitive Neuroscience Task Reliability & Clinical Applications Consortium
5/5-认知神经科学任务可靠性
- 批准号:
7812309 - 财政年份:2010
- 资助金额:
$ 54.88万 - 项目类别:
Imaging the Impact of Glutamate Liability Genes in Schizophrenia
谷氨酸责任基因对精神分裂症的影响成像
- 批准号:
7470504 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
5/5-Cognitive Neuroscience Task Reliability & Clinical Applications Consortium
5/5-认知神经科学任务可靠性
- 批准号:
8576889 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
5/5-Cognitive Neuroscience Task Reliability & Clinical Applications Consortium
5/5-认知神经科学任务可靠性
- 批准号:
7841790 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
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 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
Imaging the Impact of Glutamate Liability Genes in Schizophrenia
谷氨酸责任基因对精神分裂症的影响成像
- 批准号:
7567549 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
5/5-Cognitive Neuroscience Task Reliability & Clinical Applications Consortium
5/5-认知神经科学任务可靠性
- 批准号:
9095443 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
5/5-Cognitive Neuroscience Task Reliability & Clinical Applications Consortium
5/5-认知神经科学任务可靠性
- 批准号:
8882080 - 财政年份:2008
- 资助金额:
$ 54.88万 - 项目类别:
相似海外基金
Developing deep learning algorithms for studying infant brain and behavior relationships
开发深度学习算法来研究婴儿大脑和行为关系
- 批准号:
10263607 - 财政年份:2021
- 资助金额:
$ 54.88万 - 项目类别:
Real-time statistical algorithms for controlling neural dynamics and behavior
用于控制神经动力学和行为的实时统计算法
- 批准号:
10001503 - 财政年份:2018
- 资助金额:
$ 54.88万 - 项目类别:
Real-time statistical algorithms for controlling neural dynamics and behavior
用于控制神经动力学和行为的实时统计算法
- 批准号:
9789318 - 财政年份:2018
- 资助金额:
$ 54.88万 - 项目类别:
CCF-BSF: CIF: Small: Identification and Isolation of Malicious Behavior in Multi-Agent Optimization Algorithms
CCF-BSF:CIF:小:多代理优化算法中恶意行为的识别和隔离
- 批准号:
1714672 - 财政年份:2017
- 资助金额:
$ 54.88万 - 项目类别:
Standard Grant
EAGER: Using Learning Algorithms to Morph Product Behavior for Specific Task Contexts and Cognitive Styles of Users
EAGER:使用学习算法针对特定任务环境和用户认知风格来改变产品行为
- 批准号:
1548234 - 财政年份:2015
- 资助金额:
$ 54.88万 - 项目类别:
Standard Grant
CAREER: Human Behavior Assessment from Internet Usage: Foundations, Applications and Algorithms
职业:基于互联网使用的人类行为评估:基础、应用程序和算法
- 批准号:
1559588 - 财政年份:2015
- 资助金额:
$ 54.88万 - 项目类别:
Continuing Grant
CAREER: Human Behavior Assessment from Internet Usage: Foundations, Applications and Algorithms
职业:基于互联网使用的人类行为评估:基础、应用程序和算法
- 批准号:
1254117 - 财政年份:2013
- 资助金额:
$ 54.88万 - 项目类别:
Continuing Grant
Machine learning algorithms for automated analysis of player behavior in next-generation video games
用于自动分析下一代视频游戏中玩家行为的机器学习算法
- 批准号:
396001-2009 - 财政年份:2012
- 资助金额:
$ 54.88万 - 项目类别:
Collaborative Research and Development Grants
Machine learning algorithms for automated analysis of player behavior in next-generation video games
用于自动分析下一代视频游戏中玩家行为的机器学习算法
- 批准号:
396001-2009 - 财政年份:2011
- 资助金额:
$ 54.88万 - 项目类别:
Collaborative Research and Development Grants
Machine learning algorithms for automated analysis of player behavior in next-generation video games
用于自动分析下一代视频游戏中玩家行为的机器学习算法
- 批准号:
396001-2009 - 财政年份:2010
- 资助金额:
$ 54.88万 - 项目类别:
Collaborative Research and Development Grants