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中文摘要
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描述(由申请人提供):大脑在快速变化的环境中灵活参与不同功能网络的潜力对于促进各种行为和在损伤后自适应重组都是至关重要的。这种网络的可塑性既可以通过功能网络中局部连接强度的变化来实现,也可以通过整个大脑更加全球化的网络结构来实现。最近有大量研究使用功能性磁共振成像(FMRI)来评估休息时局部脑区网络的内在功能连通性。现在,使用图论工具来量化这种复杂的大脑组织的全局属性是可能的,在图论工具中,大脑区域被定义为节点,区域之间的连接被定义为边。这项建议的主要目标是应用局部的、特定于网络的连接性测量以及全球的图论方法来检验在两种不同的背景下的神经可塑性的能力:皮质功能的中断(急性和慢性)和特定的认知任务需求。关于脑损伤对网络组织的影响的研究主要集中在局部的、网络特定的损伤效应上,一般发现损伤影响到网络的一部分,影响的是相连但未受损的区域。局灶性损伤对全球脑组织的影响已经用模拟的损伤数据进行了初步检验,并提出了大脑区域对于跨网络整合信息特别重要,对于维持网络完整性是最关键的。在这里,我们将使用从经颅磁刺激(TMS)后的局灶性脑损伤患者和健康参与者收集的静息状态fMRI数据来验证这一预测。我们将检验这一假设,即大脑固有组织的扰动导致局部 减少了受影响的网络和大脑模块的全球重新配置。此外,我们假设,在脑损伤后重新配置的网络中,节点的角色是补偿的。研究网络重构的另一种方法是将静止的大脑组织与认知任务中的大脑组织进行比较。因此,我们还将检验这一假设,即与损伤后的适应性重组类似,特定的任务需求将导致全球和局部层面的网络组织迅速变化。这一提议将进一步了解脑组织及其在不同背景下的可塑性潜力,如脑损伤和日常生活的动态认知需求。此外,我们认为,网络方法,如那些应用在本提案中,可以提供经验数据,以严格协调地方主义者和分布主义者对大脑功能的描述。与NIH的任务相关,拟议研究中确定的大脑可塑性潜在的神经机制可以作为开发诊断生物标记物和认知治疗干预的目标,用于中风和创伤性脑损伤等普遍神经疾病造成的脑损伤患者的康复。
英文摘要
DESCRIPTION (provided by applicant): The brain's potential to flexibly engage different functional networks in a rapidly changing environment is crucial both for facilitating a wide variety of behaviors and adaptively reorganizing following damage. This network plasticity can emerge through both changes in the local connectivity strengths within functional networks and the more global network structure of the whole brain. A recent surge of studies has assessed the intrinsic functional connectivity of local networks of brain regions during rest using functionl MRI (fMRI). Quantifying the global properties of this complex brain organization is now possible using graph theoretical tools, in which brain regions are defined as nodes and connections between regions are defined as edges. The broad goal of this proposal is to apply local, network-specific connectivity measurements as well as global, graph theoretical methods to examine the capacity for neuroplasticity under two different contexts: disruption of cortical function (acute and chronic) and specific cognitive task demands. Studies of the effect of brain damage on network organization have focused on the local, network specific effects of damage, generally finding that damage to one portion of a network effects connected but undamaged regions. The consequence of focal damage on global brain organization has primarily been examined with simulated lesion data and it is proposed that brain regions particularly important for integrating information across networks, are most critical to maintaining network integrity. Here we will test this prediction by using resting state fMRI data collected from patients with focal brain lesions and healthy participants following transcranial magnetic stimulation (TMS). We will test the hypothesis that perturbation of intrinsic brain organization results in both local decreases in the affected network and global reconfiguration of brain modules. Moreover, we hypothesize that the roles of nodes within networks that have reconfigured following brain damage is compensatory. Another approach for investigating network reconfiguration is to compare brain organization at rest to that during a cognitive task. Thus, we will also test the hypothesis that, similar to the adaptive reorganization after damage, specific task demands will result in rapid alteration of network organization at both the global and local level. This proposa will further knowledge about brain organization and its potential for plasticity in various context, such as brain damage and the dynamics cognitive demands of daily life. Moreover, we propose that network approaches such as those applied in this proposal can provide empirical data to reconcile strictly localizationalist vs. distributionist accounts of brain function. Relevant to th NIH mission, the neural mechanisms underlying brain plasticity identified in the proposed studies can serve as targets for the development of diagnostic biomarkers as well as cognitive therapy interventions for rehabilitation of patients with brain damage from prevalent neurological disorders such as stroke and traumatic brain injury.
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Developing Behavioral and Neuroimaging Predictors of Stroke Recovery
Neural dynamics of human working memory networks
Neural dynamics of human working memory networks
Neural dynamics of human working memory networks
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