Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis

Abstract

BackgroundAlzheimer’s Disease (AD) is the most common form of dementia with genetic and environmental risk contributing to its development. Graph theoretical analyses of brain networks constructed from structural and functional MRI measurements have identified connectivity changes in AD and individuals with mild cognitive impairment (MCI). However, brain connectivity in asymptomatic individuals at risk of AD remains poorly understood.MethodsWe acquired diffusion-weighted magnetic resonance imaging (dMRI) data from 165 asymptomatic individuals (38-71 years) from the Cardiff Ageing and Risk of Dementia Study (CARDS). We calculated white matter tracts and constructed whole-brain, default-mode-network and visual structural brain networks that incorporate multiple structural metrics as edge weights. We then calculated the relationship of three AD risk factors, namely Apolipoprotein-E ɛ4 genotype (APOE4), family history (FH) of dementia, and central obesity, on graph theoretical measures and hubs.ResultsWe observed no risk-related differences in clustering coefficients, characteristic path lengths, eccentricity, diameter and radius across the whole-brain, default-mode-network or visual system. However, a hub in the right paracentral lobule was present in all high-risk groups (FH, APOE4, obese) but absent in low-risk groups (no FH, APOE4-ve, healthy weight).DiscussionWe identified no risk-related effects on graph theoretical metrics in the structural brain networks of cognitively healthy individuals. However, high-risk was associated with a hub in the right paracentral lobule, an area with motor and sensory functions related to the lower limb. If this phenotype is shown to predict symptom development in longitudinal studies, it could be used as an early biomarker of AD.Impact StatementAlzheimer’s Disease is a common form of dementia which to date has no cure. Identifying early biomarkers will aid the discovery and development of treatments that may slow AD progression in the future. In this paper we report that asymptomatic individuals at heightened risk of dementia due to their family history, Apolipoprotein-E ɛ4 genotype and body adiposity have a hub in the right paracentral lobule which is absent in low-risk groups. If this phenotype were to predict the development of symptoms in a longitudinal study of the same cohort, it could provide an early biomarker of disease progression.

The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks

“…We recently explored the repeatability of structural brain graphs, their edge weights and graph‐theoretical metrics, for 21 different edge‐weighting schemes (Messaritaki, Dimitriadis, & Jones, 2019a ). We demonstrated that integrating several metrics as edge weights is very good at capturing differences between populations, and is interesting from the perspective of developing biomarkers (Clarke, Messaritaki, Dimitriadis, & Metzler‐Baddeley, 2021 ; Dimitriadis et al, 2017 ).…”

Predicting MEG resting-state functional connectivity from microstructural information

“…It is noteworthy that the correlations were highest for the delta band, regardless of the pair of algorithm/metric used. We note that the composite structural connectomes that were derived with the algorithm of Dimitriadis et al (2017) , even though very good at capturing differences between populations ( Clarke, Messaritaki, Dimitriadis, & Metzler-Baddeley, 2020 ; Dimitriadis et al, 2017 ), did not provide any improvement in the relationship between structure and function compared to the single-metric structural connectomes, possibly because the true relationship is more complex than that implied by the linear data-driven algorithm. For that reason we do not discuss them any further.…”

The impact of genetic risk for Alzheimer’s disease on the structural brain networks of young adults

“…APOE4 status also affects the clustering coefficient and the local efficiency of structural brain networks (Ma et al, 2017). Middle-aged adults with genetic, family and lifestyle risks of developing AD have a hub in their structural connectome that is not present in the structural connectome of people with no such risks of developing AD (Clarke et al, 2020). Significant functional connectivity differences in the brain networks implicated in cognition were seen in middle-aged individuals with a genetic risk for AD (Goveas et al, 2013).…”

“…Specifically, Ma et al (2017) observed that the values for the APOE4 carriers were higher than those of the non-carriers in a normal-cognition group, while the opposite pattern was observed in a group of participants suffering from Mild Cognitive Impairment (MCI). Middle-aged adults with genetic, family and lifestyle risks of developing AD have a hub in their structural connectome that is not present in the structural connectome of people with no such risks of developing AD (Clarke et al, 2020). Significant functional connectivity differences in the brain networks implicated in cognition were seen in middle-aged individuals with a genetic risk for AD (Goveas et al, 2013).…”