APOE ε4 positivity predicts centrality of episodic memory nodes in patients with mild cognitive impairment: A
Matteo De Marco1, Laura M Wright2, Jose Manuel Valera Bermejo3
1Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom.
Abstract:
As network neuroscience can capture the systemic impact of APOE variability at a neuroimaging level, this study investigated the network-based cognitive endophenotypes of ε4-carriers and non-carriers across the continuum between normal ageing and Alzheimer's dementia (AD). We hypothesised that the impact of APOE-ε4 on cognitive functioning can be reliably captured by the measurement of graph-theory centrality. Cognitive networks were calculated in 8118 controls, 3482 MCI patients and 4573 AD patients, recruited in the National Alzheimer's Coordinating Center (NACC) database. Nodal centrality was selected as the neurofunctional readout of interest. ε4-carrier-vs.-non-carrier differences were tested in two independent NACC sub-cohorts assessed with either Version 1 or Version 2 of the Uniform Data Set neuropsychological battery. A significant APOE-dependent effect emerged from the analysis of the Logical-Memory nodes in MCI patients in both sub-cohorts. While non-carriers showed equal centrality in immediate and delayed recall, the latter was significantly less central among carriers (v1: bootstrapped confidence interval 0.107-0.667, p < 0.001; v2: bootstrapped confidence interval 0.018-0.432, p < 0.001). This indicates that, in carriers, delayed recall was, overall, significantly more weakly correlated with the other cognitive scores. These findings were replicated in the sub-groups of sole amnestic-MCI patients (n = 2971), were independent of differences in network communities, clinical severity or other demographic factors. No effects were found in the other two diagnostic groups. APOE-ε4 influences nodal properties of cognitive networks when patients are clinically classified as MCI. This highlights the importance of characterising the impact of risk factors on the wider cognitive network via network-neuroscience methodologies.
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