Cerebral circulation - cognition and behavior最新文献

Background and aims

Methods

Results

Conclusions

Clinical trial registration

Background

Objective

Methods

Results

Conclusions

Introduction

Methods

Results

Conclusions

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.