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

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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.