Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers

Abstract

The apolipoprotein E (APOE) ε4 allele is the best characterized genetic risk factor for Alzheimer's disease to date. Older APOE ε4 carriers (aged 60 + years) are known to have disrupted structural and functional connectivity, but less is known about APOE-associated network integrity in middle age. The goal of this study was to characterize APOE-related differences in network topology in middle age, as disentangling the early effects of healthy versus pathological aging may aid early detection of Alzheimer's disease and inform treatments. We performed resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) in healthy, cognitively normal, middle-aged adults (age 40-60; N = 76, 38 APOE ε4 carriers). Graph theoretical analysis was used to calculate local and global efficiency of 1) a whole brain rs-fMRI network; 2) a whole brain DTI network; and 3) the resting state structural connectome (rsSC), an integrated functional-structural network derived using functional-by-structural hierarchical (FSH) mapping. Our results indicated no APOE ε4-associated differences in network topology of the rs-fMRI or DTI networks alone. However, ε4 carriers had significantly lower global and local efficiency of the integrated rsSC compared to non-carriers. Furthermore, ε4 carriers were less resilient to targeted node failure of the rsSC, which mimics the neuropathological process of Alzheimer's disease. Collectively, these findings suggest that integrating multiple neuroimaging modalities and employing graph theoretical analysis may reveal network-level vulnerabilities that may serve as biomarkers of age-related cognitive decline in middle age, decades before the onset of overt cognitive impairment.

Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer’s Disease

“…Our findings parallel other investigations in the study of connectivity alterations associated with APOE status in human carriers and mouse models (Heise et al, 2014;Wiesmann et al, 2016;Luo et al, 2017;Korthauer et al, 2018). These studies support the presence of alterations in both functional and structural connectomes, and report separately such biomarkers.…”

Impact of Apolipoprotein E gene polymorphism during normal and pathological conditions of the brain across the lifespan

“…However, initial studies are showing interesting findings when introducing the APOE polymorphism factor in the analyses of possible connectomics changes in APOE 4 carrier individuals. Some recent studies [300,301] show that the default-mode network in APOE 4 subjects differs from non- APOE 4 in a way that APOE 4 seems to establish the functional communications across different brain regions (connectomics), which could predispose to some cognitive disadvantage across the entire lifespan. Moreover, some investigations pointed out that there is indeed an association between cognition measurements and personality traits [302,303].…”

Default Mode Network Analysis of APOE Genotype in Cognitively Unimpaired Subjects Based on Persistent Homology

“…There are more than 50 million patients worldwide in 2018, and it is expected to reach a staggering 152 million by 2050 ( Patterson, 2018 ). It is crucial to develop the AD-related biomarkers early in the aging process before the onset of overt cognitive impairment and irreversible brain damage ( Korthauer et al, 2018 ). One hypothesis ( Reiman et al, 2009 ; Lambert et al, 2013 ; Yu et al, 2019 ) for the pathogenesis ( Karch and Goate, 2015 ) of AD indicate the apolipoprotein E (APOE) e4 allele ( Lane-Donovan and Herz, 2017 ) involves the accumulation of Amyloid-β ( Caselli et al, 2010 ), leading to increasing neuronal atrophy and synapse loss.…”

“…Some reported decreased functional connectivity ( Yan et al, 2015 ) in APOE e4 allele carriers (APOE4+) compared with non-carries (APOE4-), the others found some increased functional connectivity ( Song et al, 2015 ; Zhu et al, 2018 ), while others didn’t found any differences ( Chiesa et al, 2019 ). At the global whole-brain level, some neurobiologically meaningful graph-theoretic properties have become important indicators for measuring brain functional networks, through which we can understand the altered network architecture in those carrying risk genotype, including a loss of small-world network ( Korthauer et al, 2018 ), a redistribution of hubs ( Wink et al, 2018 ), and a disrupted modular organization ( Li et al, 2019 ). However, there are currently few network measures based on graph theory have been studied in cognitively unimpaired elderly ( Seo et al, 2013 ; Luo et al, 2017 ; Pietzuch et al, 2019 ) and some of their results were reported as inconsistent ( Seo et al, 2013 ; Qiu et al, 2016 ; Luo et al, 2017 ).…”