Structural network efficiency predicts cognitive decline in cerebral small vessel disease

• Published in: NeuroImage clinical 2020-01, Vol.27, p.102325-102325, Article 102325
• Main Authors: Boot, Esther M., MC van Leijsen, Esther, Bergkamp, Mayra I., Kessels, Roy P.C., Norris, David G., de Leeuw, Frank-Erik, Tuladhar, Anil M.
• Format: Article
• Language: English
• Subjects: Cognitive function; Diffusion tensor imaging; Graph theory; Network efficiency; Regular; Small vessel disease
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2020.102325

•Baseline network efficiency was the strongest predictor for cognitive function in SVD.•Baseline network efficiency predicted faster cognitive decline in SVD.•Network efficiency is the most sensitive marker for cognitive function in SVD.•Network efficiency plays a key role in the genesis of cognitive decline in SVD. 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 pre