Network structure-function coupling and neurocognition in cerebral small vessel disease

Background: Cerebral small vessel disease is a leading cause of cognitive decline and vascular dementia. Small vessel disease pathology changes structural brain networks, but its impact on functional networks remains poorly understood. Structural and functional networks are closely coupled in healthy individuals, and decoupling is associated with clinical symptoms in other neurological conditions. We tested the hypothesis that structural–functional network coupling is related to neurocognitive outcomes in 262 small vessel disease patients. Methods: Participants underwent multimodal magnetic resonance imaging and cognitive assessment in 2011 and 2015. Structural connectivity networks were reconstructed using probabilistic diffusion tractography, while functional connectivity networks were estimated from resting-state functional magnetic resonance imaging. Structural and functional networks were then correlated to calculate a measure of structural–functional network coupling for each participant. Results: Lower whole-brain coupling was associated with reduced processing speed and greater apathy both cross-sectionally and longitudinally. In addition, coupling within the cognitive control network was associated with all cognitive outcomes, suggesting that neurocognitive outcomes in small vessel disease may be related to the functioning of this intrinsic connectivity network. Conclusions: Our work demonstrates the influence of structural–functional connectivity network decoupling in small vessel disease symptomatology. Cognitive control network function may be investigated in future studies

Baseline Cerebral Small Vessel Disease Is Not Associated with Gait Decline After Five Years

Background Cerebral small vessel disease (SVD) is cross-sectionally associated with gait disturbances, however, the relation between baseline SVD and gait decline over time is uncertain. Furthermore, diffusion tensor imaging (DTI) studies on gait decline are currently lacking. Objective To investigate the association between baseline imaging SVD markers and gait decline. Methods In 2006, 310 participants from the RUN DMC cohort, a prospective cohort with older adults aged 50–85 years with SVD, were included. Gait variables were assessed using a computerized walkway during baseline and follow-up. Linear and logistic regression analyses were used to investigate the relation between imaging measures and gait decline and incident gait impairment (speed ≤ 1.0 m/s). Tract-based spatial statistics (TBSS) was used to identify possible differences in DTI measures of white matter tracts between participants with and without incident gait impairment. Results Mean age was 63.3 years (SD: 8.4) and mean follow-up duration 5.4 years (SD: 0.2). No significant associations between imaging measures and gait decline were found. TBSS analysis revealed no significant differences in DTI measures between participants with and without incident gait impairment after additional adjustment for SVD. In sub-analyses, a high total WMH volume (OR: 2.8 for highest quartile, 95% CI: 1.1–7.1) and high infratentorial WMH volume (OR: 1.8 per SD increase, 95% CI: 1.1–2.9) were associated with an increased 5-year risk of gait impairment, although this was not significant after correction for multiple testing. Conclusion Baseline imaging SVD markers were not associated with gait decline or incident gait impairment after 5 years. Future studies should investigate if SVD progression is related to gait deterioration

Structural network changes in cerebral small vessel disease

Objectives: To investigate whether longitudinal structural network efficiency is associated with cognitive decline and whether baseline network efficiency predicts mortality in cerebral small vessel disease (SVD). Methods: A prospective, single-centre cohort consisting of 277 non-demented individuals with SVD was conducted. In 2011 and 2015, all participants were scanned with MRI and underwent neuropsychological assessment. We computed network properties using graph theory from probabilistic tractography and calculated changes in psychomotor speed and overall cognitive index. Multiple linear regressions were performed, while adjusting for potential confounders. We divided the group into mild-to-moderate white matter hyperintensities (WMH) and severe WMH group based on median split on WMH volume. Results: The decline in global efficiency was significantly associated with a decline in psychomotor speed in the group with severe WMH (β=0.18, p=0.03) and a trend with change in cognitive index (β=0.14, p=0.068), which diminished after adjusting for imaging markers for SVD. Baseline global efficiency was associated with all-cause mortality (HR per decrease of 1 SD 0.43, 95% CI 0.23 to 0.80, p=0.008, C-statistic 0.76). Conclusion: Disruption of the network efficiency, a metric assessing the efficiency of network information transfer, plays an important role in explaining cognitive decline in SVD, which was however not independent of imaging markers of SVD. Furthermore, baseline network efficiency predicts risk of mortality in SVD that may reflect the global health status of the brain in SVD. This emphasises the importance of structural network analysis in the context of SVD research and the use of network measures as surrogate markers in research setting

Structural network changes in cerebral small vessel disease.

OBJECTIVES: To investigate whether longitudinal structural network efficiency is associated with cognitive decline and whether baseline network efficiency predicts mortality in cerebral small vessel disease (SVD). METHODS: A prospective, single-centre cohort consisting of 277 non-demented individuals with SVD was conducted. In 2011 and 2015, all participants were scanned with MRI and underwent neuropsychological assessment. We computed network properties using graph theory from probabilistic tractography and calculated changes in psychomotor speed and overall cognitive index. Multiple linear regressions were performed, while adjusting for potential confounders. We divided the group into mild-to-moderate white matter hyperintensities (WMH) and severe WMH group based on median split on WMH volume. RESULTS: The decline in global efficiency was significantly associated with a decline in psychomotor speed in the group with severe WMH (β=0.18, p=0.03) and a trend with change in cognitive index (β=0.14, p=0.068), which diminished after adjusting for imaging markers for SVD. Baseline global efficiency was associated with all-cause mortality (HR per decrease of 1 SD 0.43, 95% CI 0.23 to 0.80, p=0.008, C-statistic 0.76). CONCLUSION: Disruption of the network efficiency, a metric assessing the efficiency of network information transfer, plays an important role in explaining cognitive decline in SVD, which was however not independent of imaging markers of SVD. Furthermore, baseline network efficiency predicts risk of mortality in SVD that may reflect the global health status of the brain in SVD. This emphasises the importance of structural network analysis in the context of SVD research and the use of network measures as surrogate markers in research setting

Serum Neurofilament Light Chain Is Associated with Incident Lacunes in Pogressive Cerebral Small Vessel Disease

Background and purpose: Serum neurofilament light (NfL)-chain is a circulating marker for neuroaxonal injury and is also associated with severity of cerebral small vessel disease (SVD) cross-sectionally. Here we explored the association of serum-NfL with imaging and cognitive measures in SVD longitudinally. Methods From 503 subjects with SVD, baseline and follow-up magnetic resonance imaging (MRI) was available for 264 participants (follow-up 8.7 +/- 0.2 years). Baseline serum-NfL was measured by an ultrasensitive single-molecule-assay. SVD-MRI-markers including white matter hyperintensity (WMH)-volume, mean diffusivity (MD), lacunes, and microbleeds were assessed at both timepoints. Cognitive testing was performed in 336 participants, including SVD-related domains as well as global cognition and memory. Associations with NfL were assessed using linear regression analyses and analysis of covariance (ANCOVA). Results Serum-NfL was associated with baseline WMH-volume, MD-values and presence of lacunes and microbleeds. SVD-related MRI- and cognitive measures showed progression during follow-up. NfL-levels were associated with future MRI-markers of SVD, including WMH, MD and lacunes. For the latter, this association was independent of baseline lacunes. Furthermore, NfL was associated with incident lacunes during follow-up (P=0.040). NfL-levels were associated with future SVD-related cognitive impairment (processing speed: beta=-0.159;95910 confidence interval [CI]. -0.242 to -0.068;P=0.001;executive function beta=-0.095;95% CI, -0.170 to -0.007;P=0.033), adjusted for age, sex, education, and depression. Dementia-risk increased with higher NfL-levels (hazard ratio, 5.0;95% CI, 2.6 to 9.4;P<0.001), however not after adjusting for age. Conclusions: Longitudinally, serum-NfL is associated with markers of SVD, especially with incident lacunes, and future cognitive impairment affecting various domains. NfL may potentially serve as an additional marker for disease monitoring and outcome in SVD, potentially capturing both vascular and neurodegenerative processes in the elderly

Simple MRI score aids prediction of dementia in cerebral small vessel disease.

OBJECTIVE: To determine whether a simple small vessel disease (SVD) score, which uses information available on rapid visual assessment of clinical MRI scans, predicts risk of cognitive decline and dementia, above that provided by simple clinical measures. METHODS: Three prospective longitudinal cohort studies (SCANS [St George's Cognition and Neuroimaging in Stroke], RUN DMC [Radboud University Nijmegen Diffusion Imaging and Magnetic Resonance Imaging Cohort], and the ASPS [Austrian Stroke Prevention Study]), which covered a range of SVD severity from mild and asymptomatic to severe and symptomatic, were included. In all studies, MRI was performed at baseline, cognitive tests repeated during follow-up, and progression to dementia recorded prospectively. Outcome measures were cognitive decline and onset of dementia during follow-up. We determined whether the SVD score predicted risk of cognitive decline and future dementia. We also determined whether using the score to select a group of patients with more severe disease would reduce sample sizes for clinical intervention trials. RESULTS: In a pooled analysis of all 3 cohorts, the score improved prediction of dementia (area under the curve [AUC], 0.85; 95% confidence interval [CI], 0.81-0.89) compared with that from clinical risk factors alone (AUC, 0.76; 95% CI, 0.71-0.81). Predictive performance was higher in patients with more severe SVD. Power calculations showed selecting patients with a higher score reduced sample sizes required for hypothetical clinical trials by 40%-66% depending on the outcome measure used. CONCLUSIONS: A simple SVD score, easily obtainable from clinical MRI scans and therefore applicable in routine clinical practice, aided prediction of future dementia risk.Alzheimers research UK, MR

Simple MRI score aids prediction of dementia in cerebral small vessel disease

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