scholarly journals Diffusion tensor imaging and MR spectroscopy in hypertension and presumed cerebral small vessel disease

2008 ◽  
Vol 59 (3) ◽  
pp. 528-534 ◽  
Author(s):  
Arani Nitkunan ◽  
Rebecca A. Charlton ◽  
Dominick J.O. McIntyre ◽  
Thomas R. Barrick ◽  
Franklyn A. Howe ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Mr Spectroscopy ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease

scholarly journals White Matter Microstructural Damage on Diffusion Tensor Imaging in Cerebral Small Vessel Disease

Stroke ◽  
2016 ◽  
Vol 47 (6) ◽  
pp. 1679-1684 ◽  
Author(s):  
Marco Pasi ◽  
Inge W.M. van Uden ◽  
Anil M. Tuladhar ◽  
Frank-Erik de Leeuw ◽  
Leonardo Pantoni
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease ◽  
Microstructural Damage

scholarly journals Application of Diffusion Tensor Imaging Parameters to Detect Change in Longitudinal Studies in Cerebral Small Vessel Disease

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147836 ◽  
Author(s):  
Eva Anna Zeestraten ◽  
Philip Benjamin ◽  
Christian Lambert ◽  
Andrew John Lawrence ◽  
Owen Alan Williams ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Longitudinal Studies ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease ◽  
Imaging Parameters

scholarly journals Diffusion tensor imaging and cognition in cerebral small vessel disease

2012 ◽  
Vol 1822 (3) ◽  
pp. 401-407 ◽  
Author(s):  
Anouk G.W. van Norden ◽  
Karlijn F. de Laat ◽  
Ewoud J. van Dijk ◽  
Inge W.M. van Uden ◽  
Lucas J.B. van Oudheusden ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease

Diffusion tensor imaging and mild parkinsonian signs in cerebral small vessel disease

2012 ◽  
Vol 33 (9) ◽  
pp. 2106-2112 ◽  
Author(s):  
Karlijn F. de Laat ◽  
Anouk G.W. van Norden ◽  
Lucas J.B. van Oudheusden ◽  
Inge W.M. van Uden ◽  
David G. Norris ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease

scholarly journals Diffusion Tensor Imaging and Gait in Elderly Persons With Cerebral Small Vessel Disease

Stroke ◽  
2011 ◽  
Vol 42 (2) ◽  
pp. 373-379 ◽  
Author(s):  
Karlijn F. de Laat ◽  
Anouk G.W. van Norden ◽  
Rob A.R. Gons ◽  
Lucas J.B. van Oudheusden ◽  
Inge W.M. van Uden ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Elderly Persons ◽  
Vessel Disease

scholarly journals Apathy, but not depression, predicts all-cause dementia in cerebral small vessel disease

2020 ◽  
Vol 91 (9) ◽  
pp. 953-959
Author(s):  
Jonathan Tay ◽  
Robin G Morris ◽  
Anil M Tuladhar ◽  
Masud Husain ◽  
Frank-Erik de Leeuw ◽  
...  
Keyword(s):  
Predictive Models ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Model Fit ◽  
Small Vessel ◽  
Prodromal Symptom ◽  
Vessel Disease ◽  
Prospective Cohort Studies ◽  
Improved Model

ObjectiveTo determine whether apathy or depression predicts all-cause dementia in small vessel disease (SVD) patients.MethodsAnalyses used two prospective cohort studies of SVD: St. George’s Cognition and Neuroimaging in Stroke (SCANS; n=121) and Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC; n=352). Multivariate Cox regressions were used to predict dementia using baseline apathy and depression scores in both datasets. Change in apathy and depression was used to predict dementia in a subset of 104 participants with longitudinal data from SCANS. All models were controlled for age, education and cognitive function.ResultsBaseline apathy scores predicted dementia in SCANS (HR 1.49, 95% CI 1.05 to 2.11, p=0.024) and RUN DMC (HR 1.05, 95% CI 1.01 to 1.09, p=0.007). Increasing apathy was associated with dementia in SCANS (HR 1.53, 95% CI 1.08 to 2.17, p=0.017). In contrast, baseline depression and change in depression did not predict dementia in either dataset. Including apathy in predictive models of dementia improved model fit.ConclusionsApathy, but not depression, may be a prodromal symptom of dementia in SVD, and may be useful in identifying at-risk individuals.

scholarly journals Microstructural Predictors of Cognitive Impairment in Cerebral Small Vessel Disease and the Conditions of Their Formation

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 720
Author(s):  
Larisa A. Dobrynina ◽  
Zukhra Sh. Gadzhieva ◽  
Kamila V. Shamtieva ◽  
Elena I. Kremneva ◽  
Bulat M. Akhmetzyanov ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cognitive Impairment ◽  
White Matter ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Venous Blood Flow ◽  
Csf Flow ◽  
Vessel Disease

Introduction: Cerebral small vessel disease (CSVD) is the leading cause of vascular and mixed degenerative cognitive impairment (CI). The variability in the rate of progression of CSVD justifies the search for sensitive predictors of CI. Materials: A total of 74 patients (48 women, average age 60.6 ± 6.9 years) with CSVD and CI of varying severity were examined using 3T MRI. The results of diffusion tensor imaging with a region of interest (ROI) analysis were used to construct a predictive model of CI using binary logistic regression, while phase-contrast magnetic resonance imaging and voxel-based morphometry were used to clarify the conditions for the formation of CI predictors. Results: According to the constructed model, the predictors of CI are axial diffusivity (AD) of the posterior frontal periventricular normal-appearing white matter (pvNAWM), right middle cingulum bundle (CB), and mid-posterior corpus callosum (CC). These predictors showed a significant correlation with the volume of white matter hyperintensity; arterial and venous blood flow, pulsatility index, and aqueduct cerebrospinal fluid (CSF) flow; and surface area of the aqueduct, volume of the lateral ventricles and CSF, and gray matter volume. Conclusion: Disturbances in the AD of pvNAWM, CB, and CC, associated with axonal damage, are a predominant factor in the development of CI in CSVD. The relationship between AD predictors and both blood flow and CSF flow indicates a disturbance in their relationship, while their location near the floor of the lateral ventricle and their link with indicators of internal atrophy, CSF volume, and aqueduct CSF flow suggest the importance of transependymal CSF transudation when these regions are damaged.

scholarly journals Predicting Dementia in Cerebral Small Vessel Disease Using an Automatic Diffusion Tensor Image Segmentation Technique

Stroke ◽  
2019 ◽  
Vol 50 (10) ◽  
pp. 2775-2782 ◽  
Author(s):  
Owen A. Williams ◽  
Eva A. Zeestraten ◽  
Philip Benjamin ◽  
Christian Lambert ◽  
Andrew J. Lawrence ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Diffusion Tensor Image ◽  
Vessel Disease ◽  
Segmentation Technique

scholarly journals Nonlinear temporal dynamics of cerebral small vessel disease

Neurology ◽  
2017 ◽  
Vol 89 (15) ◽  
pp. 1569-1577 ◽  
Author(s):  
Esther M.C. van Leijsen ◽  
Ingeborg W.M. van Uden ◽  
Mohsen Ghafoorian ◽  
Mayra I. Bergkamp ◽  
Valerie Lohner ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Quadratic Term ◽  
Vessel Disease ◽  
Semiautomatic Segmentation ◽  
Over Time

Objective:To investigate the temporal dynamics of cerebral small vessel disease (SVD) by 3 consecutive assessments over a period of 9 years, distinguishing progression from regression.Methods:Changes in SVD markers of 276 participants of the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort (RUN DMC) cohort were assessed at 3 time points over 9 years. We assessed white matter hyperintensities (WMH) volume by semiautomatic segmentation and rated lacunes and microbleeds manually. We categorized baseline WMH severity as mild, moderate, or severe according to the modified Fazekas scale. We performed mixed-effects regression analysis including a quadratic term for increasing age.Results:Mean WMH progression over 9 years was 4.7 mL (0.54 mL/y; interquartile range 0.95–5.5 mL), 20.3% of patients had incident lacunes (2.3%/y), and 18.9% had incident microbleeds (2.2%/y). WMH volume declined in 9.4% of the participants during the first follow-up interval, but only for 1 participant (0.4%) throughout the whole follow-up. Lacunes disappeared in 3.6% and microbleeds in 5.7% of the participants. WMH progression accelerated over time: including a quadratic term for increasing age during follow-up significantly improved the model (p < 0.001). SVD progression was predominantly seen in participants with moderate to severe WMH at baseline compared to those with mild WMH (odds ratio [OR] 35.5, 95% confidence interval [CI] 15.8–80.0, p < 0.001 for WMH progression; OR 5.7, 95% CI 2.8–11.2, p < 0.001 for incident lacunes; and OR 2.9, 95% CI 1.4–5.9, p = 0.003 for incident microbleeds).Conclusions:SVD progression is nonlinear, accelerating over time, and a highly dynamic process, with progression interrupted by reduction in some, in a population that on average shows progression.

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