Structural connectivity of the default mode network and cognition in Alzheimer׳s disease

Abstract Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performa...

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Published inPsychiatry research. Neuroimaging Vol. 223; no. 1; pp. 15 - 22
Main Authors Weiler, Marina, de Campos, Brunno Machado, Nogueira, Mateus Henrique, Pereira Damasceno, Benito, Cendes, Fernando, Balthazar, Marcio L.F
Format Journal Article
LanguageEnglish
Published Ireland Elsevier Ireland Ltd 30.07.2014
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Summary:Abstract Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performance. MRI data of 76 subjects (25 mild AD, 21 amnestic Mild Cognitive Impairment subjects and 30 controls) were acquired on a 3.0 T scanner. ExploreDTI software (fractional Anisotropy threshold=0.25 and the angular threshold=60°) calculated axial, radial, and mean diffusivities, fractional anisotropy and streamline count. AD patients showed lower fractional anisotropy ( P =0.01) and streamline count ( P =0.029), and higher radial diffusivity ( P =0.014) than controls in the cingulum. After correction for white matter atrophy, only fractional anisotropy and radial diffusivity remained significantly lower in AD compared to controls ( P =0.003 and P =0.05). In the parahippocampal bundle, AD patients had lower mean and radial diffusivities ( P =0.048 and P =0.013) compared to controls, from which only radial diffusivity survived for white matter adjustment ( P =0.05). Regression models revealed that cognitive performance is also accounted for by white matter microstructural values. Structural connectivity within the DMN is important to the execution of high-complexity tasks, probably due to its relevant role in the integration of the network.
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ISSN:0925-4927
1872-7506
DOI:10.1016/j.pscychresns.2014.04.008