Cerebral circulation - cognition and behavior最新文献

Introduction

Cerebrovascular disease is a leading cause of death and disability worldwide. Identification and treatment of cognitive impairment following cerebrovascular disease (such as following stroke) remains a large unmet need. There is a growing need for in-depth scalable and cost-effective longitudinal assessment of cognitive function to better understand the range of factors that contribute to long-term cognitive outcomes after a vascular insult. To address this gap and to capitalise on the recent growth of telemedicine, we developed the IC3 online tool (Imperial College Comprehensive assessment for Cerebrovascular disease; https://ic3study.co.uk/) combined with MRI brain imaging and plasma biomarkers to identify novel multimodal predictors of recovery after stroke (Figure 1).

Figure 1: Study Timeline.

Methods

The IC3 platform is a web-based digital technology, designed to detect both domain-general and domain-specific cognitive deficits prevalent in cerebrovascular disease (Figure 2). Demographic, socio-economic, and neuropsychiatric information is collected alongside 22 short, computerised cognitive tests, with minimal input from a health professional, at 0-, 3-, 6-, and 12-months post- stroke. These are related to structural and functional brain MRI and plasma biomarkers (such as plasma brain-derived tau, neurofilament light, glial fibrillary acidic protein and amyloid entities).

Results

We present IC3 assessment results from N>5000 older adults. We outline the cognitive performance of a modest sample of patients with stroke against a gender-, age- and education- matched control sample. Furthermore, we present an overview of our validation studies which examine the battery's specificity and sensitivity, and test-retest reliability. Finally, as the assessment has been designed to be self-administered remotely, we also present validation against face-to-face supervised delivery of the battery and discuss the effect of several technical confounds affecting a patient's performance (such as device type, operating system, and motoric impairments).

Discussion

The IC3 tool is the first assessment to offer a an in-depth relatively unsupervised cognitive phenotyping of patients with cerebrovascular disease, facilitating scalable and cost-efficient longitudinal monitoring of cognition in this group. The assessment fares very well against various validation methods, making it an attractive tool for understanding the mechanisms of recovery in relation to novel brain and plasma biomarkers in a plethora of cerebrovascular disorders.

Introduction

Post-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke.

Methods

This retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3–6 months, defined as an adjusted z-score of less than -2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards neuropsychological protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables.

Results

A total of 1047 patients (mean age 65.7±11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts.

Discussion

Our findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.

Introduction

Recent small subcortical infarcts (RSSI) may evolve into lacunes (cavities) smaller than 3mm or even disappear. The 3mm size cut-off used in guidelines might underestimate SVD burden. We hypothesised that participants with smaller (<3mm) lacunes have better cognitive outcomes at one-year follow-up than those with larger lacunes. We also aimed to determine rates of development of lacunes <3mm.

Methods

We recruited participants from two prospective stroke cohorts (MSS2 and MSS3) within 3-months after mild stroke. We included participants with MRI-confirmed RSSI and at least two MRI scans during the first one-year follow-up. We assessed for lesion change by visual assessment on T2- FLAIR (blinded). We recorded demographics, vascular risk factors, SVD burden, and clinical outcomes (NIHSS, modified Rankin score [mRS], Montreal Cognitive Assessment score [MoCA]), at baseline and one-year. We report maximum axial diameters (max-ax, mm) for RSSI and lacunes (continuous and dichotomised at < /≥3mm). We used regression analysis for associations between final lacune size/appearance and outcomes at one-year, adjusting for baseline demographics, VRF, and clinical scores.

Results

We included 198 participants; mean age 64 years (SD 11.1); 33% female. At one-year, 53/184 (26.8%) RSSI evolved into lacunes <3mm and 105/184 in to lacunes over 3mm (Table.1) Participants with lacunes <3mm had higher MoCA (MoCA<26; RR=0.57 [95%CI 0.33, 0.97]; vs 1.35 [1.05-1.75] for larger lacunes; p=0.03) and lower mRS (mRS 0-1; RR=1.79[1.11,2.91] vs 0.72[0.58-0.89]; p=.009). The end-stage lacune size correlated with RSSI max-ax diameter at baseline (r[df1]=[0.73],p<.001); there were no associations with demographics, VRF or SVD burden. At one-year, 47/143 (23.7%) participants had MoCA<26, and we investigated the effects of age, NIHSS, NART, RSSI max-ax diameter, SVD burden and MoCA at baseline and end-stage lacune max-axial diameter in this group. MoCA at baseline was a significant predictor for cognition at one-year (β=0.586, SE=0.90 [95%CI: 0.41, 0.76], p<.001). MoCA scores were lower in those with larger end-stage lacunes (β=-1.950, SE=0.70 [95%CI: 0.04, 0.56], p=0.005).

Discussion

Larger end-stage lacune diameters are associated with worse cognitive outcomes at one-year after mild stroke. Careful cognitive and lesion assessment of patients at diagnosis may help determine cognitive trajectories in patients with mild stroke.

Introduction

The relative contributions of aortic and carotid artery stiffness on cognitive impairment and dementia remain unknown. We examined the associations of aortic and carotid artery stiffness with cerebrovascular disease markers, cognition, and dementia subtypes in a memory clinic cohort.

Methods

272 participants underwent applanation tonometry, carotid ultrasonography, 3T brain MRI, and neuropsychological assessment. Aortic stiffness parameters (carotid-femoral pulse wave velocity, aortic augmentation index, and aortic pulse pressure) were obtained with applanation tonometry whilst carotid artery stiffness parameters (β-index, pressure-strain elastic modulus, and pulse- wave velocity-β) were assessed by carotid ultrasonography, from which composite scores for aortic and carotid artery stiffness were calculated. Brain magnetic resonance images were graded for cerebrovascular disease markers, including white matter hyperintensities (WMH), lacunes, cerebral microbleeds, cortical infarcts, and stenosis. Cognition was assessed by the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the National Institute of Neurological Disorders and Stroke–Canadian Stroke Network harmonization battery in order to classify participants as having no cognitive impairment, cognitive impairment no dementia, or dementia subtyped as Alzheimer disease and vascular dementia.

Results

After considering both aortic and carotid artery stiffness in the same regression models, carotid artery stiffness remained independently and significantly associated with WMH (β=0.78, p<0.001), cortical infarcts (odds ratio [OR], 1.05, p=0.003), vascular dementia (OR, 1.10, p=0.017), MMSE (β=-0.89, p=0.013), global cognition (β=-0.33, p=0.017), and visuomotor speed (β=-0.19, p=0.004). Although aortic stiffness was associated with lacunes (OR, 1.09, p=0.035), and cortical infarcts (OR, 1.063, p=0.016), these associations became non-significant after adjusting for carotid artery stiffness.

Discussion

We found that compared with aortic stiffness, carotid artery stiffness had more robust associations with markers of cerebrovascular disease, vascular dementia, and cognitive function in memory clinic patients. Our study suggests that carotid artery stiffness may have a more salient effect on the brain.

Anxiety has been associated with a greater risk of Alzheimer's disease (AD). Existing research has identified structural differences in regional brain tissue in participants with anxiety, but results have been inconsistent. We sought to determine the association between anxiety and regional brain volumes, and the moderation effect of APOE ε4. Using data from participants in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set, with complete imaging (MRI) and biomarker data (n = 1533), multiple linear regression estimated the adjusted effect of anxiety on 30 structural MRI regions. The moderation effect of APOE ε4 on the relation between structural MRI regions and anxiety was assessed as was the moderation effect of cognitive status. False discovery rate was used to adjust for multiple comparisons. After controlling for intracranial volume, age, sex, years of education, race, Hispanic ethnicity, and cognitive status, seven MRI regions demonstrated lower volumes among participants with anxiety: total cerebrum gray matter volume, right hippocampus volume, hippocampal volume (total), right and left frontal lobe cortical gray matter volume, and right and total temporal lobe cortical gray matter volume. Findings suggest that anxiety is associated with significant atrophy in multiple brain regions, with corresponding ventricular enlargement. Future research should investigate if anxiety-related changes to brain morphology contribute to greater AD risk.

Background

Psycho-cognitive consequences are a frequent cause of disability in stroke survivors but are often underdiagnosed also because of lack of services dedicated to these aspects. We started assessing systematically cognitive and behavioral functions in acute stroke patients and to follow them up. Here, we report a retrospective analysis of the organization of the Sacco VAS-COG stroke care pathway and the refinements implemented during 5 years of activity.

Methods

The protocol includes baseline collection of clinical history, general and neurologic examinations, functional, neuropsychological, and neuroimaging assessment. At follow-up, a diagnosis of cognitive decline was made based on best clinical judgment in the first period (January 2018 to May 2019, namely VAS-COG protocol 1.0) and then based on an extensive neuropsychological battery (May 2019 to January 2023, namely VASCOG protocol 2.0); psychiatric and behavioral disturbances are investigated through suitable scales.

Results

From January 2018 to December 2022, 834 patients (mean age 76±13.6 years; 46.6 % females) with acute cerebrovascular events were admitted to the stroke unit, mostly (80 %) for ischemic strokes. Pre-event cognitive impairment was not assessable in 78 patients (9.3 %) because no reliable informant was present and was reported in 327/756 (43 %) patients. During follow-up, post-stroke cognitive impairment was detected in 124/217 (57.1 %) patients in VAS-COG protocol 1.0 and in 137/201(68.2 %) patients in VAS-COG protocol 2.0, while 95/218 (43.2 %) patients were found to be depressed and patients presented on average 2.5 neuropsychiatric symptoms on Neuropsychiatric Inventory-questionnaire.

Conclusions

The VAS-COG stroke care pathway represents a model for patients and for their families.