Reconfiguration of brain networks and its association with cognition across the lifespan

老年人占美国人口的15%，预计到2050年将超过20%。因此，我们必须提高对健康大脑衰老的生理学以及可能导致老年人认知能力下降的机制的理解。众所周知，大脑在功能上被组织成多个相互作用的网络。大量的文献表明，大脑连接体的空间和功能组织在晚年表现出与年龄相关的变化。然而，大多数神经影像学研究都集中在一个单一的认知状态，如任务或休息状态，忽视了跨状态的大脑连接体重构在认知老化中的作用。然而，越来越多的证据表明，跨认知状态的脑功能连接体的动态重构可以预测认知能力。因此，缺乏一个更综合的观点可能会限制我们识别导致成年后期认知能力下降的机制的能力。基于我们之前的研究和试点分析，我们表明，收集功能磁共振成像（fMRI）数据从休息状态和几个认知任务将改善大脑功能连接体的表征及其与成年后期认知的联系。在这种情况下，本申请的目的是将这些成像数据与认知和行为信息联合收割机结合，以识别脑网络的空间和功能重构的作用，从而预测与衰老相关的认知衰退和异质性。为了实现这一目标，我们将使用130名19至88岁的健康参与者的静息状态和基于任务的功能性磁共振成像（fMRI）。我们的第一个目标（目标1）是量化个体间的差异，在全球和特定领域的认知及其与年龄的关联。我们将使用一种新的度量标准，称为基于人的相似性指数，它使我们能够识别其认知特征与其他参与者显着不同的参与者，从而能够详细检查可能导致老年人认知异质性的特征。然后，我们将确定基于年龄的变化的空间（目标2）和功能（目标3）的认知状态的大脑网络的重新配置。我们将证明，相对于年轻人，不同条件下网络重构程度的降低与老年人的认知能力下降程度较高有关。该项目的成功完成将提供健康老龄化认知状态下大脑重构的综合观点，并将量化其与老年健康个体认知下降的相关性。通过绘制成年后期的大脑功能连接体，这项工作有可能阐明大脑网络功能障碍如何在健康和神经退行性疾病条件下导致认知老化。

Older individuals represent 15% of the United States population, and this is expected to exceed 20% by 2050. It is therefore critical that we improve our understanding of the physiology of healthy brain aging and the mechanisms that may lead to cognitive decline in older people. It is well accepted that the brain is functionally organized into multiple interacting networks. Extensive literature has demonstrated that the spatial and functional organization of the brain connectome shows age-related alterations in later life. Yet, most neuroimaging studies have focused on one single cognitive state such as task or resting-state and ignored the role of brain connectome reconfiguration across states in cognitive aging. However, growing evidence suggests that the dynamic reconfiguration of the brain functional connectome across cognitive states can predict cognitive ability. Therefore, this lack of a more integrative view may limit our capacity to identify the mechanisms leading to cognitive decline in late adulthood. Based on our prior studies and pilot analyses, we show that collecting functional magnetic resonance imaging (fMRI) data from both resting-state and several cognitive tasks will improve the characterization of the brain functional connectome and its links to cognition in late adulthood. In this context, the aim of this application is to combine these imaging data with cognitive and behavioral information to identify the role of spatial and functional reconfigurations of the brain networks to predict cognitive decline and heterogeneity associated with aging. To achieve this, we will use both resting-state and task-based functional magnetic resonance imaging (fMRI) from 130 healthy participants between 19 and 88 years-old. Our first aim (Aim 1) is to quantify inter-individual variability in global and domain-specific cognition and its association with age. We will use a novel metric, called person-based similarity index, that allows us to identify participants whose cognitive profile significantly differs from that of other participants thus enabling a detailed examination of the characteristics that may drive cognitive heterogeneity in older age. We will then identify age-based changes in the spatial (Aim 2) and functional (Aim 3) reconfigurations of brain networks by cognitive states. We will demonstrate that a reduced degree of network reconfiguration across conditions is associated with higher cognitive decline in older individuals, relative to the younger individuals. The successful completion of this project will provide an integrative view of the brain reconfiguration across cognitive states in healthy aging and will quantify its association with cognitive decline in older healthy individuals. By mapping the brain functional connectome underlying late adulthood, this work has the potential to elucidate how dysfunction of the brain networks contributes to cognitive aging in healthy and neurodegenerative conditions.