Stroke, Brain Networks, and Behavior

 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治疗方法。然而，这种竞争性更新的直接目标是确定与中风诱导的缺陷相关的结构和功能连接中断的模式，确定它们在恢复过程中如何变化，并观察它们是否预测结果。计算理论已经提出，基于底层网络架构，大脑的病变将产生不同的影响。控制功能恢复的机制可以类似地取决于网络架构。具体目标1测量了主要在网络内连接的外围网络与与更广泛地与其他大脑网络通信的中央网络受损后全脑功能连接（FC）的变化。我们分别测量皮质与白色物质通路损伤的影响。我们预测外周病变（无论是到灰质还是白色物质）将产生主要限于受影响网络的FC变化，而中央病变（无论是到灰质还是白色物质）将产生多网络FC变化。外围网络连接到输入/输出通路，并主要介导感觉或运动功能。中央网络通过关联途径连接，并主要介导注意力和记忆功能。具体目标2使用机器学习岭回归方法来测试感觉运动缺陷及其恢复更依赖于结构损伤和解剖断开的想法，而认知障碍更好地被多网络功能连接功能障碍所捕获。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fMRI是否可以提供有关个体患者恢复和结果的有用临床信息，超过急性行为，人口统计学变量和治疗量的测量。在具体目标3中，我们将神经影像学数据输入分类器，以预测个体患者的结果，以及预测康复干预的效果。最后，在具体目标4中，我们从相关性和预测转向机制。我们的基本假设是，局灶性损伤后功能连接的变化反映了脑动力学的改变，特别是区域间相位差的变异性降低。该假设通过相位测量进行经验检验，但也通过计算进行检验，使用基于生物病理学的模型，该模型可以模拟在患者中观察到的FC变化。重要的是，该模型还估计了受损大脑的信息容量，我们将其与患者的经验测量的模块化程度和行为缺陷的轮廓相关联。

 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.