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      Elevated hippocampal resting-state connectivity underlies deficient neurocognitive function in aging

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          The brain is not idle during rest. Functional MRI (fMRI) studies have identified several resting-state networks, including the default mode network (DMN), which contains a set of cortical regions that interact with a hippocampus (HC) subsystem. Age-related alterations in the functional architecture of the DMN and HC may influence memory functions and possibly constitute a sensitive biomarker of forthcoming memory deficits. However, the exact form of DMN-HC alterations in aging and concomitant memory deficits is largely unknown. Here, using both task and resting data from 339 participants (25-80 y old), we have demonstrated age-related decrements in resting-state functional connectivity across most parts of the DMN, except for the HC network for which age-related elevation of connectivity between left and right HC was found along with attenuated HC-cortical connectivity. Elevated HC connectivity at rest, which was partly accounted for by age-related decline in white matter integrity of the fornix, was associated with lower cross-sectional episodic memory performance and declining longitudinal memory performance over 20 y. Additionally, elevated HC connectivity at rest was associated with reduced HC neural recruitment and HC-cortical connectivity during active memory encoding, which suggests that strong HC connectivity restricts the degree to which the HC interacts with other brain regions during active memory processing revealed by task fMRI. Collectively, our findings suggest a model in which age-related disruption in cortico-hippocampal functional connectivity leads to a more functionally isolated HC at rest, which translates into aberrant hippocampal decoupling and deficits during mnemonic processing.

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          Most cited references20

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          Age-related changes in modular organization of human brain functional networks.

          Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased.
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            Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.

            Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5-16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test-retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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              Resting-state functional connectivity in normal brain aging.

              The world is aging and, as the elderly population increases, age-related cognitive decline emerges as a major concern. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), allow the investigation of the neural bases of age-related cognitive changes in vivo. Typically, fMRI studies map brain activity while subjects perform cognitive tasks, but such paradigms are often difficult to implement on a wider basis. Resting-state fMRI (rs-fMRI) has emerged as an important alternative modality of fMRI data acquisition, during which no specific task is required. Due to such simplicity and the reliability of rs-fMRI data, this modality presents increased feasibility and potential for clinical application in the future. With rs-fMRI, fluctuations in regional brain activity can be detected across separate brain regions and the patterns of intercorrelation between the functioning of these regions are measured, affording quantitative indices of resting-state functional connectivity (RSFC). This review article summarizes the results of recent rs-fMRI studies that have documented a variety of aging-related RSFC changes in the human brain, discusses the neurophysiological hypotheses proposed to interpret such findings, and provides an overview of the future, highly promising perspectives in this field. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                December 09 2014
                December 09 2014
                December 09 2014
                November 24 2014
                : 111
                : 49
                : 17654-17659
                Article
                10.1073/pnas.1410233111
                4267388
                25422457
                123331ab-0e0a-44a5-a745-7c516d0ad855
                © 2014
                History

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