5 research outputs found

    Aging brain from a network science perspective: Something to be positive about?

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    To better understand age differences in brain function and behavior, the current study applied network science to model functional interactions between brain regions. We observed a shift in network topology whereby for older adults subcortical and cerebellar structures overlapping with the Salience network had more connectivity to the rest of the brain, coupled with fragmentation of large-scale cortical networks such as the Default and Fronto-Parietal networks. Additionally, greater integration of the dorsal medial thalamus and red nucleus in the Salience network was associated with greater satisfaction with life for older adults, which is consistent with theoretical predictions of age-related increases in emotion regulation that are thought to help maintain well-being and life satisfaction in late adulthood. In regard to cognitive abilities, greater ventral medial prefrontal cortex coherence with its topological neighbors in the Default Network was associated with faster processing speed. Results suggest that large-scale organizing properties of the brain differ with normal aging, and this perspective may offer novel insight into understanding age-related differences in cognitive function and well-being. © 2013 Voss et al

    A New Measure of Centrality for Brain Networks

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    Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network

    Arterial spin labeling perfusion imaging demonstrates cerebral hyperperfusion in anti-NMDAR encephalitis

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    Anti-N-methyl-d-aspartate receptor encephalitis is an increasingly recognized autoimmune disorder that results in substantial morbidity, prolonged hospital stays, and even death. The diagnosis is often delayed or unrecognized entirely as a result of absent or only subtle initial magnetic resonance imaging findings and a nonspecific clinical syndrome. The discovery of early imaging findings in this disease may help clinicians to more aggressively treat this autoimmune encephalitis and to potentially lessen morbidity and mortality. We report a novel case of anti-N-methyl-d-aspartate receptor encephalitis characterized by early evidence of increased cerebral perfusion on arterial spin labeling perfusion imaging, a finding that preceded laboratory diagnosis and conventional magnetic resonance imaging abnormalities. Further investigation is needed to firmly establish the pathologic basis of this finding
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