Structural network efficiency predicts cognitive decline in cerebral small vessel disease
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 whi...
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Format: | Article |
Language: | English |
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Elsevier
2020-01-01
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Series: | NeuroImage: Clinical |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158220301625 |
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doaj-03d28029c8df438992f126d16b23c7ed |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Esther M. Boot Esther MC van Leijsen Mayra I. Bergkamp Roy P.C. Kessels David G. Norris Frank-Erik de Leeuw Anil M. Tuladhar |
spellingShingle |
Esther M. Boot Esther MC van Leijsen Mayra I. Bergkamp Roy P.C. Kessels David G. Norris Frank-Erik de Leeuw Anil M. Tuladhar Structural network efficiency predicts cognitive decline in cerebral small vessel disease NeuroImage: Clinical Small vessel disease Diffusion tensor imaging Graph theory Network efficiency Cognitive function |
author_facet |
Esther M. Boot Esther MC van Leijsen Mayra I. Bergkamp Roy P.C. Kessels David G. Norris Frank-Erik de Leeuw Anil M. Tuladhar |
author_sort |
Esther M. Boot |
title |
Structural network efficiency predicts cognitive decline in cerebral small vessel disease |
title_short |
Structural network efficiency predicts cognitive decline in cerebral small vessel disease |
title_full |
Structural network efficiency predicts cognitive decline in cerebral small vessel disease |
title_fullStr |
Structural network efficiency predicts cognitive decline in cerebral small vessel disease |
title_full_unstemmed |
Structural network efficiency predicts cognitive decline in cerebral small vessel disease |
title_sort |
structural network efficiency predicts cognitive decline in cerebral small vessel disease |
publisher |
Elsevier |
series |
NeuroImage: Clinical |
issn |
2213-1582 |
publishDate |
2020-01-01 |
description |
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. |
topic |
Small vessel disease Diffusion tensor imaging Graph theory Network efficiency Cognitive function |
url |
http://www.sciencedirect.com/science/article/pii/S2213158220301625 |
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doaj-03d28029c8df438992f126d16b23c7ed2020-11-25T03:34:43ZengElsevierNeuroImage: Clinical2213-15822020-01-0127102325Structural network efficiency predicts cognitive decline in cerebral small vessel diseaseEsther M. Boot0Esther MC van Leijsen1Mayra I. Bergkamp2Roy P.C. Kessels3David G. Norris4Frank-Erik de Leeuw5Anil M. Tuladhar6Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the NetherlandsRadboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the NetherlandsRadboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the NetherlandsRadboud University Medical Center, Department of Medical Psychology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany; Faculty of Science and Technology, Magnetic Detection and Imaging, University Twente, Enschede, the NetherlandsRadboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the NetherlandsRadboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the Netherlands; Corresponding author at: Department of Neurology, Radboud University Medical Center, Reinier Postlaan 4, PO-box 9101, 6500 HB Nijmegen, the Netherlands.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.http://www.sciencedirect.com/science/article/pii/S2213158220301625Small vessel diseaseDiffusion tensor imagingGraph theoryNetwork efficiencyCognitive function |