Cerebral circulation - cognition and behavior最新文献

Introduction

White matter hyperintensities (WMH) are related to cognitive decline, particularly processing speed and executive functioning (EF). However, all cognitive domains might be affected. WMH can regress and this might have positive consequences for cognition, but the effects of WMH regression on cognition are unknown. This study aims to assess whether changes in global cognition, processing speed and EF are related to progressing and regressing WMH.

Methods

We recruited mild, non-disabling, ischemic stroke patients with sporadic small vessel disease. They underwent MRI and cognitive assessment within 3 months post-stroke and 1 year later. Cognitive assessment included MoCA (global cognition; score 0-30), trail making test (TMT) A (processing speed; seconds) and TMT B (EF; seconds). WMH volumes are reported as % intracranial volume (ICV). WMH volume change (%ICV) is defined as WMH volume difference between baseline and 1 year. We calculated quintiles (Q) of WMH volume change to group volume change. We performed three linear mixed models, with MoCA, TMT-A and B as outcomes, to assess relationships over time with quintile of WMH volume change. Predictors in addition to quintile are age, sex, premorbid intelligence (NART), stroke severity (NIHSS). Extra predictor for TMT-B analysis is the TMT-A time to correct for speed.

Results

202 participants had WMH change volumes available (mean age: 66.03 years [SD=11.15), 33% female]. Q1 reflect greatest WMH volume reduction and Q5 greatest volume increase, while Q3 is lowest overall volume change (Figure 1). MoCA score increased over time (Figure 2). Older age (std. B [std. 95%CI]: -0.303 [-0.400, -0.206]), worse NART (-0.463 [-0.557, -0.368]), increasing NIHSS (-0.208 [-0.286, -0.130]) and more WMH increase (Q5; -0.513 [-0.814, -0.211]) predict lower MoCA. Older age (0.257 [0.135, 0.380]), worse NART (0.259 [0.139, 0.378]) and higher NIHSS (0.152 [0.078, 0.226]) predict worse TMT-A time (Figure 3A). Older age (0.224 [0.132, 0.316]), worse NART (0.295 [0.207, 0.383]), worse TMT-A (0.469 [0.384, 0.555]), worsening WMH volume (Q2; 0.382 [0.115, 0.0.649] and Q5; 0.425 [0.150, 0.699]) predict worse TMT-B time (Figure 3B).

Discussion

More WMH progression independently predicts worse global cognition and EF but not processing speed. WMH regression (Q1) was not related to worsening or improving global cognition, processing speed and EF. Cognitive consequences of WMH regression might be comparable to stable WMH (Q3).

Introduction

Mid-life cardiovascular and late-life structural cerebrovascular pathology play a major role in accelerating cognitive decline in normal aging and dementia. Cerebral blood flow (CBF) may be a critical intermediate biomarker of future cognitive decline, however our understanding of normal CBF variability and its relation with mid-life cardiovascular and late-life structural cerebrovascular parameters is limited. The MRC National Survey of Health and Development neuroimaging sub-study ‘Insight 46’ provides a unique opportunity to study the effects of cardiovascular and cerebrovascular health on CBF in a cohort with a fixed chronological age. We explored associations of life-long cardiovascular parameters and WMH volume with ASL MRI CBF in this cognitively healthy population-based sample.

Methods

3D T1-weighted, FLAIR, and pseudo-continuous ASL (labeling duration = 1800ms; post-labelling delay = 1800ms) data (3T) were acquired in 295 participants (Table 1). Clinical data included systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), pulse pressure (PP), and the Framingham Risk Score (FRS). MRI scans were processed with ExploreASL. Grey matter (GM), white matter (WM) and white matter hyperintensity (WMH) volume were segmented from the T1w and FLAIR images. ASL- derived partial volume-corrected CBF and the spatial coefficient of variation (sCoV) were obtained in whole-brain GM and WM. Linear regression models examined associations between life-long cardiovascular health (ages 43, 53, 60-64, 69, and 69-71 years), ASL metrics (at 69-71 years), and WMH load (at 69-71 years). Models were corrected for sex, and sex interactions were tested if applicable.

Results

While mid-life cardiovascular parameters were associated with late-life sCoV and CBF, the strongest associations were seen at 69-71 years. At this time point, higher SBP and MAP were associated with lower GM CBF in men (1A-B, β=-0.16,-0.28) and relatively stable GM CBF in women (1A-B, β=0.02,-0.02); higher DBP was associated with lower GM CBF (β=-0.18). Higher WMH volume was related to higher GM and WM CBF (2A-B, β=2.04,1.45), and lower GM and WM sCoV (2C-D, β=-0.04,0.05).

Discussion

These sex-dependent associations encourage further investigation into the potential mediatory role of CBF between mid-life cardiovascular and late-life structural decline of cerebrovascular health, leading to cognitivexxxxx

Objectives

Cardiovascular conditions like hypercholesterolemia and hypertension increase the risk for Alzheimer's Disease (AD). Thus, certain AD risk profiles may benefit from optimized multi- medication therapies including for example cholesterol-lowering and antihypertensive drugs. We previously showed that the drugs most frequently prescribed in older patients in Sweden are compounds from cardiovascular drug classes such as antithrombotic agents, lipid modifying agents, ACE inhibitors, selective calcium channel blockers and are used in combination. In this study we aim to investigate the effects of two multiple-drug regimens in the APPNL-G-F knock-in (APP KI) mouse model of AD and explore whether this could affect the disease progression at early stages.

Methods

APP KI mice were fed for 2 months with a control or multi-medication diet (including anti- hypertensive, lipid lowering and psychotropic drugs, in two different combinations) based on the most used medications by older adults in Sweden. In addition to the multi-medication regimens, mice were also tested for specific monotherapies from the compounds used in combination. Animals were assessed for locomotion, cognition, and anxiety-like behavior. Brain tissues were collected for molecular biology experiments, while blood was analyzed by GC/LC-MS to measure serum metabolomics.

Results

We found that multi-medication therapies in AD mice differentially affected essential functions such as locomotion, and distinct types of memory. APP KI male mice treated with combination#1 exhibited a rescue of cognitive deficits compared to controls, which we didn´t observe in females. These findings positively correlate with the significant reduction of cortical Aβ plaques observed in treated APP KI males. Conversely, combination#2 didn´t exert a positive effect on cognitive abilities. Monotherapy treatments induced different effects on functional and cognitive outcomes depending on sex, partially explaining the results observed in polypharmacy groups.

Discussion

We show that multi-medication therapies comprising cardiovascular drugs like statins and β-blockers or ACE inhibitors may impact the progression of AD pathophysiology depending on sex and multiple-drug combination, suggesting some synergic effects deriving from the multi- medication rather than the administration of single compounds. The outcomes from this study can provide knowledge for designing more individualized therapies in aging and AD, taking sex and gender into special consideration.

Introduction

Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and leukoencephalopathy (CADASIL) is the most frequent genetic cerebrovascular disease. The clinical aspects of the disease in relation to the various types of lesions on Magnetic Resonance Imaging (MRI) vary widely within families but also between multiple cohorts reported worldwide. Many limitations prevent the comparison of imaging data obtained using different scanners and sequences, in different patients cohorts. We aimed to develop and validate a practical tool to inventory quickly the most important MRI features in CADASIL for comparing imaging data across different populations.

Methods

The Inventory Tool (CADA-MRIT) was designed by consensus after repeated expert meetings. It consisted of eleven imaging items for evaluating periventricular, deep, and superficial white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMB), centrum semiovale and basal ganglia dilated perivascular spaces (PVS), superficial and deep atrophy, large infarcts, and macrobleeds. The reliability, clinical relevance and time-effectiveness of the CADA-MRIT were assessed using data from three independent cohorts of patients.

Results

Imaging data from 671 CADASIL patients (440 from France, 119 from Germany and 112 from Taiwan) were analyzed. Their mean age was 53.4 ± 12.2 years, 54.5% were women, 56.2% had stroke, and 31.1% had migraine with aura. Any lacune was present in at least 70% of individuals, while CMB were present in 83% of patients from the Asian cohort and in only 35% European patients. The CADA-MRIT scores obtained for WMH, CMB and PVS were comparable regardless of the scanner or sequence used (weighted κ > 0.60). Intrarater and interrater agreements were good to very good (weighted κ > 0.60). Global WMH and atrophy scores strongly correlated with the exact volumetric quantification of WMH or brain parenchymal fraction (Pearson r > 0.60). Different imaging scores were significantly associated with the main clinical manifestations of the disease. The time for evaluating one patient was around 2-3 minutes.

Discussion

The CADA-MRIT is an easy-to-use instrument for analyzing and comparing the most frequent MRI lesions of CADASIL across different populations. This tool is reliable. It can be used with different imaging sequences or scanners.

Introduction

Cerebral small vessel disease (SVD) is a common co-pathology in elderly and individuals with dementia. Neuroimaging markers of SVD include white matter hyperintensities (WMH) and MRI-visible perivascular spaces (PVS). However, the mechanisms underlying changes in these markers over time, whether ischemic or beta-amyloid (Aβ)-related, remain elusive. Here, we evaluated the effects of microstructural injury in the normal-appearing white matter and Aβ in the cerebral cortex on the progression of WMH and PVS over three years.

Methods

Data was obtained from two independent cohorts: (i) TRIAD, comprising cognitively normal, MCI, and AD dementia participants (baseline: N=199, follow-up year 1 and 2: N=102 and 62); and (ii) MITNEC-C6, comprising “real-world” patients with mixed dementia and moderate-to- severe WMH burden (baseline: N=52, 2 years follow-up: N=25). We quantified global WMH and PVS volumes from FLAIR and T1w MRI. At baseline, we examined associations between these volumes and diffusion MRI-derived free water. Longitudinally, we employed linear [mixed-effect] models to investigate the relation of WMH or PVS volume changes over time with baseline free water, using cortical Aβ-PET, age, sex, and APOE-ε4 as covariates.

Results

In TRIAD and MITNEC-C6 respectively, mean ages were 72±6 and 77±8 y, 60% and 42% were female, and 41% and 48% were Aβ-positive. At baseline, higher free water in normal- appearing white matter was associated with higher WMH volume (β_TRIAD=+0.34±0.06, P_TRIAD<0.001 and β_MITNEC=+0.31±0.14, P_MITNEC=0.03) as well as total PVS volume (β_TRIAD=+0.53±0.06, P_TRIAD<0.001 and β_MITNEC=+0.30±0.13, P_MITNEC=0.03).Longitudinally, faster WMH progression was predicted by higher baseline free water in normal- appearing white matter (P_TRIAD<0.001 and P_MITNEC=0.01) in Aβ-positive but not negative individuals (Figure 1). Conversely, higher free water in normal-appearing white matter was moderately related to slower PVS progression (P_TRIAD=0.03 and P_MITNEC=0.004).

Discussion

Our findings support a key role for free water and Aβ in predicting the progression of volumetric MRI-based markers of SVD. Future investigations will explore the spatial relationships involved. Additionally, further validation of our longitudinal segmentation tools for PVS is necessary to support a potential link between free water and PVS temporal dynamics.

Introduction

Cardiometabolic multimorbidity (CMM) and depression are risk factors for cognitive decline and dementia. We investigated the independent and synergistic associations of CMM and depression on longitudinal cognitive deterioration in two Asian cohorts of elderly at varying cognitive status with differed underlying neuropathology.

Methods

Eligible memory clinic (Harmonization) and stroke patients (Coast) aged ≥50 completed cognitive and clinical assessments at baseline and up to 5 follow-up visits in 6 years. Cardiometabolic diseases (CMDs), including hypertension, hyperlipidemia, diabetes mellitus, stroke and other cardiovascular diseases were assessed. Presence of CMM was defined as having two or more CMDs. Depression was defined by Geriatric Depression Scale (GDS) of ≥5. Biomarkers including cerebral beta-amyloid, cerebrovascular disease and ApoE genotype, were assessed by MRI and laboratory tests. Cognitive outcomes included cognitive trajectory patterns identified by the group-based trajectory model (GBTM) and the rate of change of global cognitive z-score. Multivariable logistic models and mixed models were conducted to assess the associations of CMM(+) and depression(+) with longitudinal cognitive decline. Stratification analysis by demographics and biomarkers was conducted.

Results

Among a total of 809 (Harmonization: 586, Coast: 223) participants, three cognitive trajectories were identified by the GBTM in each cohort. CMM(+) was independently associated with longitudinal cognitive decline. There was a significant interaction among CMM, depression and time with longitudinal cognition in both cohorts (ps of β(CMM*depression*time) < 0.05). Compared with reference group, CMM(+) and depression(+) were associated with patterns of rapid decline (OR=3.58, =95%CI=(1.64,7.81)) and slow decline (OR=3.32, 95%CI=(1.20,9.19)) in Harmonization, and decline/low stable (OR=4.01, 95%CI=(1.03,15.50)) in Coast. Participants with CMM(+) and depression(+) had a 0.24 (95%CI= -0.49, -0.01) and 0.13 (95%CI=-0.24,-0.01) units decline per year on global cognitive z-scores in Harmonization and Coast, respectively, compared with those with CMM(-) and depression(-). The combined effect between CMM and depression was more pronounced among older and male participants, as well as APOEε4 carriers.

Discussion

Our study demonstrated a synergistic effect between CMM and depression on longitudinal cognitive decline among elderly with differed underlying neuropathology. Targeting both CMM and depression in preventing cognitive decline may lead to greater effectiveness.

Introduction

Lesion-symptom mapping (LSM) is used to capture the impact of lesions on cognitive performance while accounting for their location in the brain and is highly relevant for vascular cognitive impairment [1], [2]. Current LSM methods only consider a single lesion type and single cognitive score at a time. Neural networks (NNs) allow for multiple inputs (lesions) and outputs (cognitive domains), which might take interrelations of vascular lesions and cognitive subscores into account. Explainable AI (XAI) can be used to compute attribution maps reflecting which image locations are deemed important for a NN, even at an individual level. We explore the feasibility of NNs and XAI for LSM by comparing two NNs with current gold standard, support vector regression (SVR) [3], [4].

Methods

White matter hyperintensity segmentations from 821 patients in the TRACE-VCI dataset were used to develop a simulation study similar to [3]. Three regions of interest (ROIs) were defined within the lesion prevalence map. Lesion volume fractions within each ROI were calculated and summed to create an artificial cognitive score with a known source location. A linear NN, a convolutional NN (CNN), and SVR were used to predict the artificial scores and determine responsible ROI locations. Predictive performance was quantified using the coefficient of determination, while ROI identification was evaluated using the precision-recall metric based on the attribution maps of each method: SVR's β-map, the linear NN's weight map, and the CNN XAI saliency map. The XAI saliency map was computed by occluding parts of the image: the predicted outcome changes considerably for relevant locations and remains unchanged when background is occluded [5].

Results

SVR and both NNs have similar predictive performance, all reaching R^2>0.9 (Fig. 1). However, attribution maps (Fig. 2) show differences in ROI location determination, which is reflected by the precision-recall curves. The curves in Fig. 3 show that SVR has overall better precision and recall (AUC=0.761), followed by the CNN (AUC=0.582) and the linear NN (AUC=0.203).

Discussion

In this first exploration, the CNN with XAI did not outperform SVR, but it proved able to detect relevant lesion locations, thereby showing potential for LSM.

Introduction

Advanced age is associated with poorer prognosis after stroke. Machine learning based on brain imaging can be used to estimate the age of a patient and compute the difference to chronological age; the brain age gap (BAG). Higher BAG (an older appearing brain) has been reported in a range of clinical conditions and is associated with risk of dementia and cognitive decline following stroke, but the associations and predictive value of brain age prediction for post- stroke decline in specific cognitive domains remain unknown. To this end, using longitudinal data after stroke we tested the hypothesis that higher BAG during the acute stroke phase is associated with decline in specific cognitive domains 3 and 18 months after stroke.

Methods

272 stroke survivors (age = 71.1 (11.2), women = 43.4%) were included from the ‘Norwegian Cognitive Impairment After Stroke (Nor-COAST) study. Clinical and MRI data was collected in the acute phase of the stroke and cognitive assessment of attention, executive function, language, and memory was collected at 3 and 18-months follow-up. FreeSurfer anatomical segmentation machine learning was used to predict age of each patient. Z-scores of the cognitive data normalized by mean and standard deviation of published normative data were calculated.

Results

Mean (SD) z-scores at 3 and 18 months respectively were, for attention; -.8 (2.5) and -.4 (2.2), for executive function; -.6 (1.4) and -4. (1.4), for language; -.6 (1.2) and -.3 (1.5), and for memory; -.8 (1.4) and -.8 (1.2). Generalised linear model analyses revealed significant associations between baseline BAG and all of the cognitive domains at both 3 and 18 months, with low/moderate effect sizes (see figure).

Discussion

We found that higher BAG during the acute phase of a stroke is associated with poorer 3- and 18-month cognitive outcome in attention, executive function, language, and memory.

These findings suggest that BAG may be used as a predictive marker for decline in specific cognitive domains after stroke.

Introduction

A connection exists between peripheral disease such as cardiovascular (CV) disease and the risk for dementia potentially through shared vascular biological underpinnings. Large-scale proteomic phenotyping can be useful to establish associations and elucidate mechanisms and etiology of disease. We applied previously developed and validated plasma proteomic tests that predict domains of CV risks to characterize the association between CV risk profiles and 20-year dementia risk in an observational cohort.

Methods

We used data and samples obtained from Atherosclerosis Risk in Communities study (ARIC) visit 3 (n = 11,701) to assess the association between the SomaScan® assay CV risk tests (secondary cardiovascular risk, primary cardiovascular risk, heart failure with preserved ejection fraction, and heart failure with reduced ejection fraction) and incident dementia. Survival analysis was used to estimate 20-year Kaplan-Meier (KM) dementia rates and hazard ratios by tertile of CV risk scores.

Results

Increasing risks of all four CV-related risk tests were positively associated with the risk for dementia (P< 0.0001) and the tertiles for each of these tests stratified dementia risk 20 years from sample collection (figure). The table shows the KM dementia 20-year event rates and hazard ratios for the associations between CV risk domains and dementia. For example, compared to the lowest risk tertile, there was a 59% increased risk for dementia in the middle secondary CV risk tertile and more than 2-fold increased dementia risk in the highest risk tertile.

Discussion

These findings demonstrate an association between CV risk and the development of dementia and may capture pathophysiology of dementia that is below the threshold to detect disease. In addition, the use of proteomic phenotypes can be a tool for monitoring to characterize the multi-organ disease processes and vascular association with the development of dementia.

Background and aims

The Montreal Cognitive Assessment (MoCA) is a widely used instrument for assessing cognitive function in stroke survivors. To interpret changes in MoCA scores accurately, it is crucial to consider the minimal detectable change (MDC) and minimal clinically important difference (MCID). The aim was to establish the MDC and MCID of the MoCA within 6 months after stroke.

Methods

This cohort study analysed data from the EFFECTS trial. The MoCA was administered at baseline and at 6-month follow-up. The MDC was calculated as the upper limit of the 95 % confidence interval of the standard error of the MoCA mean. The MCID was determined using anchor-based and distribution methods. The visual analogue recovery scale of the Stroke Impact Scale (SIS [primary anchor]) and Euro Quality of Life-5 Dimensions index (EQ-5D [confirmatory anchor]) were used as anchors. The distribution-based method, the Cohen benchmark effect size was chosen.

Results

In total, 1131 (mean age [SD], 71 [10.6] years) participants were included. The mean (SD) MoCA scores at admission and 6-month follow-up were 22 (5.2) and 25 (4.2), respectively. The MDC of the MoCA was 5.1 points. The anchor method yielded the MCIDs 2 and 1.6 points for SIS and EQ-5D, respectively. Using the distribution method, the MCID for the MoCA was 1 point.

Conclusions

Even a small change in MoCA scores can be important for stroke survivors; however, larger differences are required to ensure that any difference in MoCA values is a true change and is not related to the inherent variation in the test. Due to small sample sizes, the results of the anchor analysis need to be interpreted with caution.