Characterizing brain function changes during neuroplasticity within distributed neural systems

人类大脑功能的神经成像研究越来越多地使用功能连接(即专门用于特定功能的神经网络)的概念来表示大脑活动。这些分布式功能网络来自大脑广泛的底层结构网络，并可以通过神经可塑性(即大脑天生的重组神经网络的能力)概念所依据的生物过程进行动态修改。自2004年以来，我的NSERC资助的神经成像研究一直专注于描述感觉、感知、运动和认知处理过程中跨分布式神经系统的大脑活动-目标是提高非侵入性成像大脑功能的能力，并将这些知识转化为对大脑功能/功能障碍的高级评估。值得注意的是，该项目将功能神经成像的范围从灰质的专门处理区域(GM：网络中的节点)扩展到白质的功能活跃连接(WM：节点之间的连接)。它解决了使用功能磁共振成像(FMRI)检测WM激活这一历史上有争议的挑战。由于WM连接几乎占总神经组织的50%，这项工作解决了一个重大的历史盲点。在网络层面，对活跃的WM连接的直接测量提供了关键信息。与此同时，我们已经开始了纵向监测研究，以表征大脑活动随时间的变化。这项工作的重点是空间和时间的变化，作为一种衡量神经可塑性的方法，使用功能磁共振成像和磁脑电(M/EEG)。这两条中心研究路线最近被整合到统一的实验中，这些实验将分布式功能网络与神经可塑性的纵向监测相结合--这是这一应用的中心焦点。目前NSERC的应用将对功能连接和神经可塑性之间的关系进行更深入的研究。这项研究将利用任务依赖和静息状态分析来关注运动学习中涉及的功能连接。我的团队将通过对行为运动性能改善的纵向研究来检查随着时间的推移激活的变化。网络级别变化的常见模式将首先在WM中确定，然后在GM中确定。中心假设预测，在基于任务的状态评估和静态状态评估之间，共同的潜在机制一致地管理动态网络变化。具体地说，根据初步结果，我们预测西医的神经可塑性导致血流动力学反应的变异性降低--独立于基于任务的或静息状态的功能连接组分析。探索性分析还将把这种方法扩展到M/EEG，以检查功能连接性中的相关性。动态变化的潜在模式的知识反过来可以用来改善对各种应用程序的大脑功能的评估。

Neuroimaging studies of human brain function are increasingly representing brain activity using the concept of functional connectomes (i.e., a neural network dedicated to a specific function). These distributed functional networks emerge from the brain's extensive underlying structural networks and can be dynamically modified through biological processes that underlie the concept of neuroplasticity (i.e., the brain's innate ability to reorganize neural networks). Since 2004, my NSERC-funded neuroimaging research has focused on characterizing brain activity across distributed neural systems during sensory, perceptual, motor, and cognitive processing - with the goal to improve the ability to non-invasively image brain function and translate this knowledge into advanced evaluation of brain function/dysfunction. Notably, this program has expanded the reach of functional neuroimaging from specialized processing regions in gray matter (GM: the nodes within a network) to functionally active connections in white matter (WM: the connections between nodes). It has tackled the historically controversial challenge of detecting WM activation using functional magnetic resonance imaging (fMRI). As WM connections comprise almost 50% of total neural tissue, the work addressed a major historical blindspot. At the network level, direct measures of active WM connections provide critical information. In parallel, we have begun longitudinal monitoring studies to characterize brain activity changes over time. This work focuses on spatial and temporal changes as a measure of neuroplasticity using fMRI and magneto-/electro- encephalography (M/EEG). These two central research lines have recently been integrated into unified experiments that combine distributed functional networks with longitudinal monitoring of neuroplasticity - the central focus of this application. The current NSERC application will develop deeper investigations into the relationship between functional connectomes and neuroplasticity. The research will focus on functional connectomes involved in motor learning using task-dependent and resting state analyses. My team will examine activation changes over time using a longitudinal study of behavioural motor performance improvements. Common patterns of network level change will be identified first in WM and then in GM. The central hypothesis predicts that common underlying mechanisms govern dynamic network changes consistently between task-based and resting state evaluations. Specifically, based on preliminary results, we predict that neuroplasticity in WM leads to reduced variability in hemodynamic responses - independent of a task-based or resting state functional connectome analysis. Exploratory analyses will also extend this approach to M/EEG to examine correlates in functional connectivity. Knowledge of the underlying patterns for dynamic change can in turn be utilized to improve the evaluation of brain function across various applications.