Stroke, Brain Networks, and Behavior
中风、大脑网络和行为
基本信息
- 批准号:9518014
- 负责人:
- 金额:$ 58.03万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-09-30 至 2020-06-30
- 项目状态:已结题
- 来源:
- 关键词:AcuteAffectAgeAnatomyAttentionBehaviorBehavioralBiophysicsBrainBrain InjuriesBrain regionChronicClinicalCognitiveCommunicationComplexComputer SimulationDataDependenceEducationFiberFrequenciesFunctional Magnetic Resonance ImagingFunctional disorderGoalsImageImpaired cognitionImpairmentIndividualInjuryInterventionLanguageLesionMachine LearningMeasurementMeasuresMediatingMemoryMemory LossMethodsModelingMotorNervous System PhysiologyNetwork-basedOutcomeOutputParietalPathway interactionsPatientsPatternPeripheralPhasePhysiologicalPhysiologyProblem behaviorRecoveryRecovery of FunctionRehabilitation therapyResearchSensorySignal TransductionSpinalStrokeTechniquesTestingTimeVisualVisual FieldsWeightbasebehavioral impairmentgray matterhemiparesisimprovedindividual patientmotor deficitneglectnetwork architecturenetwork modelsneuroimagingoutcome predictionpost strokepredictive modelingprogramspublic health relevancesegregationstroke patienttheoriesvisual motorwhite matter
项目摘要
DESCRIPTION (provided by applicant): The long-term goal of our research program is to develop new treatments for improving behavioral deficits post-stroke based on a formal theory of brain function and organization. However, the immediate goal of this competitive renewal is to identify patterns of disrupted structural and functional connectivity that are related to stroke-induced deficits, determine how they change during recovery, and see if they predict outcome. Computational theories have proposed that lesions to the brain will have different effects based on the underlying network architecture. Mechanisms controlling recovery of function may similarly depend on network architecture. Specific Aim 1 measures changes in whole brain functional connectivity (FC) after damage to peripheral networks that are mainly connected within the network vs. central networks that communicate more broadly with other brain networks. We measure separately the effect of cortical vs. white matter pathway damage. We predict that peripheral lesions (either to gray or white matter) will produce FC changes that are mainly limited to the affected network, while central lesions (either gray or white matter) will produce multi- network FC changes. Peripheral networks are connected to input/output pathways and mediate predominantly sensory or motor functions. Central networks are connected through association pathways and mediate predominantly attention and memory functions. Specific Aim 2 uses a machine learning ridge regression method to test the idea that sensory-motor deficits and their recovery are more dependent on structural damage and anatomical disconnection, whereas cognitive impairments are better captured by multi-network functional connectivity dysfunction. An important clinical goal of the project is to determine whether advanced neuroimaging techniques, such as structural imaging, DTI, and fMRI can provide useful clinical information on an individual patient's recovery and outcome, over and above that provided by measures of acute behavior, demographic variables, and amount of treatment. In Specific Aim 3 we enter neuroimaging data into a classifier to predict outcome in individual patients, as well as to predict the effect of rehabilitation interventions. Finally, in Specific Aim 4 we move from correlation and prediction to mechanism. Our basic hypothesis is that changes in functional connectivity after focal injury reflect alterations in brain dynamics, specifically a decrease in the variability of inter-regional phase differences. This hypothesis is tested empirically by phase measurements, but also computationally, using a biophysically based model that can simulate the FC changes observed in patients. Importantly, the model also estimates the information capacity of the damaged brain, which we will correlate with the patient's empirically measured degree of modularity and profile of behavioral deficits.
描述(由申请人提供):我们研究计划的长期目标是基于大脑功能和组织的正式理论,开发改善中风后行为缺陷的新疗法。然而,这种竞争性更新的直接目标是识别与中风导致的缺陷相关的结构和功能连接中断的模式,确定它们在复苏期间如何变化,并看看它们是否可以预测结果。计算理论提出,大脑的损伤将根据底层网络架构的不同而产生不同的影响。控制功能恢复的机制可以类似地依赖于网络体系结构。具体目标1衡量主要在网络内连接的外围网络与与其他大脑网络进行更广泛通信的中央网络受损后,全脑功能连接性(FC)的变化。我们分别测量皮质和白质通路损伤的影响。我们预测,外周性病变(无论是灰质还是白质)将产生主要局限于受影响网络的FC变化,而中心性病变(无论是灰质还是白质)将产生多网络FC变化。外周网络连接到输入/输出通路,并主要调节感觉或运动功能。中枢网络通过联想通路连接在一起,主要调节注意力和记忆功能。特定目的2使用机器学习岭回归方法来检验感觉-运动障碍及其恢复更依赖于结构损伤和解剖断开的观点,而认知障碍更好地被多网络功能连接障碍所捕获。该项目的一个重要临床目标是确定先进的神经成像技术,如结构成像、DTI和功能磁共振成像,是否可以提供有关单个患者恢复和结果的有用临床信息,而不是通过急性行为、人口统计变量和治疗量的测量提供的信息。在特定目标3中,我们将神经影像数据输入分类器以预测个别患者的结果,以及预测康复干预的效果。最后,在具体目标4中,我们从关联和预测转向机制。我们的基本假设是,局灶性损伤后功能连通性的变化反映了脑动力学的变化,特别是区域间时相差异的变异性降低。这一假设通过相位测量进行了经验验证,但也使用了基于生物物理学的模型进行了计算验证,该模型可以模拟在患者身上观察到的FC变化。重要的是,该模型还估计了受损大脑的信息容量,我们将把这一容量与患者经验测量的模块化程度和行为缺陷的特征相关联。
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)
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Maurizio Corbetta其他文献
Maurizio Corbetta的其他文献
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{{ truncateString('Maurizio Corbetta', 18)}}的其他基金
UNDERSTANDING THE EFFECTS OF STROKE USING FUNCTIONAL CONNECTIVITY MRI
使用功能连接 MRI 了解中风的影响
- 批准号:
7919316 - 财政年份:2009
- 资助金额:
$ 58.03万 - 项目类别:
UNDERSTANDING THE EFFECTS OF STROKE USING FUNCTIONAL CONNECTIVITY MRI
使用功能连接 MRI 了解中风的影响
- 批准号:
8109180 - 财政年份:2009
- 资助金额:
$ 58.03万 - 项目类别:
UNDERSTANDING THE EFFECTS OF STROKE USING FUNCTIONAL CONNECTIVITY MRI
使用功能连接 MRI 了解中风的影响
- 批准号:
8488454 - 财政年份:2009
- 资助金额:
$ 58.03万 - 项目类别:
UNDERSTANDING THE EFFECTS OF STROKE USING FUNCTIONAL CONNECTIVITY MRI
使用功能连接 MRI 了解中风的影响
- 批准号:
7738041 - 财政年份:2009
- 资助金额:
$ 58.03万 - 项目类别:
UNDERSTANDING THE EFFECTS OF STROKE USING FUNCTIONAL CONNECTIVITY MRI
使用功能连接 MRI 了解中风的影响
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8300153 - 财政年份:2009
- 资助金额:
$ 58.03万 - 项目类别:
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7193472 - 财政年份:2004
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$ 58.03万 - 项目类别:
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