Change in multimodal MRI markers predicts dementia risk in cerebral small vessel disease

Alterations in structural integrity of superior longitudinal fasciculus III associated with cognitive performance in cerebral small vessel disease

BACKGROUND

This study aimed to investigate the alterations in structural integrity of superior longitudinal fasciculus subcomponents with increasing white matter hyperintensity severity as well as the relationship to cognitive performance in cerebral small vessel disease.

METHODS

110 cerebral small vessel disease study participants with white matter hyperintensities were recruited. According to Fazekas grade scale, white matter hyperintensities of each subject were graded. All subjects were divided into two groups. The probabilistic fiber tracking method was used for analyzing microstructure characteristics of superior longitudinal fasciculus subcomponents.

RESULTS

Probabilistic fiber tracking results showed that mean diffusion, radial diffusion, and axial diffusion values of the left arcuate fasciculus as well as the mean diffusion value of the right arcuate fasciculus and left superior longitudinal fasciculus III in high white matter hyperintensities rating group were significantly higher than those in low white matter hyperintensities rating group (p < 0.05). The mean diffusion value of the left superior longitudinal fasciculus III was negatively related to the Montreal Cognitive Assessment score of study participants (p < 0.05).

CONCLUSIONS

The structural integrity injury of bilateral arcuate fasciculus and left superior longitudinal fasciculus III is more severe with the aggravation of white matter hyperintensities. The structural integrity injury of the left superior longitudinal fasciculus III correlates to cognitive impairment in cerebral small vessel disease.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

BACKGROUND AND OBJECTIVES

Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored.

METHODS

We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden.

RESULTS

The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden.

CONCLUSIONS

We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Essential Nutrients and White Matter Hyperintensities: A Two-Sample Mendelian Randomization Study

Stroke and dementia have been linked to the appearance of white matter hyperintensities (WMHs). Meanwhile, diffusion tensor imaging (DTI) might capture the microstructural change in white matter early. Specific dietary interventions may help to reduce the risk of WMHs. However, research on the relationship between specific nutrients and white matter changes is still lacking. We aimed to investigate the causal effects of essential nutrients (amino acids, fatty acids, mineral elements, and vitamins) on WMHs and DTI measures, including fraction anisotropy (FA) and mean diffusivity (MD), by a Mendelian randomization analysis. We selected single nucleotide polymorphisms (SNPs) associated with each nutrient as instrumental variables to assess the causal effects of nutrient-related exposures on WMHs, FA, and MD. The outcome was from a recently published large-scale European Genome Wide Association Studies pooled dataset, including WMHs (N = 18,381), FA (N = 17,663), and MD (N = 17,467) data. We used the inverse variance weighting (IVW) method as the primary method, and sensitivity analyses were conducted using the simple median, weighted median, and MR-Egger methods. Genetically predicted serum calcium level was positively associated with WMHs risk, with an 8.1% increase in WMHs risk per standard deviation unit increase in calcium concentration (OR = 1.081, 95% CI = 1.006-1.161, p = 0.035). The plasma linoleic acid level was negatively associated with FA (OR = 0.776, 95% CI = 0.616-0.978, p = 0.032). Our study demonstrated that genetically predicted calcium was a potential risk factor for WMHs, and linoleic acid may be negatively associated with FA, providing evidence for interventions from the perspective of gene-environment interactions.

Sleep Characteristics and Risk of Stroke and Dementia: An Observational and Mendelian Randomization Study

BACKGROUND AND OBJECTIVES

Sleep disturbances are implicated as risk factors of both stroke and dementia. However, whether these associations are causal and whether treatment of sleep disorders could reduce stroke and dementia risk remain uncertain. We aimed to evaluate associations and ascertain causal relationships between sleep characteristics and stroke/dementia risk and MRI markers of small vessel disease (SVD).

METHODS

We used data sets from a multicenter population-based study and summary statistics from genome-wide association studies (GWASs) of sleep characteristics and outcomes. We analyzed 502,383 UK Biobank participants with self-reported sleep measurements, including sleep duration, insomnia, chronotype, napping, daytime dozing, and snoring. In observational analyses, the primary outcomes were incident stroke, dementia, and their subtypes, alongside SVD markers. Hazard ratios (HRs) and odds ratios (ORs) were adjusted for age, sex, and ethnicity, and additional vascular risk factors. In Mendelian randomization (MR) analyses, ORs or risk ratios are reported for the association of each genetic score with clinical or MRI end points.

RESULTS

Among 502,383 participants (mean [SD] age, 56.5 [8.1] years; 54.4% female), there were 7,668 cases of all-cause dementia and 10,334 strokes. In longitudinal analyses, after controlling for cardiovascular risk factors, participants with insomnia, daytime napping, and dozing were associated with increased risk of any stroke (HR 1.05, 95% CI 1.01-1.11, p = 8.53 × 10-3; HR 1.09, 95% CI 1.05-1.14, p = 3.20 × 10-5; HR 1.19, 95% CI 1.08-1.32, p = 4.89 × 10-4, respectively). Almost all sleep measures were associated with dementia risk (all p < 0.001, except insomnia). Cross-sectional analyses identified associations between napping, snoring, and MRI markers of SVD (all p < 0.001). MR analyses supported a causal link between genetically predicted insomnia and increased stroke risk (OR 1.31, 95% CI 1.13-1.51, p = 0.00072), but not with dementia or SVD markers.

DISCUSSION

We found that multiple sleep measures predicted future risk of stroke and dementia, but these associations were attenuated after controlling for cardiovascular risk factors and were absent in MR analyses for Alzheimer disease. This suggests possible confounding or reverse causation, implying caution before proposing sleep disorder modifications for dementia treatment.

Relationship of Perivascular Space Markers With Incident Dementia in Cerebral Small Vessel Disease

BACKGROUND

Recent studies, using diffusion tensor image analysis along the perivascular space (DTI-ALPS), suggest impaired perivascular space (PVS) function in cerebral small vessel disease, but they were cross-sectional, making inferences on causality difficult. We determined associations between impaired PVS, measured using DTI-ALPS and PVS volume, and cognition and incident dementia.

METHODS

In patients with lacunar stroke and confluent white matter hyperintensities, without dementia at baseline, recruited prospectively in a single center, magnetic resonance imaging was performed annually for 3 years, and cognitive assessments, including global, memory, executive function, and processing speed, were performed annually for 5 years. We determined associations between DTI-ALPS and PVS volume with cerebral small vessel disease imaging markers (white matter hyperintensity volume, lacunes, and microbleeds) at baseline and with changes in imaging markers. We determined whether DTI-ALPS and PVS volume at baseline and change over 3 years predicted incident dementia. Analyses were controlled for conventional diffusion tensor image metrics using 2 markers (median mean diffusivity [MD] and peak width of skeletonized MD) and adjusted for age, sex, and vascular risk factors.

RESULTS

A total of 120 patients, mean age 70.0 years and 65.0% male, were included. DTI-ALPS declined over 3 years, while no change in PVS volume was found. Neither DTI-ALPS nor PVS volume was associated with cerebral small vessel disease imaging marker progression. Baseline DTI-ALPS was associated with changes in global cognition (β=0.142, P=0.032), executive function (β=0.287, P=0.027), and long-term memory (β=0.228, P=0.027). Higher DTI-ALPS at baseline predicted a lower risk of dementia (hazard ratio, 0.328 [0.183-0.588]; P<0.001), and this remained significant after including median MD as a covariate (hazard ratio, 0.290 [0.139-0.602]; P<0.001). Change in DTI-ALPS predicted dementia conversion (hazard ratio, 0.630 [0.428-0.964]; P=0.048), but when peak width of skeletonized MD and median MD were entered as covariates, the association was not significant. There was no association between baseline PVS volume, or PVS change over 3 years, and conversion to dementia.

CONCLUSIONS

DTI-ALPS predicts future dementia risk in patients with lacunar strokes and confluent white matter hyperintensities. However, the weakening of the association between change in DTI-ALPS and incident dementia after controlling for peak width of skeletonized MD and median MD suggests part of the signal may represent conventional diffusion tensor image metrics. PVS volume is not a predictor of future dementia risk.

Retinal structure and vessel density changes in cerebral small vessel disease

Background

Cerebral small vessel disease (CSVD) attaches people's attention in recent years. In this study, we aim to explore retinal structure and vessel density changes in CSVD patients.

Methods

We collected information on retinal metrics assessed by optical coherence tomography (OCT) and OCT angiography and CSVD characters. Logistic and liner regression was used to analyze the relationship between retinal metrics and CSVD.

Results

Vessel density of superficial retinal capillary plexus (SRCP), foveal density- 300 length (FD-300), radial peripapillary capillary (RPC) and thickness of retina were significantly lower in CSVD patients, the difference only existed in the thickness of retina after adjusted relevant risk factors (OR (95% CI): 0.954 (0.912, 0.997), p = 0.037). SRCP vessel density showed a significant downward trend with the increase of CSVD scores (β: -0.087, 95%CI: -0.166, -0.008, p = 0.031). SRCP and FD-300 were significantly lower in patients with lacunar infarctions and white matter hypertensions separately [OR (95% CI): 0.857 (0.736, 0.998), p = 0.047 and OR (95% CI): 0.636 (0.434, 0.932), p = 0.020, separately].

Conclusion

SRCP, FD-300 and thickness of retina were associated with the occurrence and severity of total CSVD scores and its different radiological manifestations. Exploring CSVD by observing alterations in retinal metrics has become an optional research direction in future.

Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis

Background

The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis.

Methods

Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model.

Results

After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia.

Discussion

Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.

Correlation between lenticulostriate arteries and white matter microstructure changes in patients with cerebral small vessel disease

To explore the correlation between the number of lenticulostriate arteries (LSAs) and the white matter features in cerebral small vessel diseases (CSVD) by 3T magnetic resonance imaging (MRI). Seventy-one patients with diagnoses of CSVD were prospectively enrolled to undergo 3T MRI examination, including high-resolution vascular wall imaging (VWI) and diffusion tensor imaging (DTI). The LSAs were observed and counted on VWI, and the patients were divided into three groups according to the LSA counts. The presence of white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), and enlarged perivascular spaces (EPVS) was assessed in each patient, and a composite CSVD score was calculated. Periventricular and deep white matter hyperintensity (PVWMH, DWMH) volume ratios were obtained based on automatic segmentation. Fractional anisotropy (FA) and mean diffusivity (MD) were processed by using tract-based spatial statistics (TBSS) analysis. These parameters were compared among the three groups. Correlations between the LSA counts and white matter features were also analyzed. There were differences in WMHs (P = 0.001), CMBs (P < 0.001), EPVS (P = 0.017), composite CSVD scores (P < 0.001), PVWMH volume ratios (P = 0.001), DWMH volume ratios (P < 0.001), global FA (P = 0.001), and global MD (P = 0.002) among the three groups. There were correlations between the LSA counts and WMHs (r = -0.45, P < 0.001), CMBs (r = -0.44, P < 0.001), EPVS (r = -0.28, P = 0.020), the composite CSVD score (r = -0.52, P < 0.001), DWMH volume ratio (r = -0.47, P < 0.001), PWMH volume ratio (r = -0.34, P = 0.004), global FA (r = 0.36, P = 0.002), and global MD (r = -0.33, P = 0.005). Diabetes mellitus (OR 3.36, 95% CI 1.06-10.63; P = 0.039) and increased DWMH volume ratios (OR 1.04, 95% CI 1.00-1.08; P = 0.048) were independent risk factors for a decrease in LSA counts. TBSS analysis showed differences among the three groups in global FA and MD after adjusting for age and sex (P < 0.05). The LSA counts was associated with white matter microstructure changes in CSVD and has the potential to represent the extent of subcortical microvascular damage in CSVD patients.

Vascular cognitive impairment - Past, present, and future challenges

Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.

Predicting incident dementia in cerebral small vessel disease: comparison of machine learning and traditional statistical models

Background

Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide, yet we lack a reliable model for predicting dementia in SVD. Past attempts largely relied on traditional statistical approaches. Here, we investigated whether machine learning (ML) methods improved prediction of incident dementia in SVD from baseline SVD-related features over traditional statistical methods.

Methods

We included three cohorts with varying SVD severity (RUN DMC, n = 503; SCANS, n = 121; HARMONISATION, n = 265). Baseline demographics, vascular risk factors, cognitive scores, and magnetic resonance imaging (MRI) features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranked by different models.

Results

We included 789 participants without missing data in the survival analysis, amongst whom 108 (13.7%) developed dementia during a median follow-up of 5.4 years. Excluding those censored before three years, we included 750 participants in the classification analysis, amongst whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only regularised Cox/logistic regression outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognition was highly predictive, and global cognition was the most important feature.

Conclusions

When using baseline SVD-related features to predict dementia in SVD, the ML survival or classification models we evaluated brought little improvement over traditional statistical approaches. The benefits of ML should be evaluated with caution, especially given limited sample size and features.

The association of diffusion tensor MRI measures of normal appearing white matter and cognition

Objective

Median and peak height of fractional anisotropy (FA) and mean diffusivity (MD) are diffusion tensor imaging (DTI) markers used to quantify white matter microstructure changes. We examine the association of DTI histogram-derived measures in global normal appearing white matter (NAWM) and cognitive decline in patients with normal cognition and cognitive impairment no dementia from a memory clinic in Singapore.

Methods

A total of 252 patients (mean age: 71.1 ± 7.6 years, 53.2% women) were included. All patients underwent clinical assessments, a brain MRI scan at baseline, and neuropsychological assessments annually for 2 years. DTI scans were processed to obtain MD and FA histogram-derived measures. The National Institute of Neurological Disorders and Stroke and the Canadian Stroke Network harmonization neuropsychological battery were used to assess cognitive function. Linear regression models with generalised estimating equation (GEE) and logistic regression models were used to examine the association between DTI histogram measures and cognitive decline.

Results

When compared to baseline, MD and FA measures at Year 2 were associated with an accelerated worsening in global cognition (all p for interaction <0.001; Year 0 vs 2, MD median: -0.29 (95%CI: -0.49, -0.09) vs -0.45 (95%CI: -0.65,-0.25); MD peak height: 0.22 (95%CI: 0.07, 0.37) vs 0.37 (95%CI: 0.21, 0.53); FA median: 0.11 (95%CI: -0.05, 0.26) vs 0.22 (95%CI: 0.07, 0.37); FA peak height: -0.14 (95%CI: -0.28, 0.00) vs -0.24 (95%CI: -0.38, -0.10);). Similar findings were observed for executive function and visuomotor speed while only MD measures predicted worsening in memory domain.

Interpretation

This study shows that DTI histogram measures are associated with accelerated cognitive decline suggesting the utility of DTI as a pre-clinical marker in predicting the worsening of cognition in clinical trials.

Elevated plasma sulfides are associated with cognitive dysfunction and brain atrophy in human Alzheimer's disease and related dementias

Emerging evidence indicates that vascular stress is an important contributor to the pathophysiology of Alzheimer's disease and related dementias (ADRD). Hydrogen sulfide (H2S) and its metabolites (acid-labile (e.g., iron-sulfur clusters) and bound (e.g., per-, poly-) sulfides) have been shown to modulate both vascular and neuronal homeostasis. We recently reported that elevated plasma sulfides were associated with cognitive dysfunction and measures of microvascular disease in ADRD. Here we extend our previous work to show associations between elevated sulfides and magnetic resonance-based metrics of brain atrophy and white matter integrity. Elevated bound sulfides were associated with decreased grey matter volume, while increased acid labile sulfides were associated with decreased white matter integrity and greater ventricular volume. These findings are consistent with alterations in sulfide metabolism in ADRD which may represent maladaptive responses to oxidative stress.

Reproducibility of regional structural and functional MRI networks in cerebral small vessel disease compared to age matched and stroke-free controls

Abnormalities in structural and functional MRI connectivity measures have been reported in cerebral small vessel disease (SVD). Previous research has shown that whole-brain structural connectivity was highly reproducible in SVD patients, while whole-brain functional connectivity showed low reproducibility. It remains unclear whether the lower reproducibility of functional networks reported in SVD is due to selective disruption of reproducibility in specific networks or is generalised in patients with SVD. In this case-control study 15 SVD and 10 age-matched control participants were imaged twice with diffusion tensor imaging and resting state fMRI. Structural and functional connectivity matrices were constructed from this data and the default mode, fronto-parietal, limbic, salience, somatomotor and visual networks were extracted and the average connectivity between connections calculated and used to determine their reproducibility. Regional structural networks were more reproducible than functional networks, all structural networks showed ICC values ≥0.64 (except the salience network in SVD). The functional networks showed greater reproducibility in the controls compared to SVD with ICC values >0.7 for control participants and <=0.5 for the SVD group. The default mode network showed the greatest reproducibility for both control and SVD groups. Reproducibility of functional networks was affected by disease status with lower reproducibility in SVD compared with controls.

Whole-brain diffusion tensor imaging predicts 6-month functional outcome in acute intracerebral haemorrhage

INTRODUCTION

Small vessel disease (SVD) causes most spontaneous intracerebral haemorrhage (ICH) and is associated with widespread microstructural brain tissue disruption, which can be quantified via diffusion tensor imaging (DTI) metrics: mean diffusivity (MD) and fractional anisotropy (FA). Little is known about the impact of whole-brain microstructural alterations after SVD-related ICH. We aimed to investigate: (1) association between whole-brain DTI metrics and functional outcome after ICH; and (2) predictive ability of these metrics compared to the pre-existing ICH score.

METHODS

Sixty-eight patients (38.2% lobar) were retrospectively included. We assessed whole-brain DTI metrics (obtained within 5 days after ICH) in cortical and deep grey matter and white matter. We used univariable logistic regression to assess the associations between DTI and clinical-radiological variables and poor outcome (modified Rankin Scale > 2). We determined the optimal predictive variables (via LASSO estimation) in: model 1 (DTI variables only), model 2 (DTI plus non-DTI variables), model 3 (DTI plus ICH score). Optimism-adjusted C-statistics were calculated for each model and compared (likelihood ratio test) against the ICH score.

RESULTS

Deep grey matter MD (OR 1.04 [95% CI 1.01-1.07], p = 0.010) and white matter MD (OR 1.11 [95% CI 1.01-1.23], p = 0.044) were associated (univariate analysis) with poor outcome. Discrimination values for model 1 (0.67 [95% CI 0.52-0.83]), model 2 (0.71 [95% CI 0.57-0.85) and model 3 (0.66 [95% CI 0.52-0.82]) were all significantly higher than the ICH score (0.62 [95% CI 0.49-0.75]).

CONCLUSION

Our exploratory study suggests that whole-brain microstructural disruption measured by DTI is associated with poor 6-month functional outcome after SVD-related ICH. Whole-brain DTI metrics performed better at predicting recovery than the existing ICH score.

Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain

Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.

Systemic Immune-Inflammation Index is Associated with Cerebral Small Vessel Disease Burden and Cognitive Impairment

OBJECTIVE

This study sought to explore the associations of the systemic immune-inflammation index (SII) with total cerebral small vessel disease (CSVD) burden and cognitive impairment.

METHODS

We enrolled 201 patients in the retrospective study with complete clinical and laboratory data. The SII was calculated as platelet count × neutrophil count/lymphocyte count. Cognitive function was evaluated by the Mini-Mental State Examination (MMSE). Total CSVD burden was assessed based on magnetic resonance imaging. We performed logistic regression models, Spearman correlation, and mediation analysis to evaluate the associations of SII with CSVD burden and cognitive impairment.

RESULTS

After adjustment for confounding factors in the multivariate binary logistic regression model, elevated SII (odds ratio [OR], 3.263; 95% confidence interval [CI], 1.577-6.752; P = 0.001) or severe CSVD burden (OR, 2.794; 95% CI, 1.342-5.817; P = 0.006) was significantly associated with the risk of cognitive impairment. Correlation analyses revealed that SII levels were negatively associated with MMSE scores (rs = -0.391, P < 0.001), and positively associated with the total CSVD burden score (rs = 0.361, P < 0.001). Moreover, SII was significantly related to the severity of the CSVD burden (OR, 2.674; 95% CI, 1.359-5.263; P = 0.004). The multivariable-adjusted odds ratios (95% CI) in highest tertile versus lowest tertile of SII were 8.947 (3.315-24.145) for cognitive impairment and 4.945 (2.063-11.854) for severe CSVD burden, respectively. The effect of higher SII on cognitive impairment development was partly mediated by severe CSVD burden.

CONCLUSION

Elevated SII is associated with severe CSVD burden and cognitive impairment. The mediating role of severe CSVD burden suggests that higher SII may contribute to cognitive impairment through aggravating CSVD burden.

Efficacy and safety of cilostazol in decreasing progression of cerebral white matter hyperintensities-A randomized controlled trial

Introduction

Cerebral small vessel disease (SVD) is an important cause of dementia that lacks effective treatment. We evaluated the efficacy and safety of cilostazol, an antiplatelet agent with potential neurovascular protective effects, in slowing the progression of white matter hyperintensities (WMHs) in stroke- and dementia-free subjects harboring confluent WMH on magnetic resonance imaging (MRI).

Methods

In this single-center, randomized, double-blind, placebo-controlled study, we randomized stroke- and dementia-free subjects with confluent WMHs to receive cilostazol or placebo for 2 years in a 1:1 ratio. The primary outcome was change in WMH volume over 2 years. Secondary outcomes were changes in brain volumes, lacunes, cerebral microbleeds, perivascular space, and alterations in white matter microstructural integrity, cognition, motor function, and mood.

Results

We recruited 120 subjects from October 27, 2014, to January 21, 2019. A total of 55 subjects in the cilostazol group and 54 subjects in the control group were included for intention-to-treat analysis. At 2-year follow-up, the changes in WMH volume were not statistically different between cilostazol treatment and placebo (0.3±1.0 mL vs -0.1±0.8 mL, p = 0.167). Secondary outcomes, bleeding and vascular events, were also not statistically different between the two groups.

Discussion

In this trial with stroke- and dementia-free subjects with confluent WMHs, cilostazol did not impact WMH progression but demonstrated an acceptable safety profile. Future studies should address the treatment effects of cilostazol on subjects at different clinical stages of SVD.

Superficial white matter microstructure affects processing speed in cerebral small vessel disease

White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.

Different Dynamic Nodal Properties Contribute to Cognitive Impairment in Patients with White Matter Hyperintensities

White matter hyperintensities (WMHs) are commonly observed in older adults and are associated with cognitive impairment. Although previous studies have found abnormal functional connectivities in patients with WMHs based on static functional magnetic resonance imaging (fMRI), the topological properties in the context of brain dynamics remain relatively unexplored. Herein, we explored disrupted dynamic topological properties of functional network connectivity in patients with WMHs and its relationship with cognitive impairment. We included 36 healthy controls (HC) and 104 patients with mild WMHs (n = 39), moderate WMHs (n = 37), and severe (n = 28) WMHs. The fMRI data of all participants were analyzed using Anatomical Automatic Labeling (AAL) and a sliding-window approach to generate dynamic functional connectivity matrics. Then, graph theory methods were applied to calculate the topological properties. Comprehensive neuropsychological scales were used to assess cognitive functions. Relationships between cognitive functions and abnormal dynamic topological properties were evaluated by Pearson’s correlation. We found that the patients with WMHs had higher temporal variability in regional properties, including betweenness centrality, nodal efficiencies, and nodal clustering coefficient. Furthermore, we found that the degree of centrality was related to executive function and memory, and the local coefficient correlated to executive function. Our results indicate that patients with WMHs have higher temporal variabilities in regional properties and are associated with executive and memory function.

Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE): A Review

Importance

Cerebral small vessel disease (SVD) causes a quarter of strokes and is the most common pathology underlying vascular cognitive impairment and dementia. An important step to developing new treatments is better trial methodology. Disease mechanisms in SVD differ from other stroke etiologies; therefore, treatments need to be evaluated in cohorts in which SVD has been well characterized. Furthermore, SVD itself can be caused by a number of different pathologies, the most common of which are arteriosclerosis and cerebral amyloid angiopathy. To date, there have been few sufficiently powered high-quality randomized clinical trials in SVD, and inconsistent trial methodology has made interpretation of some findings difficult.

Observations

To address these issues and develop guidelines for optimizing design of clinical trials in SVD, the Framework for Clinical Trials in Cerebral Small Vessel Disease (FINESSE) was created under the auspices of the International Society of Vascular Behavioral and Cognitive Disorders. Experts in relevant aspects of SVD trial methodology were convened, and a structured Delphi consensus process was used to develop recommendations. Areas in which recommendations were developed included optimal choice of study populations, choice of clinical end points, use of brain imaging as a surrogate outcome measure, use of circulating biomarkers for participant selection and as surrogate markers, novel trial designs, and prioritization of therapeutic agents using genetic data via Mendelian randomization.

Conclusions and Relevance

The FINESSE provides recommendations for trial design in SVD for which there are currently few effective treatments. However, new insights into understanding disease pathogenesis, particularly from recent genetic studies, provide novel pathways that could be therapeutically targeted. In addition, whether other currently available cardiovascular interventions are specifically effective in SVD, as opposed to other subtypes of stroke, remains uncertain. FINESSE provides a framework for design of trials examining such therapeutic approaches.

Association of Blood Pressure Lowering Intensity With White Matter Network Integrity in Patients With Cerebral Small Vessel Disease

BACKGROUND AND OBJECTIVES

Diffusion tensor imaging (DTI) networks integrate damage from a variety of pathologic processes in cerebral small vessel disease (SVD) and may be a sensitive marker to detect treatment effects. We determined whether brain network analysis could detect treatment effects in the PRESERVE trial data set, in which intensive vs standard blood pressure (BP) lowering was compared. The primary end point of DTI had not shown treatment differences.

METHODS

Participants with lacunar stroke were randomized to standard (systolic 130-140 mm Hg) or intensive (systolic ≤ 125 mm Hg) BP lowering and followed for 2 years with MRI at baseline and at 2 years. Graph theory-based metrics were derived from DTI data to produce a measure of network integrity weighted global efficiency and compared with individual MRI markers of DTI, brain volume, and white matter hyperintensities.

RESULTS

Data were available in 82 subjects: standard n = 40 (mean age 66.3 ± 1.5 years) and intensive n = 42 (mean age 69.6 ± 1.0 years). The mean (SD) systolic BP was reduced by 13(14) and 23(23) mm Hg in the standard and intensive groups, respectively (p < 0.001 between groups). Significant differences in diffusion network metrics were found, with improved network integrity (weighted global efficiency, p = 0.002) seen with intensive BP lowering. In contrast, there were no significant differences in individual MRI markers including DTI histogram metrics, brain volume, or white matter hyperintensities.

DISCUSSION

Brain network analysis may be a sensitive surrogate marker in trials in SVD. This work suggests that measures of brain network efficiency may be more sensitive to the effects of BP control treatment than conventional DTI metrics.

TRIAL REGISTRATION INFORMATION

The trial is registered with the ISRCTN Registry (ISRCTN37694103; doi.org/10.1186/ISRCTN37694103) and the NIHR Clinical Research Network (CRN 10962; public-odp.nihr.ac.uk/QvAJAXZfc/opendoc.htm?document=crncc_users%5Cfind%20a%20clinical%20research%20study.qvw&lang=en-US&host=QVS%40crn-prod-odp-pu&anonymous=true).

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that intensive BP lowering in patients with SVD results in improved brain network function when assessed by DTI-based brain network metrics.

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

OBJECTIVE

Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small vessel disease (SVD), but methods and results have varied across studies. This work aims to assess the impact of mask selection for extracting histogram-based metrics of fractional anisotropy (FA) and mean diffusivity (MD) on their sensitivity as SVD biomarkers.

METHODS

DTI data were collected from 17 SVD patients and 12 healthy controls. FA and MD maps were estimated; from these, histograms were computed on two whole-brain white-matter masks: normal-appearing white-matter (NAWM) and mean FA tract skeleton (TBSS). Histogram-based metrics (median, peak height, peak width, peak value) were extracted from the FA and MD maps. These were compared between groups and correlated with the patients' cognitive scores (executive function and processing speed).

RESULTS

White-matter mask selection significantly impacted FA and MD histogram metrics. In particular, significant interactions were found between Mask and Group for FA peak height (p = 0.027), MD Median (p = 0.035) and MD peak width (p = 0.047); indicating that the mask used affected their ability to discriminate between groups. In fact, MD peak width showed a significant 8.8% increase in patients when using TBSS (p = 0.037), but not when using NAWM (p = 0.69). Moreover, the mask may have an effect on the correlations with cognitive measures. Nevertheless, MD peak width (TBSS: r = - 0.75, NAWM: r = - 0.71) and MD peak height (TBSS: r = 0.65, NAWM: r = 0.62) remained significantly correlated with executive function, regardless of the mask.

CONCLUSION

The impact of the processing methodology, in particular the choice of white-matter mask, highlights the need for standardized MRI data-processing pipelines.

Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury

In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R² of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R² = 0.26 and R² = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R² = 0.49 and R² = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.

Brain-age is associated with progression to dementia in memory clinic patients

BACKGROUND

Biomarkers for the early detection of dementia risk hold promise for better disease monitoring and targeted interventions. However, most biomarker studies, particularly in neuroimaging, have analysed artificially 'clean' research groups, free from comorbidities, erroneous referrals, contraindications and from a narrow sociodemographic pool. Such biases mean that neuroimaging samples are often unrepresentative of the target population for dementia risk (e.g., people referred to a memory clinic), limiting the generalisation of these studies to real-world clinical settings. To facilitate better translation from research to the clinic, datasets that are more representative of dementia patient groups are warranted.

METHODS

We analysed T1-weighted MRI scans from a real-world setting of patients referred to UK memory clinic services (n = 1140; 60.2 % female and mean [SD] age of 70.0[10.8] years) to derive 'brain-age'. Brain-age is an index of age-related brain health based on quantitative analysis of structural neuroimaging, largely reflecting brain atrophy. Brain-predicted age difference (brain-PAD) was calculated as brain-age minus chronological age. We determined which patients went on to develop dementia between three months and 7.8 years after neuroimaging assessment (n = 476) using linkage to electronic health records.

RESULTS

Survival analysis, using Cox regression, indicated a 3 % increased risk of dementia per brain-PAD year (hazard ratio [95 % CI] = 1.03 [1.02,1.04], p < 0.0001), adjusted for baseline age, age2, sex, Mini Mental State Examination (MMSE) score and normalised brain volume. In sensitivity analyses, brain-PAD remained significant when time-to-dementia was at least 3 years (hazard ratio [95 % CI] = 1.06 [1.02, 1.09], p = 0.0006), or when baseline MMSE score ≥ 27 (hazard ratio [95 % CI] = 1.03 [1.01, 1.05], p = 0.0006).

CONCLUSIONS

Memory clinic patients with older-appearing brains are more likely to receive a subsequent dementia diagnosis. Potentially, brain-age could aid decision-making during initial memory clinic assessment to improve early detection of dementia. Even when neuroimaging assessment was more than 3 years prior to diagnosis and when cognitive functioning was not clearly impaired, brain-age still proved informative. These real-world results support the use of quantitative neuroimaging biomarkers like brain-age in memory clinics.

Contrast agent-free state-of-the-art magnetic resonance imaging on cerebral small vessel disease - Part 2: Diffusion tensor imaging and functional magnetic resonance imaging

Cerebral small vessel disease (cSVD) has been widely studied using conventional magnetic resonance imaging (MRI) methods, although the association between MRI findings and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast agent-free, state-of-the-art MRI techniques, particularly diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), to understand brain damage and structural and functional connectivity impairment related to cSVD. We performed a review following the PICOS worksheet and Search Strategy, including 152 original papers in English, published from 2000 to 2022. For each MRI method, we extracted information about their contributions regarding the origins, pathology, markers, and clinical outcomes in cSVD. In general, DTI studies have shown that changes in mean, radial, and axial diffusivity measures are related to the presence of cSVD. In addition to the classical deficit in executive functions and processing speed, fMRI studies indicate connectivity dysfunctions in other domains, such as sensorimotor, memory, and attention. Neuroimaging metrics have been correlated with the diagnosis, prognosis, and rehabilitation of patients with cSVD. In short, the application of contrast agent-free, state-of-the-art MRI techniques has provided a complete picture of cSVD markers and tools to explore questions that have not yet been clarified about this clinical condition. Longitudinal studies are desirable to look for causal relationships between image biomarkers and clinical outcomes.

Metabolomic profiling in small vessel disease identifies multiple associations with disease severity

Cerebral small vessel disease is a major cause of vascular cognitive impairment and dementia. There are few treatments, largely reflecting limited understanding of the underlying pathophysiology. Metabolomics can be used to identify novel risk factors to better understand pathogenesis and to predict disease progression and severity. We analysed data from 624 patients with symptomatic cerebral small vessel disease from two prospective cohort studies. Serum samples were collected at baseline and patients underwent MRI scans and cognitive testing at regular intervals with up to 14 years of follow-up. Using ultra-performance liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy, we obtained metabolic and lipidomic profiles from 369 annotated metabolites and 54 764 unannotated features and examined their association with respect to disease severity, assessed using MRI small vessel disease markers, cognition and future risk of all-cause dementia. Our analysis identified 28 metabolites that were significantly associated with small vessel disease imaging markers and cognition. Decreased levels of multiple glycerophospholipids and sphingolipids were associated with increased small vessel disease load as evidenced by higher white matter hyperintensity volume, lower mean diffusivity normalized peak height, greater brain atrophy and impaired cognition. Higher levels of creatine, FA(18:2(OH)) and SM(d18:2/24:1) were associated with increased lacune count, higher white matter hyperintensity volume and impaired cognition. Lower baseline levels of carnitines and creatinine were associated with higher annualized change in peak width of skeletonized mean diffusivity, and 25 metabolites, including lipoprotein subclasses, amino acids and xenobiotics, were associated with future dementia incidence. Our results show multiple distinct metabolic signatures that are associated with imaging markers of small vessel disease, cognition and conversion to dementia. Further research should assess causality and the use of metabolomic screening to improve the ability to predict future disease severity and dementia risk in small vessel disease. The metabolomic profiles may also provide novel insights into disease pathogenesis and help identify novel treatment approaches.

In vivo myelin imaging and tissue microstructure in white matter hyperintensities and perilesional white matter

White matter hyperintensities negatively impact white matter structure and relate to cognitive decline in aging. Diffusion tensor imaging detects changes to white matter microstructure, both within the white matter hyperintensity and extending into surrounding (perilesional) normal-appearing white matter. However, diffusion tensor imaging markers are not specific to tissue components, complicating the interpretation of previous microstructural findings. Myelin water imaging is a novel imaging technique that provides specific markers of myelin content (myelin water fraction) and interstitial fluid (geometric mean T₂). Here we combined diffusion tensor imaging and myelin water imaging to examine tissue characteristics in white matter hyperintensities and perilesional white matter in 80 individuals (47 older adults and 33 individuals with chronic stroke). To measure perilesional normal-appearing white matter, white matter hyperintensity masks were dilated in 2 mm segments up to 10 mm in distance from the white matter hyperintensity. Fractional anisotropy, mean diffusivity, myelin water fraction, and geometric mean T₂ were extracted from white matter hyperintensities and perilesional white matter. We observed a spatial gradient of higher mean diffusivity and geometric mean T₂, and lower fractional anisotropy, in the white matter hyperintensity and perilesional white matter. In the chronic stroke group, myelin water fraction was reduced in the white matter hyperintensity but did not show a spatial gradient in perilesional white matter. Across the entire sample, white matter metrics within the white matter hyperintensity related to whole-brain white matter hyperintensity volume; with increasing white matter hyperintensity volume there was increased mean diffusivity and geometric mean T₂, and decreased myelin water fraction in the white matter hyperintensity. Normal-appearing white matter adjacent to white matter hyperintensities exhibits characteristics of a transitional stage between healthy white matter and white matter hyperintensities. This effect was observed in markers sensitive to interstitial fluid, but not in myelin water fraction, the specific marker of myelin concentration. Within the white matter hyperintensity, interstitial fluid was higher and myelin concentration was lower in individuals with more severe cerebrovascular disease. Our data suggests white matter hyperintensities have penumbra-like effects in perilesional white matter that specifically reflect increased interstitial fluid, with no changes to myelin concentration. In contrast, within the white matter hyperintensity there are varying levels of demyelination, which vary based on the severity of cerebrovascular disease. Diffusion tensor imaging and myelin imaging may be useful clinical markers to predict white matter hyperintensity formation, and to stage neuronal damage within white matter hyperintensities.

MINocyclinE to Reduce inflammation and blood brain barrier leakage in small Vessel diseAse (MINERVA) trial study protocol

Background:

Cerebral small vessel disease (SVD) is a common cause of stroke and cognitive impairment. Recent data has implicated neuroinflammation and increased blood-brain barrier (BBB) permeability in its pathogenesis, but whether such processes are causal and can be therapeutically modified is uncertain. In a rodent model of SVD, minocycline was associated with reduced white matter lesions, inflammation and BBB permeability.

Aims:

To determine whether blood-brain barrier permeability (measured using dynamic contrast-enhanced MRI) and microglial activation (measured by positron emission tomography using the radioligand 11C-PK11195) can be modified in SVD.

Design:

Phase II randomised double blind, placebo-controlled trial of minocycline 100 mg twice daily for 3 months in 44 participants with moderate to severe SVD defined as a clinical lacunar stroke and confluent white matter hyperintensities.

Outcomes:

Primary outcome measures are volume and intensity of focal increases of blood-brain barrier permeability and microglial activation determined using PET-MRI imaging. Secondary outcome measures include inflammatory biomarkers in serum, and change in conventional MRI markers and cognitive performance over 1 year follow up.

Discussion:

The MINERVA trial aims to test whether minocycline can influence novel pathological processes thought to be involved in SVD progression, and will provide insights into whether central nervous system inflammation in SVD can be therapeutically modulated.

Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease

Background

White matter hyperintensity (WMH) is one of the typical neuroimaging manifestations of cerebral small vessel disease (CSVD), and the WMH correlates closely to cognitive impairment (CI). CSVD patients with WMH own altered topological properties of brain functional network, which is a possible mechanism that leads to CI. This study aims to identify differences in the characteristics of some brain functional network among patients with different grades of WMH and estimates the correlations between these different brain functional network characteristics and cognitive assessment scores.

Methods

110 CSVD patients underwent 3.0 T Magnetic resonance imaging scans and neuropsychological cognitive assessments. WMH of each participant was graded on the basis of Fazekas grade scale and was divided into two groups: (A) WMH score of 1–2 points (n = 64), (B) WMH score of 3–6 points (n = 46). Topological indexes of brain functional network were analyzed using graph-theoretical method. T-test and Mann–Whitney U test was used to compare the differences in topological properties of brain functional network between groups. Partial correlation analysis was applied to explore the relationship between different topological properties of brain functional networks and overall cognitive function.

Results

Patients with high WMH scores exhibited decreased clustering coefficient values, global and local network efficiency along with increased shortest path length on whole brain level as well as decreased nodal efficiency in some brain regions on nodal level (p < 0.05). Nodal efficiency in the left lingual gyrus was significantly positively correlated with patients' total Montreal Cognitive Assessment (MoCA) scores (p < 0.05). No significant difference was found between two groups on the aspect of total MoCA and Mini-mental State Examination (MMSE) scores (p > 0.05).

Conclusion

Therefore, we come to conclusions that patients with high WMH scores showed less optimized small-world networks compared to patients with low WMH scores. Global and local network efficiency on the whole-brain level, as well as nodal efficiency in certain brain regions on the nodal level, can be viewed as markers to reflect the course of WMH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00769-7.

Impact of Small Vessel Disease Progression on Long-term Cognitive and Functional Changes After Stroke

Background and Objectives

The severity of white matter hyperintensities (WMH) at presentation with stroke is associated with poststroke dementia and dependency. However, WMH can decrease or increase after stroke; prediction of cognitive decline is imprecise; and there are few data assessing longitudinal interrelationships among changing WMH, cognition, and function after stroke, despite the clinical importance.

Methods

We recruited patients within 3 months of a minor ischemic stroke, defined as NIH Stroke Scale (NIHSS) score <8 and not expected to result in a modified Rankin Scale (mRS) score >2. Participants repeated MRI at 1 year and cognitive and mRS assessments at 1 and 3 years. We ran longitudinal mixed-effects models assessing change in Addenbrooke’s Cognitive Examination–Revised (ACE-R) and mRS scores. For mRS score, we assessed longitudinal WMH volumes (cube root; percentage intracranial volume [ICV]), adjusting for age, NIHSS score, ACE-R, stroke subtype, and time to assessment. For ACE-R score, we additionally adjusted for ICV, mRS, premorbid IQ, and vascular risk factors. We then used a multivariate model to jointly assess changing cognition/mRS score, adjusted for prognostic variables, using all available data.

Results

We recruited 264 patients; mean age was 66.9 (SD 11.8) years; 41.7% were female; and median mRS score was 1 (interquartile range 1–2). One year after stroke, normalized WMH volumes were associated more strongly with 1-year ACE-R score (β = −0.259, 95% CI −0.407 to −0.111 more WMH per 1-point ACE-R decrease, p = 0.001) compared to subacute WMH volumes and ACE-R score (β = 0.105, 95% CI −0.265 to 0.054, p = 0.195). Three-year mRS score was associated with 3-year ACE-R score (β = −0.272, 95% CI −0.429 to −0.115, p = 0.001). Combined change in baseline-1-year jointly assessed ACE-R/mRS scores was associated with fluctuating WMH volumes (F = 9.3, p = 0.03).

Discussion

After stroke, fluctuating WMH mean that 1-year, but not baseline, WMH volumes are associated strongly with contemporaneous cognitive scores. Covarying longitudinal decline in cognition and independence after stroke, central to dementia diagnosis, is associated with increasing WMH volumes.

Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions

Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.

Determining the OPTIMAL DTI analysis method for application in cerebral small vessel disease

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We were not able to identify a single optimal diffusion-weighted imaging analysis strategy across all 6 cohorts.

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Diffusion tensor imaging measures at baseline predicted dementia conversion in cerebral small vessel disease and mild cognitive impairment.

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Diffusion tensor imaging measures at baseline may be sensitive to differentiate between later vascular dementia vs Alzheimer’s disease dementia.

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Diffusion tensor imaging measures significantly changed over time in cohorts with cerebral small vessel disease and cohorts with mild cognitive impairment. Change in diffusion tensor imaging measures were only consistently associated with dementia conversion in severe SVD.

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The diffusion tensor imaging measures PSMD and DSEG required the lowest minimum sample sizes for a hypothetical clinical trial in patients with sporadic cerebral small vessel disease and mild cognitive impairment.

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.

Prediction of dementia using diffusion tensor MRI measures: the OPTIMAL collaboration

OBJECTIVES

It has been suggested that diffusion tensor imaging (DTI) measures sensitive to white matter (WM) damage may predict future dementia risk not only in cerebral small vessel disease (SVD), but also in mild cognitive impairment. To determine whether DTI measures were associated with cognition cross-sectionally and predicted future dementia risk across the full range of SVD severity, we established the International OPtimising mulTImodal MRI markers for use as surrogate markers in trials of Vascular Cognitive Impairment due to cerebrAl small vesseL disease collaboration which included six cohorts.

METHODS

Among the six cohorts, prospective data with dementia incidences were available for three cohorts. The associations between six different DTI measures and cognition or dementia conversion were tested. The additional contribution to prediction of other MRI markers of SVD was also determined.

RESULTS

The DTI measure mean diffusivity (MD) median correlated with cognition in all cohorts, demonstrating the contribution of WM damage to cognition. Adding MD median significantly improved the model fit compared to the clinical risk model alone and further increased in all single-centre SVD cohorts when adding conventional MRI measures. Baseline MD median predicted dementia conversion. In a study with severe SVD (SCANS) change in MD median also predicted dementia conversion. The area under the curve was best when employing a multimodal MRI model using both DTI measures and other MRI measures.

CONCLUSIONS

Our results support a central role for WM alterations in dementia pathogenesis in all cohorts. DTI measures such as MD median may be a useful clinical risk predictor. The contribution of other MRI markers varied according to disease severity.

Neopterin and kynurenic acid as predictors of stroke recurrence and mortality: a multicentre prospective cohort study on biomarkers of inflammation measured three months after ischemic stroke

BACKGROUND

Chronic low-grade inflammation is associated with both ischemic stroke and sedentary behaviour. The aim of this study was to investigate the predictive abilities of biomarkers of inflammation and immune modulation associated with sedentary behaviour for ischemic stroke recurrence and mortality in a stroke population.

METHODS

Patients admitted to hospital for acute stroke were recruited to the prospective multicentre cohort study, the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study, from May 2015 until March 2017. Patients with ischemic stroke, blood samples available from the three-month follow-up, and no stroke recurrence before the three-month follow-up were included. Serum was analysed for C-reactive protein (CRP) with high-sensitive technique, and plasma for interleukin-6 (IL-6), neopterin, pyridoxic acid ratio index (PAr-index: 4-pyridoxic acid: [pyrioxal+pyridoxal-5'-phosphate]) and kynurenic acid (KA). Ischemic stroke recurrence and death were identified by the Norwegian Stroke Registry and the Cause of Death Registry until 31 December 2018.

RESULTS

The study included 354 patients, 57% male, mean age 73 (SD 11) years, mean observation time 2.5 (SD 0.6) years, and median National Institute of Health Stroke Scale of 0 (IQR 1) at three months. CRP was associated with mortality (HR 1.40, CI 1.01, 1.96, p = 0.046), and neopterin was associated with the combined endpoint (recurrent ischemic stroke or death) (HR 1.52, CI 1.06, 2.20, p = 0.023), adjusted for age, sex, prior cerebrovascular disease, modified Rankin Scale, and creatinine. When adding neopterin and KA to the same model, KA was negatively associated (HR 0.57, CI 0.33, 0.97, p = 0.038), and neopterin was positively associated (HR 1.61, CI 1.02, 2.54, p = 0.040) with mortality. Patients with cardioembolic stroke at baseline had higher levels of inflammation at three months.

CONCLUSION

Neopterin might be a valuable prognostic biomarker in stroke patients. The use of KA as a measure of anti-inflammatory capacity should be investigated further.

TRIAL REGISTRATION

The study was registered at Clinicaltrials.gov ( NCT02650531 ). First posted on 08/01/2016.

Microglial activation and blood-brain barrier permeability in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a major cause of stroke and dementia. The underlying pathogenesis is poorly understood, but both neuroinflammation and increased blood-brain barrier permeability have been hypothesized to play a role, and preclinical studies suggest the two processes may be linked. We used PET magnetic resonance to simultaneously measure microglial activation using the translocator protein radioligand 11C-PK11195, and blood-brain barrier permeability using dynamic contrast enhanced MRI. A case control design was used with two disease groups with sporadic SVD (n = 20), monogenic SVD (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, CADASIL), and normal controls (n = 20) were studied. Hotspots of increased glial activation and blood-brain barrier permeability were identified as values greater than the 95th percentile of the distribution in controls. In sporadic SVD there was an increase in the volume of hotspots of both 11C-PK11195 binding (P = 0.003) and blood-brain barrier permeability (P = 0.007) in the normal appearing white matter, in addition to increased mean blood-brain barrier permeability (P < 0.001). In CADASIL no increase in blood-brain barrier permeability was detected; there was a non-significant trend to increased 11C-PK11195 binding (P = 0.073). Hotspots of 11C-PK11195 binding and blood-brain barrier permeability were not spatially related. A panel of 93 blood biomarkers relating to cardiovascular disease, inflammation and endothelial activation were measured in each participant; principal component analysis was performed and the first component related to blood-brain barrier permeability and microglial activation. Within the sporadic SVD group both hotspot and mean volume blood-brain barrier permeability values in the normal appearing white matter were associated with dimension 1 (β  =  0.829, P = 0.017, and β  =  0.976, P = 0.003, respectively). There was no association with 11C-PK11195 binding. No associations with blood markers were found in the CADASIL group. In conclusion, in sporadic SVD both microglial activation and increased blood-brain barrier permeability occur, but these are spatially distinct processes. No evidence of increased blood-brain barrier permeability was found in CADASIL.

Disrupted Structural Brain Connectome Is Related to Cognitive Impairment in Patients With Ischemic Leukoaraiosis

Ischemic leukoaraiosis (ILA) is related to cognitive impairment and vascular dementia in the elderly. One possible mechanism could be the disruption of white matter (WM) tracts and network function that connect distributed brain regions involved in cognition. The purpose of this study was to investigate the relationship between structural connectome and cognitive functions in ILA patients. A total of 89 patients with ILA (Fazekas score ≥ 3) and 90 healthy controls (HCs) underwent comprehensive neuropsychological examinations and diffusion tensor imaging scans. The tract-based spatial statistics approach was employed to investigate the WM integrity. Graph theoretical analysis was further applied to construct the topological architecture of the structural connectome in ILA patients. Partial correlation analysis was used to investigate the relationships between network measures and cognitive performances in the ILA group. Compared with HCs, the ILA patients showed widespread WM integrity disruptions. The ILA group displayed increased characteristic path length (L _p) and decreased global network efficiency at the level of the whole brain relative to HCs, and reduced nodal efficiencies, predominantly in the frontal-subcortical and limbic system regions. Furthermore, these structural connectomic alterations were associated with cognitive impairment in ILA patients. The association between WM changes (i.e., fractional anisotropy and mean diffusivity measures) and cognitive function was mediated by the structural connectivity measures (i.e., local network efficiency and L _p). In conclusion, cognitive impairment in ILA patients is related to microstructural disruption of multiple WM fibers and topological disorganization of structural networks, which have implications in understanding the relationship between ILA and the possible attendant cognitive impairment.

Emerging Concepts in Vascular Dementia: A Review

OBJECTIVE

Vascular dementia (VaD) is the second most common cause of dementia and a major health concern worldwide. A comprehensive review on VaD is warranted for better understanding and guidance for the practitioner. We provide an updated overview of the epidemiology, pathophysiological mechanisms, neuroimaging patterns as well as current diagnostic and therapeutic approaches.

MATERIALS AND METHODS

A narrative review of current literature in VaD was performed based on publications from the database of PubMed, Scopus and Google Scholar up to January, 2021.

RESULTS

VaD can be the result of ischemic or hemorrhagic tissue injury in a particular region of the brain which translates into clinically significant cognitive impairment. For example, a cerebral infarct in the speech area of the dominant hemisphere would translate into clinically significant impairment as would involvement of projection pathways such as the arcuate fasciculus. Specific involvement of the angular gyrus of the dominant hemisphere, with resultant Gerstman's syndrome, could have a pronounced effect on functional ability despite being termed a "minor stroke". Small vessel cerebrovascular disease can have a cumulate effect on cognitive function over time. It is unfortunately well recognized that "good" functional recovery in acute ischemic or haemorrhagic stroke, including subarachnoid haemorrhage, does not necessarily translate into good cognitive recovery. The victim may often be left unable to have gainful employment, drive a car safely or handle their affairs independently.

CONCLUSIONS

This review should serve as a compendium of updated information on VaD and provide guidance in terms of newer diagnostic and potential therapeutic approaches.

PRESERVE: Randomized Trial of Intensive Versus Standard Blood Pressure Control in Small Vessel Disease

[Figure: see text].

Structural Brain Magnetic Resonance Imaging to Rule Out Comorbid Pathology in the Assessment of Alzheimer's Disease Dementia: Findings from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) Study and Clinical Trials Over the Past 10 Years

BACKGROUND/OBJECTIVE

Structural brain magnetic resonance imaging (MRI) is not mandatory in Alzheimer's disease (AD) research or clinical guidelines. We aimed to explore the use of structural brain MRI in AD/mild cognitive impairment (MCI) trials over the past 10 years and determine the frequency with which inclusion of standardized structural MRI acquisitions detects comorbid vascular and non-vascular pathologies.

METHODS

We systematically searched ClinicalTrials.gov for AD clinical trials to determine their neuroimaging criteria and then used data from an AD/MCI cohort who underwent standardized MRI protocols, to determine type and incidence of clinically relevant comorbid pathologies.

RESULTS

Of 210 AD clinical trials, 105 (50%) included structural brain imaging in their eligibility criteria. Only 58 (27.6%) required MRI. 16,479 of 53,755 (30.7%) AD participants were in trials requiring MRI. In the observational AD/MCI cohort, 141 patients met clinical criteria; 22 (15.6%) had relevant MRI findings, of which 15 (10.6%) were exclusionary for the study.

DISCUSSION

In AD clinical trials over the last 10 years, over two-thirds of participants could have been enrolled without brain MRI and half without even a brain CT. In a study sample, relevant comorbid pathology was found in 15% of participants, despite careful screening. Standardized structural MRI should be incorporated into NIA-AA diagnostic guidelines (when available) and research frameworks routinely to reduce diagnostic heterogeneity.

Cognitive impairment in sporadic cerebral small vessel disease: A systematic review and meta-analysis

This paper is a proposal for an update on the characterization of cognitive impairments associated with sporadic cerebral small vessel disease (SVD). We pose a series of questions about the nature of SVD-related cognitive impairments and provide answers based on a comprehensive review and meta-analysis of published data from 69 studies. Although SVD is thought primarily to affect executive function and processing speed, we hypothesize that SVD affects all major domains of cognitive ability. We also identify low levels of education as a potentially modifiable risk factor for SVD-related cognitive impairment. Therefore, we propose the use of comprehensive cognitive assessments and the measurement of educational level both in clinics and research settings, and suggest several recommendations for future research.

Treatment of neuropsychiatric systemic lupus erythematosus: clinical challenges and future perspectives

Introduction: Neuropsychiatric (NP) involvement represents an emerging frontier in systemic lupus erythematosus (SLE), posing significant challenges due to its clinical diversity and obscure pathophysiology. The authors herein discuss selected aspects in the management of NPSLE based on existing literature and our experience, aiming to facilitate routine medical care.Areas covered: Research related to diagnosis, neuroimaging, treatment and outcome is discussed, focusing on data published in PubMed during the last 5 years. Selected translational studies of clinical relevance are included.Expert opinion: Identification of NPSLE patients who may benefit from appropriate treatment can be facilitated by attribution algorithms. Immunosuppressants are typically indicated in recurrent seizures, optic neuritis, myelopathy, psychosis and peripheral nerve disease, although a low threshold is recommended for cerebrovascular disease and other NP manifestations, especially when SLE is active. With the exception of stroke with positive antiphospholipid antibodies, anti-coagulation is rarely indicated in other syndromes. Refractory NPSLE can be treated with rituximab, whereas the role of other biologics remains unknown. Advances in the fields of biomarkers, neuroimaging for brain structural, perfusion or functional abnormalities, and design of novel compounds targeting not only systemic autoimmunity but also inflammatory and regenerative pathways within the nervous system, hold promise for optimizing NPSLE management.

Neurofilament light chain predicts future dementia risk in cerebral small vessel disease

OBJECTIVES

Serum neurofilament light chain (NfL) has been proposed as prognostic markers in neurogenerative disease. A cross-sectional study in cerebral small vessel disease (SVD) reported an association with cognition and disability. If NfL is to be used to predict outcome, studies are required to demonstrate baseline NfL predicts future dementia risk. Furthermore, if it is to be used as a surrogate marker in clinical trials, change in NfL over time periods typical of a clinical trial must be linked to clinical progression. In a longitudinal study of patients with lacunar stroke and confluent white matter hyperintensities, we determined whether both baseline, and change, in NfL levels were linked to changes in MRI markers, cognitive decline and dementia risk.

METHODS

Patients underwent MRI, cognitive testing and blood taking at baseline and annually for 3 years. Clinical and cognitive follow-up continued for 5 years.

RESULTS

NfL data were available for 113 subjects for baseline analysis, and 90 patients for the longitudinal analysis. Baseline NfL predicted cognitive decline (global cognition β=-0.335, SE=0.094, p=0.001) and risk of converting to dementia (HR=1.676 (95% CI 1.183 to 2.373), p=0.004). In contrast to imaging, there was no change in NfL values over the follow-up period.

CONCLUSIONS

Baseline NfL predicts changes in MRI markers, cognitive decline and dementia rate over a 5 years follow-up period in SVD, suggesting NfL may be a useful prognostic marker. However, change in NfL values was not detected, and therefore NfL may not be a useful surrogate marker in clinical trials in SVD.

White Matter Fractional Anisotropy Is a Superior Predictor for Cognitive Impairment Than Brain Volumes in Older Adults With Confluent White Matter Hyperintensities

Older patients with confluent white matter hyperintensities (WMHs) on magnetic resonance imaging have an increased risk for the onset of vascular cognitive impairment (VCI). This study investigates the predictive effects of the white matter (WM) fractional anisotropy (FA) and brain volumes on cognitive impairment for those with confluent WMHs. This study enrolled 77 participants with confluent WMHs (Fazekas grade 2 or 3), including 44 with VCI-no dementia (VCIND) and 33 with normal cognition (NC). The mean FA of 20 WM tracts was calculated to evaluate the global WM microstructural integrity, and major WM tracts were reconstructed using probabilistic tractography. Voxel-based morphometry was used to calculate brain volumes for the total gray matter (GM), the hippocampus, and the nucleus basalis of Meynert (NbM). All volumetric assays were corrected for total intracranial volume. All regression analyses were adjusted for age, gender, education, and apolipoprotein E (ApoE) gene ε4 status. Logistic regression analysis revealed that the mean FA value for global WM was the only independent risk factor for VCI (z score of FA: OR = 4.649, 95%CI 1.576-13.712, p = 0.005). The tract-specific FAs were not associated with the risk of cognitive impairment after controlling the mean FA for global WM. The mean FA value was significantly associated with scores of Mini-Mental State Examination (MMSE) and Auditory Verbal Learning Test. A lower FA was also associated with smaller volumes of total GM, hippocampus, and NbM. However, brain volumes were not found to be directly related to cognitive performances, except for an association between the hippocampal volume and MMSE. In conclusion, the mean FA for global WM microstructural integrity is a superior predictor for cognitive impairment than tract-specific FA and brain volumes in people with confluent WMHs.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter

PURPOSE

This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD).

METHODS

PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T₁ -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility.

RESULTS

Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600.

CONCLUSION

Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

Total Cerebral Small Vessel Disease Burden Is Related to Worse Performance on the Mini-Mental State Examination and Incident Dementia: A Prospective 5-Year Follow-Up

Individual cerebral small vessel disease (CSVD) may cause cognitive decline. However, the association between total burden of CSVD and cognitive deterioration in the general population remains unclear. We aimed to determine whether total CSVD score is associated with cognitive performance change and incident dementia in the general population. In the longitudinal population-based Taizhou Imaging Study, 556 participants free of neurological disorders underwent brain MRI and neuropsychological testing at baseline. A total of 456 participants were followed up for cognitive performance for a mean (standard deviation) of 4.6 (0.6) years. Total CSVD score (range 0-4) was calculated by assigning 1 point for the presence of each of the following markers: lacune, white matter hyperintensity, cerebral microbleed, and perivascular space. Beta regression was used to evaluate the association between total CSVD burden and MMSE score change. The association of prevalent CSVD with incident dementia was studied using Fisher's exact test. CSVDs were present in 262 individuals (47.1%). The total CSVD score was significantly associated with MMSE score decline (p = 0.001). Compared to those with no CSVD, participants with 4 CSVD markers had a steeper decline in MMSE score (β: -0.53, 95% CI: -0.86 to -0.21; p = 0.001). A total of 15 participants developed dementia during follow-up. The presence of more than three CSVD markers at baseline was associated with a significantly higher risk of dementia (p = 0.020). Total CSVD burden appears to be associated with MMSE score decline and incident dementia in a general population in China.

Pathophysiology, classification, and MRI parallels in microvascular disease of the heart and brain

Diagnostic imaging technology in vascular disease has long focused on large vessels and the pathologic processes that impact them. With improved diagnostic techniques, investigators are now able to uncover many underlying mechanisms and prognostic factors for microvascular disease. In the heart and brain, these pathologic entities include coronary microvascular disease and cerebral small vessel disease, both of which have significant impact on patients, causing angina, myocardial infarction, heart failure, stroke, and dementia. In the current paper, we will discuss parallels in pathophysiology, classification, and diagnostic modalities, with a focus on the role of magnetic resonance imaging in microvascular disease of the heart and brain. Novel approaches for streamlined imaging of the cardiac and central nervous systems including the use of intravascular contrast agents such as ferumoxytol are presented, and unmet research gaps in diagnostics are summarized.

Structural disconnectivity and the risk of dementia in the general population

Objective

The disconnectivity hypothesis postulates that partial loss of connecting white matter fibers between brain regions contributes to the development of dementia. Using diffusion MRI to quantify global and tract-specific white matter microstructural integrity, we tested this hypothesis in a longitudinal population-based study.

Methods

Global and tract-specific fractional anisotropy (FA) and mean diffusivity (MD) were obtained in 4,415 people without dementia (mean age 63.9 years, 55.0% women) from the prospective population-based Rotterdam Study with brain MRI between 2005 and 2011. We modeled the association of these diffusion measures with risk of dementia (follow-up until 2016) and with changes on repeated cognitive assessment after on average 5.4 years, adjusting for age, sex, education, macrostructural MRI markers, depressive symptoms, cardiovascular risk factors, and APOE genotype.

Results

During a median follow-up of 6.8 years, 101 participants had incident dementia, of whom 83 had clinical Alzheimer disease (AD). Lower global values of FA and higher values of MD were associated with an increased risk of dementia (adjusted hazard ratio [95% confidence interval (CI)] per SD increase for MD 1.79 [1.44–2.23] and FA 0.65 [0.52–0.80]). Similarly, lower global values of FA and higher values of MD related to more cognitive decline in people without dementia (difference in global cognition per SD increase in MD [95% CI] was −0.04 [−0.07 to −0.01]). Associations were most profound in the projection, association, and limbic system tracts.

Conclusions

Structural disconnectivity is associated with an increased risk of dementia and more pronounced cognitive decline in the general population.

Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants

Cerebral small vessel disease is a major cause of stroke and dementia, but its genetic basis is incompletely understood. We perform a genetic study of three MRI markers of the disease in UK Biobank imaging data and other sources: white matter hyperintensities (N = 42,310), fractional anisotropy (N = 17,663) and mean diffusivity (N = 17,467). Our aim is to better understand the disease pathophysiology. Across the three traits, we identify 31 loci, of which 21 were previously unreported. We perform a transcriptome-wide association study to identify associations with gene expression in relevant tissues, identifying 66 associated genes across the three traits. This genetic study provides insights into the understanding of the biological mechanisms underlying small vessel disease.

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.