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Purpose: There is evidence that blood pressure variability (BPV) is associated with cerebral small vessel disease (SVD) and may therefore increase the risk of stroke and dementia. It remains unclear if BPV is associated with SVD progression over years. We examined whether visit-to-visit BPV is associated with white matter hyperintensity (WMH) progression over 14 years and MRI markers after 14 years., Materials and methods: We included participants with SVD from the Radboud University Nijmegen Diffusion tensor Magnetic resonance-imaging Cohort (RUNDMC) who underwent baseline assessment in 2006 and follow-up in 2011, 2015 and 2020. BPV was calculated as coefficient of variation (CV) of BP at all visits. Association between WMH progression rates over 14 years and BPV was examined using linear-mixed effects (LME) model. Regression models were used to examine association between BPV and MRI markers at final visit in participants., Results: A total of 199 participants (60.5 SD 6.6 years) who underwent four MRI scans and BP measurements were included, with mean follow-up of 13.7 (SD 0.5) years. Systolic BPV was associated with higher progression of WMH ( β  = 0.013, 95% CI 0.005 - 0.022) and higher risk of incident lacunes (OR: 1.10, 95% CI 1.01-1.21). There was no association between systolic BPV and grey and white matter volumes, Peak Skeleton of Mean Diffusivity (PSMD) or microbleed count after 13.7 years., Conclusions: Visit-to-visit systolic BPV is associated with increased progression of WMH volumes and higher risk of incident lacunes over 14 years in participants with SVD. Future studies are needed to examine causality of this association.

White matter hyperintensities (WMH) are the most prevalent markers of cerebral small vessel disease (SVD), which is the major vascular risk factor for dementia. Microvascular pathology and neuroinflammation are suggested to drive the transition from normal-appearing white matter (NAWM) to WMH, particularly in individuals with hypertension. However, current imaging techniques cannot capture ongoing NAWM changes. The transition from NAWM into WMH is a continuous process, yet white matter lesions are often examined dichotomously, which may explain their underlying heterogeneity. Therefore, we examined microvascular and neurovascular inflammation pathology in NAWM and severe WMH three-dimensionally, along with gradual magnetic resonance imaging (MRI) fluid-attenuated inversion recovery (FLAIR) signal (sub-)segmentation. In WMH, the vascular network exhibited reduced length and complexity compared to NAWM. Neuroinflammation was more severe in WMH. Vascular inflammation was more pronounced in NAWM, suggesting its potential significance in converting NAWM into WMH. Moreover, the (sub-)segmentation of FLAIR signal displayed varying degrees of vascular pathology, particularly within WMH regions. These findings highlight the intricate interplay between microvascular pathology and neuroinflammation in the transition from NAWM to WMH. Further examination of neurovascular inflammation across MRI-visible alterations could aid deepening our understanding on WMH conversion, and therewith how to improve the prognosis of SVD., (© 2024. The Author(s).)

The link between white matter hyperintensities (WMH) and cortical thinning is thought to be an important pathway by which WMH contributes to cognitive deficits in cerebral small vessel disease (SVD). However, the mechanism behind this association and the underlying tissue composition abnormalities are unclear. The objective of this study is to determine the association between WMH and cortical thickness, and the in vivo tissue composition abnormalities in the WMH-connected cortical regions. In this cross-sectional study, we included 213 participants with SVD who underwent standardized protocol including multimodal neuroimaging scans and cognitive assessment (i.e. processing speed, executive function and memory). We identified the cortex connected to WMH using probabilistic tractography starting from the WMH and defined the WMH-connected regions at three connectivity levels (low, medium and high connectivity level). We calculated the cortical thickness, myelin and iron of the cortex based on T1-weighted, quantitative R1, R2* and susceptibility maps. We used diffusion-weighted imaging to estimate the mean diffusivity of the connecting white matter tracts. We found that cortical thickness, R1, R2* and susceptibility values in the WMH-connected regions were significantly lower than in the WMH-unconnected regions (all Pcorrected < 0.001). Linear regression analyses showed that higher mean diffusivity of the connecting white matter tracts were related to lower thickness (β = -0.30, Pcorrected < 0.001), lower R1 (β = -0.26, Pcorrected = 0.001), lower R2* (β = -0.32, Pcorrected < 0.001) and lower susceptibility values (β = -0.39, Pcorrected < 0.001) of WMH-connected cortical regions at high connectivity level. In addition, lower scores on processing speed were significantly related to lower cortical thickness (β = 0.20, Pcorrected = 0.030), lower R1 values (β = 0.20, Pcorrected = 0.006), lower R2* values (β = 0.29, Pcorrected = 0.006) and lower susceptibility values (β = 0.19, Pcorrected = 0.024) of the WMH-connected regions at high connectivity level, independent of WMH volumes and the cortical measures of WMH-unconnected regions. Together, our study demonstrated that the microstructural integrity of white matter tracts passing through WMH is related to the regional cortical abnormalities as measured by thickness, R1, R2* and susceptibility values in the connected cortical regions. These findings are indicative of cortical thinning, demyelination and iron loss in the cortex, which is most likely through the disruption of the connecting white matter tracts and may contribute to processing speed impairment in SVD, a key clinical feature of SVD. These findings may have implications for finding intervention targets for the treatment of cognitive impairment in SVD by preventing secondary degeneration., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background and Objectives: It has been suggested that white matter hyperintensity lesions (WMHs), which typically progress over time, can also regress, and that this might be associated with favorable cognitive performance. We determined the prevalence of WMH regression in patients with cerebral small vessel disease (SVD) and examined which demographic, clinical, and radiological markers were associated with this regression., Methods: We used semi-automated lesion marking methods to quantify WMH volume at multiple timepoints in three cohorts with symptomatic SVD; two with moderate-to-severe symptomatic SVD (the SCANS observational cohort and the control arm of the PRESERVE interventional trial) and one with mild-to-moderate SVD (the RUN DMC observational cohort). Mixed-effects ordered logistic regression models were used to test which factors predicted participants to show WMH regression., Results: No participants (0/98) in SCANS, 6/42 (14.3%) participants in PRESERVE, and 6/276 (2.2%) in RUN DMC showed WMH regression. On multivariate analysis, only lower WMH volume (OR: 0.36, 95% CI: 0.23-0.56) and better white matter microstructural integrity assessed by fractional anisotropy using diffusion tensor imaging (OR: 1.55, 95% CI: 1.07-2.24) predicted participant classification as regressor versus stable or progressor., Discussion: Only a small proportion of participants demonstrated WMH regression across the three cohorts, when a blinded standardized assessment method was used. Subjects who showed regression had less severe imaging markers of disease at baseline. Our results show that lesion regression is uncommon in SVD and unlikely to be a major factor affecting the use of WMH quantification as an outcome for clinical trials.

White matter hyperintensities of presumed vascular origin (WMH) are the most common imaging feature of cerebral small vessel disease (cSVD) and are associated with cognitive impairment, especially information processing speed (IPS) deficits. However, it is unclear how WMH can directly impact IPS or whether the cortical thickness and brain connectivity mediate such association. In this study, it was evaluated the possible mediating roles of cortical thickness and brain (structural and functional) connectivity on the relationship between WMH (also considering its topography distribution) and IPS in 389 patients with cSVD from the RUN-DMC (Radboud University Nijmegen Diffusion tensor and Magnetic resonance imaging Cohort) database. Significant (p < 0.05 after multiple comparisons correction) associations of WMH volume and topography with cortical thickness, brain connectivity, and IPS performance in cSVD individuals were found. Additionally, cortical thickness and brain structural and functional connectivity were shown to mediate the association of WMH volume and location with IPS scores. More specifically, frontal cortical thickness, functional sensorimotor network, and posterior thalamic radiation tract were the essential mediators of WMH and IPS in this clinical group. This study provided insight into the mechanisms underlying the clinical relevance of white matter hyperintensities in information processing speed deficits in cSVD through cortical thinning and network disruptions., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

It remains unknown which factors influence how brain disconnectivity derived from White Matter Hyperintensity (WMH) lesions leads to psychomotor speed dysfunction, one of the earliest and most common cognitive manifestations in the cerebral Small Vessel Disease (cSVD) population. While the burden of WMH has been strongly linked to psychomotor speed performance, the effect that different locations and volumes of WMH may have on cSVD-related cognitive impairment remains unclear. Therefore, we aimed to explore (1) whether global WMH, deep WMH (DWMH), and periventricular (PVWMH) volumes display different psychomotor speed associations; (2) whether tract-specific WMH volume shows stronger cognitive associations compared with global measures of WMH volume; (3) whether specific patterns of WMH location lead to different degrees of disconnectivity. Using the BCBToolkit, we investigated which pattern of distribution and which locations of WMH lesion result in impaired psychomotor speed in a well-characterized sample (n = 195) of cSVD patients without dementia. Two key findings emerge from our study. First, global (and not tract-specific) measures of WMH volume were associated with psychomotor speed performance. Second, disconnection maps revealed the involvement of callosal tracts, association and projection fibers, and frontal and parietal cortical brain areas related to psychomotor speed, while the lesion location influenced such associations. In conclusion, psychomotor deficits are affected differently by WMH burden and topographic distribution through brain disconnection in non-demented cSVD patients., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data from the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 min and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm 2 and six non-collinear diffusion gradient directions for each b-value. Seven different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi 2 ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi 2 values were found using the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a nine-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions., (© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Background: Structural network damage is a potentially important mechanism by which cerebral small vessel disease (SVD) can cause cognitive impairment. As a central hub of the structural network, the role of thalamus in SVD-related cognitive impairments remains unclear. We aimed to determine the associations between the structural alterations of thalamic subregions and cognitive impairments in SVD., Methods: In this cross-sectional study, 205 SVD participants without thalamic lacunes from the third follow-up (2020) of the prospective RUN DMC study (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort), which was initiated in 2006, Nijmegen, were included. Cognitive functions included processing speed, executive function, and memory. Probabilistic tractography was performed from thalamus to 6 cortical regions, followed by connectivity-based thalamic segmentation to assess each thalamic subregion volume and connectivity (measured by mean diffusivity [MD] of the connecting white matter tracts) with the cortex. Least absolute shrinkage and selection operator regression analysis was conducted to identify the volumes or connectivity of the total thalamus and 6 thalamic subregions that have the strongest association with cognitive performance. Linear regression and mediation analyses were performed to test the association of least absolute shrinkage and selection operator-selected thalamic subregion volume or MD with cognitive performance, while adjusting for age and education., Results: We found that higher MD of the thalamic-motor tract was associated with worse processing speed (β=-0.27; P <0.001), higher MD of the thalamic-frontal tract was associated with worse executive function (β=-0.24; P =0.001), and memory (β=-0.28; P <0.001), respectively. The mediation analysis showed that MD of thalamocortical tracts mediated the association between corresponding thalamic subregion volumes and the cognitive performances in 3 domains., Conclusions: Our results suggest that the structural alterations of thalamus are linked to cognitive impairment in SVD, largely depending on the damage pattern of the white matter tracts connecting specific thalamic subregions and cortical regions.

Alzheimer's disease and cerebral small vessel disease are the two leading causes of cognitive decline and dementia and coexist in most memory clinic patients. White matter damage as assessed by diffusion MRI is a key feature in both Alzheimer's and cerebral small vessel disease. However, disease-specific biomarkers of white matter alterations are missing. Recent advances in diffusion MRI operating on the fixel level (fibre population within a voxel) promise to advance our understanding of disease-related white matter alterations. Fixel-based analysis allows derivation of measures of both white matter microstructure, measured by fibre density, and macrostructure, measured by fibre-bundle cross-section. Here, we evaluated the capacity of these state-of-the-art fixel metrics to disentangle the effects of cerebral small vessel disease and Alzheimer's disease on white matter integrity. We included three independent samples (total n = 387) covering genetically defined cerebral small vessel disease and age-matched controls, the full spectrum of biomarker-confirmed Alzheimer's disease including amyloid- and tau-PET negative controls and a validation sample with presumed mixed pathology. In this cross-sectional analysis, we performed group comparisons between patients and controls and assessed associations between fixel metrics within main white matter tracts and imaging hallmarks of cerebral small vessel disease (white matter hyperintensity volume, lacune and cerebral microbleed count) and Alzheimer's disease (amyloid- and tau-PET), age and a measure of neurodegeneration (brain volume). Our results showed that (i) fibre density was reduced in genetically defined cerebral small vessel disease and strongly associated with cerebral small vessel disease imaging hallmarks; (ii) fibre-bundle cross-section was mainly associated with brain volume; and (iii) both fibre density and fibre-bundle cross-section were reduced in the presence of amyloid, but not further exacerbated by abnormal tau deposition. Fixel metrics were only weakly associated with amyloid- and tau-PET. Taken together, our results in three independent samples suggest that fibre density captures the effect of cerebral small vessel disease, while fibre-bundle cross-section is largely determined by neurodegeneration. The ability of fixel-based imaging markers to capture distinct effects on white matter integrity can propel future applications in the context of precision medicine., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

The major vascular cause of dementia is cerebral small vessel disease (SVD), including white matter hyperintensities (WMH) amongst others. While the underlying pathology of SVD remains unclear, chronic hypertension and neuroinflammation are recognized as important risk factors for SVD and for the conversion of normal-appearing white matter (NAWM) to WMH. Unfortunately, most studies investigating the role of neuroinflammation in WMH relied on peripheral blood markers, e.g., markers for systemic or vascular inflammation, as a proxy for inflammation in the brain itself. However, it is unknown whether such markers accurately capture inflammatory changes within the cerebral white matter. Therefore, we aimed to comprehensively investigate the impact of hypertension on perivascular- and neuroinflammation in both WMH and NAWM. We conducted high field brain magnetic resonance imaging (MRI), followed by (immuno-)histopathological staining of neuroinflammatory markers on human post-mortem brains of elderly people with a history of hypertension (n = 17) and age-matched normotensive individuals (n = 5). MRI images were co-registered to (immuno-)histopathological data including stainings for microglia and astroglia to assess changes in MRI-based WMH at microscopic resolution. Perivascular inflammation was carefully assessed based on the severity of perivascular astrogliosis of the smallest vessels throughout white matter regions. Hypertension was associated with a larger inflammatory response in both WMH and NAWM. Notably, the presence of close-range perivascular inflammation was twice as common among those with hypertension than in controls both in WMH and NAWM, suggesting that neurovascular inflammation is critical in the etiology of WMH. Moreover, a higher degree of microglial activation was related to a higher burden of WMH. Our results indicate that neuro(vascular)inflammation at the level of the brain itself is involved in the etiology of WMH. Future therapeutic strategies focusing on multitarget interventions including antihypertensive treatment as well as neuroinflammation may ameliorate WMH progression., (© 2023. The Author(s).)

Background: The underlying mechanisms of incident lacunes regarding their spatial distribution remain largely unknown. We investigated the spatial distribution pattern and MRI predictors of incident lacunes in relation to white matter hyperintensity (WMH) over 14 years follow-up in sporadic small vessel disease., Methods: Five hundred three participants from the ongoing prospective single-center Radboud University Nijmegen Diffusion Tensor and Magnetic resonance Cohort (RUN DMC) were recruited with baseline assessment in 2006 and follow ups in 2011, 2015, and 2020. Three hundred eighty-two participants who underwent at least 2 available brain MRI scans were included. Incident lacunes were systematically identified, and the spatial relationship between incident lacunes located in subcortical white matter and WMH were determined using a visual rating scale. Adjusted multiple logistic regression and linear mixed-effect regression models were used to assess the association between baseline small vessel disease markers, WMH progression, and incident lacunes. Participants with atrial fibrillation were excluded in multivariable analysis., Results: Eighty incident lacunes were identified in 43 patients (mean age 66.5±8.2 years, 37.2% women) during a mean follow-up time of 11.2±3.3 years (incidence rate 10.0/1000 person-year). Sixty percent of incident lacunes were in the white matter, of which 48.9% showed no contact with preexisting WMH. Baseline WMH volume (odds ratio=2.5 [95% CI, 1.6-4.2]) predicted incident lacunes after adjustment for age, sex, and vascular risk factors. WMH progression was associated with incident lacunes independent of age, sex, baseline WMH volume, and vascular risk factors (odds ratio, 3.2 [95% CI, 1.5-6.9]). Baseline WMH volume and progression rate were higher in participants with incident lacunes in contact with preexisting WMH. No difference in vascular risk factors was observed regarding location or relation with preexisting WMH., Conclusions: The 2 different distribution patterns of lacunes regarding their relation to WMH may suggest distinct underlying mechanisms, one of which may be more closely linked to a similar pathophysiology as that of WMH. The longitudinal relation between WMH and lacunes further supports plausible shared mechanisms between the 2 key markers.

Background: The aim of this study is to investigate the temporal dynamics of small vessel disease (SVD) and the effect of vascular risk factors and baseline SVD burden on progression of SVD with 4 neuroimaging assessments over 14 years in patients with SVD., Methods: Five hundred three patients with sporadic SVD (50-85 years) from the ongoing prospective cohort study (RUN DMC [Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort]) underwent baseline assessment in 2006 and follow-up in 2011, 2015, and 2020. Vascular risk factors and magnetic resonance imaging markers of SVD were evaluated. Linear mixed-effects model and negative binomial regression model were used to examine the determinants of temporal dynamics of SVD markers., Results: A total of 382 SVD patients (mean [SD] 64.1 [8.4]; 219 men and 163 women) who underwent at least 2 serial brain magnetic resonance imaging scans were included, with mean (SD) follow-up of 11.15 (3.32) years. We found a highly variable temporal course of SVD. Mean (SD) WMH progression rate was 0.6 (0.74) mL/y (range, 0.02-4.73 mL/y) and 13.6% of patients had incident lacunes (1.03%/y) over the 14-year follow-up. About 4% showed net WMH regression over 14 years, whereas 38 out of 361 (10.5%), 5 out of 296 (2%), and 61 out of 231 (26%) patients showed WMH regression for the intervals 2006 to 2011, 2011 to 2015, and 2015 to 2020, respectively. Of these, 29 (76%), 5 (100%), and 57 (93%) showed overall progression across the 14-year follow-up, and the net overall WMH change between first and last scan considering all participants was a net average WMH progression over the 14-year period. Older age was a strong predictor for faster WMH progression and incident lacunes. Patients with mild baseline WMH rarely progressed to severe WMH. In addition, both baseline burden of SVD lesions and vascular risk factors independently and synergistically predicted WMH progression, whereas only baseline SVD burden predicted incident lacunes over the 14-year follow-up., Conclusions: SVD shows pronounced progression over time, but mild WMH rarely progresses to clinically severe WMH. WMH regression is noteworthy during some magnetic resonance imaging intervals, although it could be overall compensated by progression over the long follow-up.

Background: To investigate changes in gait performance over time and how these changes are associated with the decline in structural network efficiency and cognition in older patients with cerebral small vessel disease (SVD)., Methods: In a prospective, single-center cohort with 217 older participants with SVD, we performed 1.5T MRI scans, cognitive tests, and gait assessments evaluated by Timed UP and Go (TUG) test twice over 4 years. We reconstructed the white matter network for each subject based on diffusion tensor imaging tractography, followed by graph-theoretical analyses to compute the global efficiency. Conventional MRI markers for SVD, that is, white matter hyperintensity (WMH) volume, number of lacunes, and microbleeds, were assessed., Results: Baseline global efficiency was not related to changes in gait performance, while decline in global efficiency over time was significantly associated with gait decline (ie, increase in TUG time), independent of conventional MRI markers for SVD. Neither baseline cognitive performance nor cognitive decline was associated with gait decline., Conclusions: We found that disruption of the white matter structural network was associated with gait decline over time, while the effect of cognitive decline was not. This suggests that structural network disruption has an important role in explaining the pathophysiology of gait decline in older patients with SVD, independent of cognitive decline., (© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Background: DTI is sensitive to white matter (WM) microstructural damage and has been suggested as a surrogate marker for phase 2 clinical trials in cerebral small vessel disease (SVD). The study's objective is to establish the best way to analyse the diffusion-weighted imaging data in SVD for this purpose. The ideal method would be sensitive to change and predict dementia conversion, but also straightforward to implement and ideally automated. As part of the OPTIMAL collaboration, we evaluated five different DTI analysis strategies across six different cohorts with differing SVD severity., Methods: Those 5 strategies were: (1) conventional mean diffusivity WM histogram measure (MD median), (2) a principal component-derived measure based on conventional WM histogram measures (PC1), (3) peak width skeletonized mean diffusivity (PSMD), (4) diffusion tensor image segmentation θ (DSEG θ) and (5) a WM measure of global network efficiency (Geff). The association between each measure and cognitive function was tested using a linear regression model adjusted by clinical markers. Changes in the imaging measures over time were determined. In three cohort studies, repeated imaging data together with data on incident dementia were available. The association between the baseline measure, change measure and incident dementia conversion was examined using Cox proportional-hazard regression or logistic regression models. Sample size estimates for a hypothetical clinical trial were furthermore computed for each DTI analysis strategy., Results: There was a consistent cross-sectional association between the imaging measures and impaired cognitive function across all cohorts. All baseline measures predicted dementia conversion in severe SVD. In mild SVD, PC1, PSMD and Geff predicted dementia conversion. In MCI, all markers except Geff predicted dementia conversion. Baseline DTI was significantly different in patients converting to vascular dementia than to Alzheimer' s disease. Significant change in all measures was associated with dementia conversion in severe but not in mild SVD. The automatic and semi-automatic measures PSMD and DSEG θ required the lowest minimum sample sizes for a hypothetical clinical trial in single-centre sporadic SVD cohorts., Conclusion: DTI parameters obtained from all analysis methods predicted dementia, and there was no clear winner amongst the different analysis strategies. The fully automated analysis provided by PSMD offers advantages particularly for large datasets., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

The presence of white matter lesions in patients with cerebral small vessel disease (SVD) is among the main causes of cognitive decline. We investigated the relation between white matter hyperintensity (WMH) locations and executive and language abilities in 442 SVD patients without dementia with varying burden of WMH. We used Stroop Word Reading, Stroop Color Naming, Stroop Color-Word Naming, and Category Fluency as language measures with varying degrees of executive demands. The Symbol Digit Modalities Test (SDMT) was used as a control task, as it measures processing speed without requiring language use or verbal output. A voxel-based lesion-symptom mapping (VLSM) approach was used, corrected for age, sex, education, and lesion volume. VLSM analyses revealed statistically significant clusters for tests requiring language use, but not for SDMT. Worse scores on all tests were associated with WMH in forceps minor, thalamic radiations and caudate nuclei. In conclusion, an association was found between WMH in a core frontostriatal network and executive-verbal abilities in SVD, independent of lesion volume and processing speed. This circuitry underlying executive-language functioning might be of potential clinical importance for elderly with SVD. More detailed language testing is required in future research to elucidate the nature of language production difficulties in SVD., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Objective: To test the hypothesis that multi-shell diffusion models improve the characterization of microstructural alterations in cerebral small vessel disease (SVD), we assessed associations with processing speed performance, longitudinal change, and reproducibility of diffusion metrics., Methods: We included 50 patients with sporadic and 59 patients with genetically defined SVD (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) with cognitive testing and standardized 3T MRI, including multi-shell diffusion imaging. We applied the simple diffusion tensor imaging (DTI) model and 2 advanced models: diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI). Linear regression and multivariable random forest regression (including conventional SVD markers) were used to determine associations between diffusion metrics and processing speed performance. The detection of short-term disease progression was assessed by linear mixed models in 49 patients with sporadic SVD with longitudinal high-frequency imaging (in total 459 MRIs). Intersite reproducibility was determined in 10 patients with CADASIL scanned back-to-back on 2 different 3T MRI scanners., Results: Metrics from DKI showed the strongest associations with processing speed performance ( R 2 up to 21%) and the largest added benefit on top of conventional SVD imaging markers in patients with sporadic SVD and patients with CADASIL with lower SVD burden. Several metrics from DTI and DKI performed similarly in detecting disease progression. Reproducibility was excellent (intraclass correlation coefficient >0.93) for DTI and DKI metrics. NODDI metrics were less reproducible., Conclusion: Multi-shell diffusion imaging and DKI improve the detection and characterization of cognitively relevant microstructural white matter alterations in SVD. Excellent reproducibility of diffusion metrics endorses their use as SVD markers in research and clinical care. Our publicly available intersite dataset facilitates future studies., Classification of Evidence: This study provides Class I evidence that in patients with SVD, diffusion MRI metrics are associated with processing speed performance., (© 2020 American Academy of Neurology.)

Cerebral small vessel disease (SVD), including white matter hyperintensities (WMH), microbleeds, lacunes, was related to gait disturbances, while the underlying mechanism is unclear. Here, we investigated the relation between structural network efficiency, cognition and gait performance in 272 elderly subjects with SVD. All participants underwent 1.5 T MRI, gait and neuropsychological assessment. Conventional MRI markers for SVD, i.e. WMH volume, number of lacunes and microbleeds, were assessed. Diffusion tensor imaging-based tractography was used to reconstruct the brain network for each individual, followed by graph-theoretical analyses to compute the well-established network measure, global efficiency. We found that lower global efficiency was associated with worse gait performance, including slower gait speed and shorter stride length, independent of conventional MRI markers for SVD. This association was partly mediated via cognitive function. We identified subnetworks of white matter connections associated with gait and cognition, characterized by dominant involvement of frontal tracts. Our findings suggest that network disruption is associated with gait disturbances through cognitive dysfunction in elderly with SVD. Gait is a highly cognitive process and the crucial role of cognition should be considered when investigating gait disturbances in the elderly with SVD., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

White matter hyperintensities (WMH) constitute the visible spectrum of cerebral small vessel disease (SVD) markers and are associated with cognitive decline, although they do not fully account for memory decline observed in individuals with SVD. We hypothesize that WMH might exert their effect on memory decline indirectly by affecting remote brain structures such as the hippocampus. We investigated the temporal interactions between WMH, hippocampal atrophy and memory decline in older adults with SVD. Five hundred and three participants of the RUNDMC study underwent neuroimaging and cognitive assessments up to 3 times over 8.7 years. We assessed WMH volumes semi-automatically and calculated hippocampal volumes (HV) using FreeSurfer. We used linear mixed effects models and causal mediation analyses to assess both interaction and mediation effects of hippocampal atrophy in the associations between WMH and memory decline, separately for working memory (WM) and episodic memory (EM). Linear mixed effect models revealed that the interaction between WMH and hippocampal volumes explained memory decline (WM: β = .067; 95%CI[.024-0.111]; p < .01; EM: β = .061; 95%CI[.025-.098]; p < .01), with better model fit when the WMH*HV interaction term was added to the model, for both WM (likelihood ratio test, χ 2 [1] = 9.3, p < .01) and for EM (likelihood ratio test, χ 2 [1] = 10.7, p < .01). Mediation models showed that both baseline WMH volume (β = -.170; p = .001) and hippocampal atrophy (β = 0.126; p = .009) were independently related to EM decline, but the effect of baseline WMH on EM decline was not mediated by hippocampal atrophy (p value indirect effect: 0.572). Memory decline in elderly with SVD was best explained by the interaction of WMH and hippocampal volumes. The relationship between WMH and memory was not causally mediated by hippocampal atrophy, suggesting that memory decline during aging is a heterogeneous condition in which different pathologies contribute to the memory decline observed in elderly with SVD., (© 2018 The Authors. Hippocampus published by Wiley Periodicals, Inc.)

Objective: To investigate whether white matter network disruption underlies the pathogenesis of apathy, but not depression, in cerebral small vessel disease (SVD)., Methods: Three hundred thirty-one patients with SVD from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) study completed measures of apathy and depression and underwent structural MRI. Streamlines reflecting underlying white matter fibers were reconstructed with diffusion tensor tractography. First, path analysis was used to determine whether network measures mediated associations between apathy and radiologic markers of SVD. Next, we examined differences in whole-brain network measures between participants with only apathy, only depression, and comorbid apathy and depression and a control group free of neuropsychiatric symptoms. Finally, we examined regional network differences associated with apathy., Results: Path analysis demonstrated that network disruption mediated the relationship between apathy and SVD markers. Patients with apathy, compared to all other groups, were impaired on whole-brain measures of network density and efficiency. Regional network analyses in both the apathy subgroup and the entire sample revealed that apathy was associated with impaired connectivity in premotor and cingulate regions., Conclusions: Our results suggest that apathy, but not depression, is associated with white matter tract disconnection in SVD. The subnetworks delineated suggest that apathy may be driven by damage to white matter networks underlying action initiation and effort-based decision making., (Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Cerebral small vessel disease (SVD) is considered the most important vascular contributor to the development of cognitive impairment and dementia. There is increasing awareness that SVD exerts its clinical effects by disrupting white matter connections, predominantly disrupting connections between rich club nodes, a set of highly connected and interconnected regions. Here we examined the progression of disturbances in rich club organization in older adults with SVD and their associations with conventional SVD markers and cognitive decline. We additionally investigated associations of baseline network measures with dementia. In 270 participants of the RUN DMC study, we performed diffusion tensor imaging (DTI) and cognitive assessments longitudinally. Rich club organization was examined in structural networks derived from DTI followed by deterministic tractography. Global efficiency (p<0.05) and strength of rich club connections (p<0.001) declined during follow-up. Decline in strength of peripheral connections was associated with a decline in overall cognition (β=0.164; p<0.01), psychomotor speed (β=0.151; p<0.05) and executive function (β=0.117; p<0.05). Baseline network measures were reduced in participants with dementia, and the association between WMH and dementia was causally mediated by global efficiency (p = =0.037) and peripheral connection strength (p = =0.040). SVD-related disturbances in rich club organization progressed over time, predominantly in participants with severe SVD. In this study, we found no specific role of rich club connectivity disruption in causing cognitive decline or dementia. The effect of WMH on dementia was mediated by global network efficiency and the strength of peripheral connections, suggesting an important role for network disruption in causing cognitive decline and dementia in older adults with SVD., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and Purpose: White matter hyperintensities (WMH) are frequently seen on neuroimaging of elderly and are associated with cognitive decline and the development of dementia. Yet, the temporal dynamics of conversion of normal-appearing white matter (NAWM) into WMH remains unknown. We examined whether and when progression of WMH was preceded by changes in fluid-attenuated inversion recovery and diffusion tensor imaging values, thereby taking into account differences between participants with mild versus severe baseline WMH., Methods: From 266 participants of the RUN DMC study (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort), we semiautomatically segmented WMH at 3 time points for 9 years. Images were registered to standard space through a subject template. We analyzed differences in baseline fluid-attenuated inversion recovery, fractional anisotropy, and mean diffusivity (MD) values and changes in MD values over time between 4 regions: (1) remaining NAWM, (2) NAWM converting into WMH in the second follow-up period, (3) NAWM converting into WMH in the first follow-up period, and (4) WMH., Results: NAWM converting into WMH in the first or second time interval showed higher fluid-attenuated inversion recovery and MD values than remaining NAWM. MD values in NAWM converting into WMH in the first time interval were similar to MD values in WMH. When stratified by baseline WMH severity, participants with severe WMH had higher fluid-attenuated inversion recovery and MD and lower fractional anisotropy values than participants with mild WMH, in all areas including the NAWM. MD values in WMH and in NAWM that converted into WMH continuously increased over time., Conclusions: Impaired microstructural integrity preceded conversion into WMH and continuously declined over time, suggesting a continuous disease process of white matter integrity loss that can be detected using diffusion tensor imaging even years before WMH become visible on conventional neuroimaging. Differences in microstructural integrity between participants with mild versus severe WMH suggest heterogeneity of both NAWM and WMH, which might explain the clinical variability observed in patients with similar small vessel disease severity., (© 2018 American Heart Association, Inc.)

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

Purpose: White matter hyperintensities (WMH) are seen on FLAIR-MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small., Methods: A two-stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in-plane effective diameter). For each size-specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first-stage classifiers were combined into a single WMH likelihood by a second-stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free-response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs., Results: Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives., Conclusions: The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader.

Objective: To establish a fully automated, robust imaging marker for cerebral small vessel disease (SVD) and related cognitive impairment that is easy to implement, reflects disease burden, and is strongly associated with processing speed, the predominantly affected cognitive domain in SVD., Methods: We developed a novel magnetic resonance imaging marker based on diffusion tensor imaging, skeletonization of white matter tracts, and histogram analysis. The marker (peak width of skeletonized mean diffusivity [PSMD]) was assessed along with conventional SVD imaging markers. We first evaluated associations with processing speed in patients with genetically defined SVD (n = 113). Next, we validated our findings in independent samples of inherited SVD (n = 57), sporadic SVD (n = 444), and memory clinic patients with SVD (n = 105). The new marker was further applied to healthy controls (n = 241) and to patients with Alzheimer's disease (n = 153). We further conducted a longitudinal analysis and interscanner reproducibility study., Results: PSMD was associated with processing speed in all study samples with SVD (p-values between 2.8 × 10(-3) and 1.8 × 10(-10) ). PSMD explained most of the variance in processing speed (R(2) ranging from 8.8% to 46%) and consistently outperformed conventional imaging markers (white matter hyperintensity volume, lacune volume, and brain volume) in multiple regression analyses. Increases in PSMD were linked to vascular but not to neurodegenerative disease. In longitudinal analysis, PSMD captured SVD progression better than other imaging markers., Interpretation: PSMD is a new, fully automated, and robust imaging marker for SVD. PSMD can easily be applied to large samples and may be of great utility for both research studies and clinical use. Ann Neurol 2016;80:581-592., (© 2016 American Neurological Association.)

Background and Purpose: Poststroke cognitive impairment occurs frequently in young patients with ischemic stroke (18 through 50 years of age). Accumulating data suggest that stroke is associated with lower white matter integrity remote from the stroke impact area, which might explain why some patients have good long-term cognitive outcome and others do not. Given the life expectancy of decades in young patients, we therefore investigated remote white matter in relation to long-term cognitive function., Methods: We included all consecutive first-ever ischemic stroke patients, left/right hemisphere, without recurrent stroke or transient ischemic attack during follow-up, aged 18 through 50 years, admitted to our university medical center between 1980 and 2010. One hundred seventeen patients underwent magnetic resonance imaging scanning including a T1-weighted scan, a diffusion tensor imaging scan, and completed a neuropsychological assessment. Patients were compared with a matched stroke-free control group (age, sex, and education matched). Cognitive impairment was defined as >1.5 SD below the mean cognitive index score of controls and no cognitive impairment as ≤1 SD. Tract-Based Spatial Statistics was used to assess the white matter integrity (fractional anisotropy and mean diffusivity)., Results: About 11 years after ischemic stroke, lower remote white matter integrity was associated with a worse long-term cognitive performance. A lower remote white matter integrity, even in the contralesional hemisphere, was observed in cognitively impaired patients (n=25) compared with cognitively unimpaired patients (n=71)., Conclusions: These findings indicate that although stroke has an acute onset, it might have long lasting effects on remote white matter integrity and thereby increases the risk of long-term cognitive impairment., (© 2016 American Heart Association, Inc.)

Background: The relationship between cerebral small vessel disease (SVD) and dementia has been studied without considering white matter (WM) volume, the microstructural integrity of the WM surrounding the SVD, and grey matter (GM)., Objective: We prospectively investigated the relationship between these structures and the risk of dementia, and formed a prediction model to investigate which characteristics (macro- or microstructural) explained most of the variance., Methods: The RUN DMC study is a prospective cohort study among 503 non-demented participants with an age between 50 and 85 years at baseline, with baseline assessment in 2006 and follow-up assessment in 2012. Two were lost to follow-up (yielding a 99.6% response-rate). Cox regression analysis was used, to calculate hazard ratios for dementia, of baseline MRI characteristics. Tract-Based Spatial Statistics (TBSS) analysis was used to assess the added value of microstructural integrity of the WM., Results: Mean age at baseline was 65.6 years (SD 8.8) and 56.8% was male. 43 participants developed dementia (8.6%), resulting in a 5.5-year cumulative risk of 11.1% (95% CI 7.7-14.6). Low WM and hippocampal volume are significant predictors for dementia. WM, WM hyperintensities, and hippocampal volume explained most of the variance. TBSS analyses showed no additional value of diffusion parameters., Conclusions: WM and hippocampal volume were the main predictors for the development of incident dementia at 5-year follow-up in elderly with SVD. There was no additional diagnostic value of the diffusion tensor imaging parameters on top of the macrostructural characteristics.

Objective: Depressive symptoms are common in elderly with cerebral small vessel disease (SVD). As not every individual with SVD experiences depressive symptoms, other factors might play a role. We therefore investigated the white matter (WM) integrity of the white matter tracts in elderly with depressive symptoms, independent of global cognitive function, by applying the tract-based spatial statistics (TBSS)., Design: Prospective cohort study with cross-sectional baseline data., Setting: Radboud University Nijmegen Medical Centre, The Netherlands., Participants: 438 individuals aged between 50-85 years, with SVD without dementia., Measurements: Diffusion tensor imaging parameters and depressive symptoms, assessed with the Center for Epidemiologic Studies Depression Scale., Results: Compared with non-depressed participants (N = 287), those with depressive symptoms (N = 151) had lower fractional anisotropy in the genu and body of the corpus callosum, bilateral inferior fronto-occipital fasciculus, uncinate fasciculus, and corona radiata. These differences disappeared after adjustment for white matter hyperintensities (WMH) and lacunar infarcts. Mean-, axial- and radial diffusivity were higher in these areas in participants with depressive symptoms. After additional adjustment for WMH and lacunar infarcts, the changes observed in radial diffusivity also disappeared. Adding global cognition as confounding variable altered the diffusion parameters only slightly., Conclusion: This study indicates that elderly with depressive symptoms show a lower WM integrity, independent of global cognitive function, and that the presence of SVD is mostly responsible, affecting the fronto-subcortical regions and hereby disrupting the neural circuitry involved in mood regulation., (Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.)

Cerebral small vessel disease, including white matter hyperintensities (WMH) and lacunes of presumed vascular origin, is common in elderly people and is related to cognitive impairment and dementia. One possible mechanism could be the disruption of white matter tracts (both within WMH and normal-appearing white matter) that connect distributed brain regions involved in cognitive functions. Here, we investigated the relation between microstructural integrity of the white matter and cognitive functions in patients with small vessel disease. The Radboud University Nijmegen Diffusion tensor and Magnetic resonance Cohort study is a prospective cohort study among 444 independently living, non-demented elderly with cerebral small vessel disease, aged between 5500 and 85 years. All subjects underwent magnetic resonance imaging and diffusion tensor imaging scanning and an extensive neuropsychological assessment. We showed that loss of microstructural integrity of the white matter at specific locations was related to specific cognitive disturbances, which was mainly located in the normal-appearing white matter (p < 0.05, FWE-corrected for multiple comparisons). The microstructural integrity in the genu and splenium showed the highest significant relation with global cognitive function and executive functions, in the cingulum bundle with verbal memory performance. Associations between diffusion tensor imaging parameters and most cognitive domains remained present after adjustment for WMH and lacunes. In conclusion, cognitive disturbances in subjects with cerebral small vessel disease are related to microstructural integrity of multiple white matter fibers (within WMH and normal-appearing white matter) connecting different cortical and subcortical regions.

Background and Purpose: White matter hyperintensities (WMH) are associated with clinically heterogeneous symptoms that cannot be explained by these lesions alone. It is hypothesized that these lesions are associated with distant cortical atrophy and cortical thickness network measures, which can result in an additional cognitive impairment. Here, we investigated the relationships between WMH, cortical thickness, and cognition in subjects with cerebral small vessel disease., Methods: A total of 426 subjects with cerebral small vessel disease were included, aged between 50 and 85 years, without dementia, and underwent MRI scanning. Cortical thickness analysis was performed, and WMH were manually segmented. Graph theory was applied to examine the relationship between network measures and WMH, and structural covariance matrices were constructed using inter-regional cortical thickness correlations., Results: Higher WMH load was related to lower cortical thickness in frontotemporal regions, whereas in paracentral regions, this was related to higher cortical thickness. Network analyses revealed that measures of network disruption were associated with WMH and cognitive performance. Furthermore, WMH in specific white matter tracts were related to regional-specific cortical thickness and network measures. Cognitive performances were related to cortical thickness in frontotemporal regions and network measures, and not to WMH, while controlling for cortical thickness., Conclusions: These cross-sectional results suggest that cortical changes (regional-specific damage and network breakdown), mediated (in)directly by WMH (tract-specific damage) and other factors (eg, vascular risk factors), might lead to cognitive decline. These findings have implications in understanding the relationship between WMH, cortical morphology, and the possible attendant cognitive decline and eventually dementia., (© 2015 American Heart Association, Inc.)

Vascular factors play a role in the etiology of Alzheimer's disease (AD), presumably due to emergence of white matter lesions. However, important white matter structures involved in the etiology of AD, including the corpus callosum (CC), remain invariably free from macroscopical white matter lesions, although loss of microstructural integrity assessed with diffusion tensor imaging (DTI) has been described in the CC. Vascular factors have been related to these microstructural white matter changes too, but little is known about their effect on the CC. In 499 subjects with cerebral small vessel disease, aged 50-85 years, we cross-sectionally investigated the relation between hypertension, hypertension treatment status, the microstructural integrity of the CC using DTI, and the attendant cognitive performance. Fractional anisotropy and mean diffusivity were calculated in four substructures of the CC (genu, anterior body, posterior body, and splenium). Differences between groups were calculated with analysis of variance, adjusted for age, gender, and cardiovascular risk factors. Compared with normotensive subjects, hypertensive subjects had a lower fractional anisotropy in the splenium and a significant higher mean diffusivity in both the anterior body and the splenium; this was most noticeable in treated uncontrolled hypertensive subjects. Furthermore we found that microstructural integrity of the CC was related to global cognition. Of this relation, 14 to 60% was explained by the mediating effect of small vessel disease elsewhere in the white matter. Our findings indicate that adequate blood pressure treatment might postpone these changes and the attendant cognitive dysfunction. [ABSTRACT FROM AUTHOR]