Abstract
Cerebral small vessel disease (SVD) is a common disease in older adults and a major contributor to vascular cognitive impairment and dementia. White matter network damage is a potentially important mechanism by which SVD causes cognitive impairment. Earlier studies showed that a higher degree of white matter network damage, indicated by lower global efficiency (a graph-theory measure assessing efficiency of network information transfer), was associated with lower scores on cognitive performance independent of MRI markers for SVD. However, it is unknown whether this global efficiency index is the strongest predictor for cognitive impairment, as there is a wide range of network measures. Here, we investigate which network measure is the most informative in explaining baseline cognitive performance and decline over a period of 8.7 years in SVD. We used data from the Radboud University Nijmegen Diffusion tensor and MRI Cohort (RUN DMC), which included 436 participants without dementia (65.2 ± 8.8 years) but with evidence of SVD on neuroimaging. Binarized and weighted structural brain networks were reconstructed using diffusion tensor imaging and deterministic streamlining. Using graph-theory, we calculated 21 global network measures and performed linear regression analyses, elastic net analysis and linear mixed effect models to compare these measures. All analyses were adjusted for potential confounders (age, sex, educational level, depressive symptoms and conventional SVD MRI-markers (e.g. white matter hyperintensities (WMH), lacunes of presumed vascular origin and microbleeds). The elastic net analyses showed that, at baseline, global efficiency had the strongest association with cognitive index (CI), while characteristic path length showed the strongest association with psychomotor speed (PMS) and memory. Binary local efficiency showed the strongest association with attention & executive function (A&EF). In addition, linear mixed-effect models demonstrated that baseline global efficiency predicts decline in CI (χ2(1) = 8.18, p = 0.004),PMS (χ2(1) = 7.75, p = 0.005), memory (χ2(1) = 27.28, p = 0.000) over time and that binary local efficiency predicts decline in A&EF (χ2(1) = 8.66, p = 0.003) over time. Our results suggest that among all network measures, network efficiency measures, i.e. global efficiency and local efficiency, are the strongest predictors for cognitive functions at cross-sectional level and also predict faster cognitive decline in SVD, which is in line with earlier findings. These findings suggests that in our study sample network efficiency measures are the most suitable surrogate markers for cognitive performance in patients with cerebral SVD among all network measures and MRI markers, and play a key role in the genesis of cognitive decline in SVD.
Highlights
Cerebral small vessel disease (SVD) is prevalent in older adults and can be visualized on neuroimaging as white matter hyperintensities (WMH), lacunes of presumed vascular origin and microbleeds (Wardlaw et al, 2013)
We focused on global efficiency and binary local efficiency, since these network measures seem biologically most informative for cognitive impairment in SVD, whereas characteristic path length is mathematically more complex to use in disconnected nodes
We investigated which structural network measures derived from DTI showed the highest validity in predicting cognitive performance and is associated with cognitive decline over 9 years in SVD
Summary
Cerebral small vessel disease (SVD) is prevalent in older adults and can be visualized on neuroimaging as white matter hyperintensities (WMH), lacunes of presumed vascular origin and microbleeds (Wardlaw et al, 2013). SVD is the most impairment cause of vascular cognitive impairment and vascular dementia (Ter Telgte et al, 2018). While some patients show a benign disease course, other deteriorates rapidly (Ter Telgte et al, 2018). A potential important mechanism by which SVD causes cognitive impairment is the disruption of the white matter network in SVD, as evidenced by recent studies (Lawrence et al, 2014, 2018; Kim et al, 2015a; Tuladhar et al, 2016b)
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