Lifestyle differences modulate the effects of cognitive reserve on functional connectivity

Background Cognitive reserve (CR) refers to the individual differences in cognition relative to brain aging or neurodegenerative changes. Recently, various studies tried to quantify a marker that represents CR. Among others, the residual approach suggested being relatively reliable as it has been st...

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Published in:Alzheimer's & dementia 2020-12, Vol.16, p.n/a
Main Authors: Ersözlü, Ersin, Rauchmann, Boris, Peters, Oliver, Priller, Josef, Schneider, Anja, Wiltfang, Jens, Jessen, Frank, Düzel, Emrah, Bürger, Katharina, Teipel, Stefan J., Christoph, Laske, Spottke, Annika, Ramirez, Alfredo, Cardenas‐Blanco, Arturo, Wagner, Michael, Perneczky, Robert
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Language:English
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Summary:Background Cognitive reserve (CR) refers to the individual differences in cognition relative to brain aging or neurodegenerative changes. Recently, various studies tried to quantify a marker that represents CR. Among others, the residual approach suggested being relatively reliable as it has been studied not only with cross‐sectional but also with longitudinal observations. However, it is unknown if brain connectivity changes in the resting‐state are related to reserve factors e.g. lifestyle factors. Method In the present study we defined a CR Marker based on residuals calculated through a multimodal (multi‐linear) regression model adapted from a recent study using pathological changes of AD such as atrophy, demographic and genetic information as predictors. The data of 546 participants including healthy controls, participants with subjective cognitive decline, 62 patients with MCI, and 64 patients with AD from the German multicentric DELCODE cohort was assessed. To examine neural substrates of defined CR marker, we constructed a weighted undirected partial correlation matrix in the GraphVar toolbox and analyzed graph theoretical metrics in global topography with various network thresholds. Permutation‐based FDR‐correction has been used in statistical analyses with graph measures. Then we examined the possible associations between CR and lifestyle factors specifically the Lifetime Experiences Questionnaire(LEQ). Result CR was positively correlated (r=0.247, p=0.012) with higher scores in LEQ which includes leisure, educational and physical activities across the lifespan. We demonstrated that LEQ interacts with CR in functional connectivity changes, we used a multivariate regression model, in which LEQ was binarized using median values of total normalized scores. At the global level, interaction between CR and binarized LEQ score has been found with clustering coefficient (p=0.02), transitivity (p=0.017), but not with global efficiency(p=0.45). Moreover, component of LEQ scores corresponding to young adulthood, mid‐life, and late‐life didn’t interact with CR. However, LEQ of late‐life showed interaction at marginal level(uncorrected p=0.03). Conclusion Overall, CR and lifestyle differences appear to be reflected by changes in brain connectivity with increased global information processing capacity in participants exhibit higher CR. These findings foster understanding of early AD and will be useful to help identify individuals with vulnerability or resistanc
ISSN:1552-5260
1552-5279
DOI:10.1002/alz.042947