Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke

Effects of Combined Use of Intermittent Theta Burst Stimulation and Cognitive Training on Poststroke Cognitive Impairment: A Single-Blind Randomized Controlled Trial

OBJECTIVE

Poststroke cognitive impairment substantially affects patients' quality of life. This study explored the therapeutic efficacy of intermittent theta burst stimulation combined with cognitive training for poststroke cognitive impairment.

DESIGN

The experimental group received intermittent theta burst stimulation and cognitive training, whereas the control group only received cognitive training, both for 6 wks. The outcome measures were the Loewenstein Occupational Therapy Cognitive Assessment, modified Barthel Index, transcranial Doppler ultrasonography, and functional near-infrared spectroscopy.

RESULTS

After therapy, between-group comparisons revealed a substantial difference in the Loewenstein Occupational Therapy Cognitive Assessment scores ( P = 0.024). Improvements in visuomotor organization and thinking operations were more noticeable in the experimental group than in the other groups ( P = 0.017 and P = 0.044, respectively). After treatment, the resistance index of the experimental group differed from that of the control group; channels 29, 37, and 41 were activated ( P < 0.05). The active locations were the left dorsolateral prefrontal cortex, prefrontal polar cortex, and left Broca's region.

CONCLUSIONS

Intermittent theta burst stimulation combined with cognitive training had a superior effect on improving cognitive function and everyday activities compared with cognitive training alone, notably in visuomotor organization and thinking operations. Intermittent theta burst stimulation may enhance cognitive performance by improving network connectivity.

Blood biomarkers for post-stroke cognitive impairment: A systematic review and meta-analysis

BACKGROUND AND PURPOSE

Post-stroke cognitive impairment (PSCI) is a frequent consequence of stroke, which affects the quality of life and prognosis of stroke survivors. Numerous studies have indicated that blood biomarkers may be the key determinants for predicting and diagnosing cognitive impairment, but the results remain varied. Therefore, this meta-analysis aims to summarize potential biomarkers associated with PSCI.

METHODS

PubMed, Web of Science, Embase, and Cochrane Library were comprehensively searched for studies exploring blood biomarkers associated with PSCI from inception to 15 April 2022.

RESULTS

63 studies were selected from 4,047 references, which involves 95 blood biomarkers associated with the PSCI. We meta-analyzed 20 potential blood biomarker candidates, the results shown that the homocysteine (Hcy) (SMD = 0.35; 95 %CI: 0.20-0.49; P < 0.00001), c-reactive protein (CRP) (SMD = 0.49; 95 %CI: 0.20-0.78; P = 0.0008), uric acid (UA) (SMD = 0.41; 95 %CI: 0.06-0.76; P = 0.02), interleukin 6 (IL-6) (SMD = 0.92; 95 % CI: 0.27-1.57; P = 0.005), cystatin C (Cys-C) (SMD = 0.58; 95 %CI: 0.28-0.87; P = 0.0001), creatinine (SMD = 0.39; 95 %CI: 0.23-0.55; P < 0.00001) and tumor necrosis factor alpha (TNF-α) (SMD = 0.45; 95 %CI: 0.08-0.82; P = 0.02) levels were significantly higher in patients with PSCI than in the non-PSCI group.

CONCLUSION

Based on our findings, we recommend that paramedics focus on the blood biomarkers levels of Hcy, CRP, UA, IL-6, Cys-C, creatinine and TNF-α in conjunction with neuroimaging and neuropsychological assessment to assess the risk of PSCI, which may help with early detection and timely preventive measures. At the same time, other potential blood biomarkers should be further validated in future studies.

Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study

BACKGROUND

Post-stroke cognitive impairment (PSCI) is a serious complication of stroke that warrants prompt detection and management. Consequently, the development of a diagnostic prediction model holds clinical significance.

OBJECTIVE

Machine learning algorithms were employed to identify crucial variables and forecast PSCI occurrence within 3-6 months following acute ischemic stroke (AIS).

METHODS

A prospective study was conducted on a developed cohort (331 patients) utilizing data from the Affiliated Zhongda Hospital of Southeast University between January 2022 and August 2022, as well as an external validation cohort (66 patients) from December 2022 to January 2023. The optimal model was determined by integrating nine machine learning classification models, and personalized risk assessment was facilitated by a Shapley Additive exPlanations (SHAP) interpretation.

RESULTS

Age, education, baseline National Institutes of Health Scale (NIHSS), Cerebral white matter degeneration (CWMD), Homocysteine (Hcy), and C-reactive protein (CRP) were identified as predictors of PSCI occurrence. Gaussian Naïve Bayes (GNB) model was determined to be the optimal model, surpassing other classifier models in the validation set (area under the curve [AUC]: 0.925, 95 % confidence interval [CI]: 0.861 - 0.988) and achieving the lowest Brier score. The GNB model performed well in the test sets (AUC: 0.919, accuracy: 0.864, sensitivity: 0.818, and specificity: 0.932).

CONCLUSIONS

The present study involved the development of a GNB model and its elucidation through employment of the SHAP method. These findings provide compelling evidence for preventing PSCI, which could serve as a guide for high-risk patients to undertake appropriate preventive measures.

Prediction of post-stroke cognitive impairment after acute ischemic stroke using machine learning

BACKGROUND AND OBJECTIVES

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 951 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.

CONCLUSION

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.

Changes in gene expression and neuroinflammation in the hippocampus of rats with poststroke cognitive impairment

Poststroke cognitive impairment (PSCI) often occurs during the stroke recovery period and greatly increases the difficulty of rehabilitation. Activation of neuroinflammation and long-term changes in gene expression patterns in the hippocampus could be essential in the development of PSCI. Therefore, this study aimed to identify neuroinflammation and changes in gene expression patterns in the hippocampus in rats with PSCI. Rats underwent transient middle cerebral artery occlusion (tMCAO) or sham surgery. The infarct volume was measured on day 3 after surgery. The Morris water maze (MWM) test was used to assess cognitive function. Microglial activation and white matter (WM) lesions in the hippocampus were evaluated on day 28 after surgery. In addition, we compared differentially expressed genes (DEGs) in the hippocampus between tMCAO group rats and sham group rats by RNA sequencing. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analyses were conducted to investigate these DEGs. The results showed that the tMCAO group rats showed extensive infarction and cognitive dysfunction compared with the sham group rats. Microglial activation and WM damage were obvious in the hippocampus of tMCAO group rats. We found 43 DEGs by RNA sequencing: 29 genes with upregulated expression and 14 genes with downregulated expression. The GO analysis indicated that DEGs were mainly involved in cell proliferation and differentiation, cholesterol synthesis, and metabolism. The KEGG pathway analysis suggested that the DEGs were significantly enriched in intestinal immune network for IgA production and steroid biosynthesis. Acta2, Calb2, and Cxcl12 were notable in the PPI analysis. Our results suggest that microglial activation and WM damage are maintained in rats with PSCI. The mechanism may be related to the regulation of steroid biosynthesis, intestinal immunity, and potential key genes such as Acta2, Calb2, and Cxcl12 in the hippocampus.

Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke

Background

Accurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques to systematically compare the prediction of functional recovery, cognitive function, depression, and mortality of first-ever ischemic stroke patients and to identify the leading prognostic factors.

Methods

We predicted clinical outcomes for 307 patients (151 females, 156 males; 68 ± 14 years) from the PROSpective Cohort with Incident Stroke Berlin study using 43 baseline features. Outcomes included modified Rankin Scale (mRS), Barthel Index (BI), Mini-Mental State Examination (MMSE), Modified Telephone Interview for Cognitive Status (TICS-M), Center for Epidemiologic Studies Depression Scale (CES-D) and survival. The ML models included a Support Vector Machine with a linear kernel and a radial basis function kernel as well as a Gradient Boosting Classifier based on repeated 5-fold nested cross-validation. The leading prognostic features were identified using Shapley additive explanations.

Results

The ML models achieved significant prediction performance for mRS at patient discharge and after 1 year, BI and MMSE at patient discharge, TICS-M after 1 and 3 years and CES-D after 1 year. Additionally, we showed that National Institutes of Health Stroke Scale (NIHSS) was the top predictor for most functional recovery outcomes as well as education for cognitive function and depression.

Conclusion

Our machine learning analysis successfully demonstrated the ability to predict clinical outcomes after first-ever ischemic stroke and identified the leading prognostic factors that contribute to this prediction.

Neuroimaging biomarkers of cognitive recovery after ischemic stroke

Post-stroke cognitive impairment affects more than one-third of patients after an ischemic stroke (IS). Identifying markers of potential cognitive recovery after ischemic stroke can guide patients' selection for treatments, enrollment in clinical trials, and cognitive rehabilitation methods to restore cognitive abilities in post-stroke patients. Despite the burden of post-stroke cognitive impairment, biomarkers of cognitive recovery are an understudied area of research. This narrative review summarizes and critically reviews the current literature on the use and utility of neuroimaging as a predictive biomarker of cognitive recovery after IS. Most studies included in this review utilized structural Magnetic Resonance Imaging (MRI) to predict cognitive recovery after IS; these studies highlighted baseline markers of cerebral small vessel disease and cortical atrophy as predictors of cognitive recovery. Functional Magnetic Resonance Imaging (fMRI) using resting-state functional connectivity and Diffusion Imaging are potential biomarkers of cognitive recovery after IS, although more precise predictive tools are needed. Comparison of these studies is limited by heterogeneity in cognitive assessments. For all modalities, current findings need replication in larger samples. Although no neuroimaging tool is ready for use as a biomarker at this stage, these studies suggest a clinically meaningful role for neuroimaging in predicting post-stroke cognitive recovery.

Clinical Risk Score for Predicting Vascular Dementia after Ischemic Stroke in Thailand

Background. Poststroke dementia is an important consequence of stroke and warrants early prevention, detection, and management. The objective of the study was to develop a simple clinical risk score for predicting risk of vascular dementia in patients with ischemic stroke. Methods. The design was a prospective cohort study with 177 ischemic stroke survivors. A standard stroke evaluation was performed at admission, and dementia evaluation was conducted at six months after stroke. The significant predictors were used to develop a risk score using a multivariable logistic regression model. Results. Six months after stroke, 27.1% of the patients were diagnosed with vascular dementia. Five predictors were used in the risk score: age, education, history of stroke, white matter hyperintensities, and stroke subtype. The risk score had an area under receiver operating characteristic curve (AuROC) of 0.76, 72.9% sensitivity, and 79.1% specificity in predicting risk of vascular dementia. The predicted probability of vascular dementia for each risk score point was also reported. Conclusion. The clinical risk score had an acceptable accuracy in predicting vascular dementia in ischemic stroke survivors. It can be used for identifying those who are at a high risk of developing vascular dementia.

High serum amyloid A predicts risk of cognitive impairment after lacunar infarction: Development and validation of a nomogram

Background

Post-stroke cognitive impairment (PSCI) after lacunar infarction was worth attention in recent years. An easy-to-use score model to predict the risk of PSCI was rare. This study aimed to explore the association between serum amyloid A (SAA) and cognitive impairment, and it also developed a nomogram for predicting the risk of PSCI in lacunar infarction patients.

Methods

A total of 313 patients with lacunar infarction were enrolled in this retrospective study between January 2021 and December 2021. They were divided into a training set and a validation set at 70%:30% randomly. The Chinese version of the Mini-Mental State Examination (MMSE) was performed to identify cognitive impairment 3 months after discharge. Univariate and multivariate logistic regression analyses were used to determine the independent risk factors for PSCI in the training set. A nomogram was developed based on the five variables, and the calibration curve and the receiver operating characteristic (ROC) curve were drawn to assess the predictive ability of the nomogram between the training set and the validation set. The decision curve analysis (DCA) was also conducted in both sets.

Results

In total, 52/313 (16.61%) participants were identified with PSCI. The SAA levels in patients with PSCI were significantly higher than non-PSCI patients in the training set (P < 0.001). After multivariate analysis, age, diabetes mellitus, white blood count, cystatin C, and SAA were independent risk predictors of PSCI. The nomogram demonstrated a good discrimination performance between the training set (AUC = 0.860) and the validation set (AUC = 0.811). The DCA showed that the nomogram had a well clinical utility in the two sets.

Conclusion

The increased SAA is associated with PSCI in lacunar infarction patients, and the nomogram developed with SAA can increase prognostic information for the early detection of PSCI.

Do P2Y12 receptor inhibitors prescribed poststroke modify the risk of cognitive disorder or dementia? Protocol for a target trial using multiple national Swedish registries

INTRODUCTION

The target of a class of antiplatelet medicines, P2Y12R inhibitors, exists both on platelets and on brain immune cells (microglia). This protocol aims to describe a causal (based on a counterfactual model) approach for analysing whether P2Y12R inhibitors prescribed for secondary prevention poststroke may increase the risk of cognitive disorder or dementia via their actions on microglia, using real-world evidence.

METHODS AND ANALYSIS

This will be a cohort study nested within the Swedish National Health and Medical Registers, including all people with incident stroke from 2006 to 2016. We developed directed acyclic graphs to operationalise the causal research question considering potential time-independent and time-dependent confounding, using input from several experts. We developed a study protocol following the components of the target trial approach described by Hernan et al and describe the data structure that would be required in order to make a causal inference. We also describe the statistical approach required to derive the causal estimand associated with this important clinical question; that is, a time-to-event analysis for the development of cognitive disorder or dementia at 1, 2 and 5-year follow-up, based on approaches for competing events to account for the risk of all-cause mortality. Causal effect estimates and the precision in these estimates will be quantified.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the University of Gothenburg and Confidentiality Clearance at Statistics Sweden with Dnr 937-18, and an approved addendum with Dnr 2019-0157. The analysis and interpretation of the results will be heavily reliant on the structure, quality and potential for bias of the databases used. When we implement the protocol, we will consider and document any biases specific to the dataset and conduct appropriate sensitivity analyses. Findings will be disseminated to local stakeholders via conferences, and published in appropriate scientific journals.

Gamma-glutamyl transferase levels are associated with the occurrence of post-stroke cognitive impairment: a multicenter cohort study

Background

Gamma-glutamyl transferase (GGT) is involved in maintenance of physiological concentrations of glutathione in cells, and protects them from oxidative stress-induced damage. However, its role in post-stroke cognitive impairment (PSCI) remains unknown. Here, we investigated the effects of serum GGT on PSCI.

Methods

We conducted a prospective, multicenter cohort study. A total of 1, 957 participants with a minor ischemic stroke or transient ischemic attack whose baseline GGT levels were measured were enrolled from the Impairment of Cognition and Sleep (ICONS) study of the China National Stroke Registry-3 (CNSR-3). They were categorized into four groups according to quartiles of baseline GGT levels. Cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) approach. Multiple logistic regression models were performed to evaluate the relationship between GGT and PSCI at 3 months follow-up.

Results

Among the 1957 participants, 671 (34.29%) patients suffered PSCI at 3 months follow-up. The highest GGT level quartile group exhibited a lower risk of PSCI in the fully adjusted model [OR (95% CI): 0.69 (0.50-0.96)], relative to the lowest group. Moreover, incorporation of GGT to the conventional model resulted in slight improvements in PSCI outcomes after 3 months (NRI: 12.00%; IDI: 0.30%).

Conclusions

Serum GGT levels are inversely associated with the risk of PSCI, with extremely low levels being viable risk factors for PSCI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02587-4.

Post-Stroke Cognitive Impairment: Epidemiology, Risk Factors, and Management

Stroke, characterized as a neurological deficit of cerebrovascular cause, is very common in older adults. Increasing evidence suggests stroke contributes to the risk and severity of cognitive impairment. People with cognitive impairment following stroke often face with quality-of-life issues and require ongoing support, which have a profound effect on caregivers and society. The high morbidity of post-stroke cognitive impairment (PSCI) demands effective management strategies, in which preventive strategies are more appealing, especially those targeting towards modifiable risk factors. In this review article, we attempt to summarize existing evidence and knowledge gaps on PSCI: elaborating on the heterogeneity in current definitions, reporting the inconsistent findings in PSCI prevalence in the literature, exploring established or less established predictors, outlining prevention and treatment strategies potentially effective or currently being tested, and proposing promising directions for future research.

Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques

Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epilepsy. Data from 441 outpatients with epilepsy were included; of these, 433 patients met the 12 clinical characteristic criteria and were divided into training (n = 304) and experimental (n = 129) groups. After descriptive statistics were analyzed, cross-validation was used to select the optimal model. The random forest (RF) algorithm was combined with the redundancy analysis (RDA) algorithm; then, optimal feature selection and resampling were carried out after removing linear redundancy information. The features that contributed more to multiple outcomes were selected. Finally, the external traceability of the model was evaluated using the follow-up data. The RF algorithm was the best prediction model for both MMSE and MoCA outcomes. Finally, seven markers were screened by overlapping the top ten important features for MMSE ranked by RF modeling, those ranked for MoCA ranked by RF modeling, and those for both assessments ranked by RDA. The optimal combination of features were namely, sex, age, age of onset, seizure frequency, brain MRI abnormalities, epileptiform discharge in EEG and usage of drugs. which was the most efficient in predicting outcomes of MMSE, MoCA, and both assessments.

Development and validation of a clinical model (DREAM-LDL) for post-stroke cognitive impairment at 6 months

Introduction: This multicenter, retrospective study assessed the prevalence of post-stroke cognitive impairment (PSCI) 6 months after acute ischemic stroke (AIS) and its risk factors to build a bedside early predictive model for PSCI using the Montreal Cognitive Assessment (MoCA).

Methods: Records of consecutive patients with AIS treated at 4 stroke centers in Shanghai had MoCA assessments within 2 weeks after AIS onset and 6 months later were reviewed. Prevalence of PSCI (MoCA<22) was calculated and risk factors were identified by multivariate logistic regression analysis. The modeling and validation and identified risk factors were included in a predictive model using multivariate regression.

Results: There were 383 patients included and prevalence of PSCI 6 months after AIS was 34.2%, significantly lower than prevalence of patients with acute cognitive impairment (49.6%). Aging, less education, higher glucose level and severe stroke were PSCI risk factors, while level of low-density lipoprotein cholesterol (LDL-C) had a paradox effect on the risk of PSCI. 40.0% of the patients with cognitive impairment at acute phase reverted to normal, and patients with LDL-C 1.8-2.5 mmol/L were more likely to revert. The predictive model we built, DREAM-LDL (Diabetes [fasting blood glucose level], Rating [NIHSS], level of Education, Age, baseline MoCA and LDL-C level), had an AUROC of 0.93 for predicting PSCI at 6 months.

Conclusion: PSCI was common among AIS patients 6 months after AIS. We provided a practical tool to predict PSCI based on MoCA and risk factors present during acute phase of AIS.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

BACKGROUND AND PURPOSE

The optimal management of post-stroke cognitive impairment (PSCI) remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making regarding prevention, diagnosis, treatment and prognosis.

METHODS

Guidelines were developed according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews, assessed the quality of the available evidence, and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations.

RESULTS

There was limited randomized controlled trial (RCT) evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Lifestyle interventions and treating vascular risk factors have many health benefits, but a cognitive effect is not proven. We found no evidence regarding routine cognitive screening following stroke, but recognize the importance of targeted cognitive assessment. We describe the accuracy of various cognitive screening tests, but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognition. The association between PSCI and acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on brain magnetic resonance imaging may help predict cognitive outcomes.

CONCLUSIONS

These guidelines highlight fundamental areas where robust evidence is lacking. Further definitive RCTs are needed, and we suggest priority areas for future research.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment

The optimal management of post-stroke cognitive impairment remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making around prevention, diagnosis, treatment and prognosis. These guidelines were developed according to ESO standard operating procedure and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews and, where possible, meta-analyses of the literature, assessed the quality of the available evidence and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations based on the GRADE approach. There was limited randomised controlled trial evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Interventions to improve lifestyle and treat vascular risk factors may have many health benefits but a beneficial effect on cognition is not proven. We found no evidence around routine cognitive screening following stroke but recognise the importance of targeted cognitive assessment. We described the accuracy of various cognitive screening tests but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognitive syndromes (cognitive impairment, dementia and delirium). The association between post-stroke cognitive impairment and most acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on acute MRI brain may help predict cognitive outcomes. These guidelines have highlighted fundamental areas where robust evidence is lacking. Further, definitive randomised controlled trials are needed, and we suggest priority areas for future research.

Analysis of the application of "psycho-cardiology" model in nursing care of acute stroke patients with depression

OBJECTIVE

To evaluate the effect of "psycho-cardiology" model in nursing care of acute stroke patients with depression.

METHODS

Seventy-eight acute stroke patients with depression were selected for this prospective study, and they were divided into two groups according to the random number table method. The control group (n=39) were given usual care, and the study group (n=39) were given nursing intervention of "psycho-cardiology" model in addition to usual care. The changes of mental state (Hamilton Depression Scale, HAMD; Hamilton Anxiety Scale, HAMA), the neurological function (National Institute of Health Stroke scale, NIHSS), and the cognitive function (Mini-Mental State Examination, MMSE), the prognostic indicator (Fugl-Meyer Assessment, FMA; Barthel Index, BI) were compared between the two groups before and after the intervention. The incidence of complications and nursing satisfaction were also compared between the two groups.

RESULTS

After nursing, the scores of HAMA and HAMD in the study group were significantly lower than those in the control group (P<0.05). The NIHSS score of the study group was significantly lower than that of the control group (P<0.05). The score of MMSE in the study group was significantly higher than that of the control group (P<0.05). The scores of FMA and BI in the study group were significantly higher than those of the control group (P<0.05). There was no significant difference in the incidence of complications between the two groups (P>0.05). The nursing satisfaction of the study group was significantly higher than that of the control group (P<0.05).

CONCLUSION

Nursing intervention of "psycho-cardiology" model for acute stroke patients with depression can effectively alleviate the mental stress of patients, improve neurological function and cognitive function, reduce the occurrence of complications, improve prognosis and nursing satisfaction.

Risk Prediction of Cognitive Decline after Stroke

BACKGROUND AND PURPOSE

Cognitive decline is one of the major outcomes after stroke. We have developed and evaluated a risk predictive tool of post-stroke cognitive decline and assessed its clinical utility.

METHODS

In this population-based cohort, 4,783 patients with first-ever stroke from the South London Stroke Register (1995-2010) were included in developing the model. Cognitive impairment was measured using the Mini Mental State Examination (cut off 24/30) and the Abbreviated Mental Test (cut off 8/10) at 3-months and yearly thereafter. A penalised mixed-effects linear model was developed and temporal-validated in a new cohort consisted of 1,718 stroke register participants recruited from (2011-2018). Prediction errors on discrimination and calibration were assessed. The clinical utility of the model was evaluated using prognostic accuracy measurements and decision curve analysis.

RESULTS

The overall predictive model showed good accuracy, with root mean squared error of 0.12 and R2 of 73%. Good prognostic accuracy for predicting severe cognitive decline was observed AUC: (88%, 95% CI [85-90]), (89.6%, 95% CI [86-92]), (87%, 95% CI [85-91]) at 3 months, one and 5 years respectively. Average predicted recovery patterns were analysed by age, stroke subtype, Glasgow-coma scale, and left-stroke and showed variability. DECISION: curve analysis showed an increased clinical benefit, particularly at threshold probabilities of above 15% for predictive risk of cognitive impairment.

CONCLUSIONS

The derived prognostic model seems to accurately screen the risk of post-stroke cognitive decline. Such prediction could support the development of more tailored management evaluations and identify groups for further study and future trials.

Nomogram to Predict Cognitive Dysfunction After a Minor Ischemic Stroke in Hospitalized-Population

An easily scoring system to predict the risk of cognitive impairment after minor ischemic stroke has not been available. We aimed to develop and externally validate a nomogram for predicting the probability of post-stroke cognitive impairment (PSCI) among hospitalized population with minor stroke. Moreover, the association of Trimethylamine N-oxide (TMAO) with PSCI is also investigated. We prospectively conducted a developed cohort on collected data in stroke center from June 2017 to February 2018, as well as an external validation cohort between June 2018 and February 2019. The main outcome is cognitive impairment defined as <22 Montreal Cognition Assessment (MoCA) score points 6 – 12 months following a minor stroke onset. Based on multivariate logistic models, the nomogram model was generated. Plasma TMAO levels were assessed at admission using liquid chromatography tandem mass spectrometry. A total of 228 participants completed the follow-up data for generating the nomogram. After multivariate logistic regression, seven variables remained independent predictors of PSCI to compose the nomogram included age, female, Fazekas score, educational level, number of intracranial atherosclerotic stenosis (ICAS), HbA1c, and cortical infarction. The area under the receiver-operating characteristic (AUC-ROC) curve of model was 0.829, C index was good (0.810), and the AUC-ROC of the model applied in validation cohort was 0.812. Plasma TMAO levels were higher in patients with cognitive impairment than in them without cognitive dysfunction (median 4.56 vs. 3.22 μmol/L; p ≤ 0.001). In conclusion, this scoring system is the first nomogram developed and validated in a stroke center cohort for individualized prediction of cognitive impairment after minor stroke. Higher plasma TMAO level at admission suggests a potential marker of PSCI.

Prognostic rules for predicting cognitive syndromes following stroke: A systematic review

Purpose

Stroke survivors are at high risk of developing cognitive syndromes, such as delirium and dementia. Accurate prediction of future cognitive outcomes may aid timely diagnosis, intervention planning, and stratification in clinical trials. We aimed to identify, describe and appraise existing multivariable prognostic rules for prediction of post-stroke cognitive status.

Method

We systematically searched four electronic databases from inception to November 2019 for publications describing a method to estimate individual probability of developing a cognitive syndrome following stroke. We extracted data from selected studies using a pre-specified proforma and applied the Prediction model Risk Of Bias Assessment Tool (PROBAST) for critical appraisal.

Findings

Of 17,390 titles, we included 10 studies (3143 participants), presenting the development of 11 prognostic rules – 7 for post-stroke cognitive impairment and 4 for delirium. Most commonly incorporated predictors were: demographics, imaging findings, stroke type and symptom severity. Among studies assessing predictive discrimination, the area under the receiver operating characteristic (AUROC) in apparent validation ranged from 0.80 to 0.91. The overall risk of bias for each study was high. Only one prognostic rule had been externally validated.

Discussion/conclusion: Research into the prognosis of cognitive outcomes following stroke is an expanding field, still at its early stages. Recommending use of specific prognostic rules is limited by the high risk of bias in all identified studies, and lack of supporting evidence from external validation. To ensure the quality of future research, investigators should adhere to current, endorsed best practice guidelines for conduct of prediction model studies.

Early Post-stroke Cognition: In-hospital Predictors and the Association With Functional Outcome

Purpose: To characterize and predict early post-stroke cognitive impairment by describing cognitive changes in stroke patients 4-8 weeks post-infarct, determining the relationship between cognitive ability and functional status at this early time point, and identifying the in-hospital risk factors associated with early dysfunction. Materials and Methods: Data were collected for 214 patients with ischemic stroke and 39 non-stroke controls. Montreal Cognitive Assessment (MoCA) exams were administered at post-hospitalization clinic visits approximately 4-8 weeks after infarct. MoCA scores were compared for patients with: no stroke, minor stroke [NIH Stroke Scale (NIHSS) < 5], and major stroke. Ordinal logistic regression was performed to assess the relationship between MoCA score and functional status [modified Rankin Scale score (mRS)] at follow-up. Predictors of MoCA < 26 and < 19 (cutoffs for mild and severe cognitive impairment, respectively) at follow-up were identified by multivariable logistic regression using variables available during hospitalization. Results: Post stroke cognitive impairment was common, with 66.8% of patients scoring < 26 on the MoCA and 22.9% < 19. The average total MoCA score at follow-up was 18.7 (SD 7.0) among major strokes, 23.6 (SD 4.8) among minor strokes, and 27.2 (SD 13.0) among non-strokes (p = <0.0001). The follow-up MoCA score was associated with the follow-up mRS in adjusted analysis (OR 0.69; 95% C.I. 0.59-0.82). Among patients with no prior cognitive impairment (N = 201), a lack of pre-stroke employment, admission NIHSS > 6, and left-sided infarct predicted a follow-up MoCA < 26 (c-statistic 0.75); while admission NIHSS > 6 and infarct volume > 17 cc predicted a MoCA < 19 (c-statistic 0.75) at follow-up. Conclusion: Many patients experience early post-stroke cognitive dysfunction that significantly impacts function during a critical time period for decision-making regarding return to work and future independence. Dysfunction measured at 4-8 weeks can be predicted during the inpatient hospitalization. These high-risk individuals should be identified for targeted rehabilitation and counseling to improve longer-term post-stroke outcomes.

Predictors of Cognitive Impairment After Stroke: A Prospective Stroke Cohort Study

BACKGROUND

Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors' quality of life and rehabilitation. A risk model identifying cognitive decline at admission would help to improve early detection and management of post-stroke patients.

OBJECTIVE

To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI.

METHODS

We prospectively enrolled 179 patients diagnosed with acute ischemic stroke within a 7-day onset. Data were analyzed based on baseline demographics, clinical risk factors, and radiological parameters. Logistic regression and area under the receiver operating curve (AUROC) were used to evaluate model efficiency.

RESULTS

One hundred forty-five subjects completed a 6-12-month follow-up visit, and 77 patients (53.1%) were diagnosed with PSCI. Age (β= 0.065, OR = 1.067, 95% CI = 1.016-1.120), years of education (β= -0.346, OR = 0.707, 95% CI = 0.607-0.824), periventricular hyperintensity grading (β= 1.253, OR = 3.501, 95% CI = 1.652-7.417), diabetes mellitus (β= 1.762, OR = 5.825, 95% CI = 2.068-16.412), and the number of acute nonlacunar infarcts (β= 0.569, OR = 1.766, 95% CI = 1.243-2.510) were independently associated with 6-12 month PSCI, constituting a model with optimal predictive efficiency (AUC = 0.884, 95% CI = 0.832-0.935).

CONCLUSIONS

The optimized risk model was effective in screening stroke survivors at high risk of developing 6-12 months PSCI in a simple and pragmatic way. It could be a potential tool to identify patients with a high risk of PSCI at an early stage in clinical practice after further independent external cohort validation.

Association of Asymmetrical White Matter Hyperintensities and Apolipoprotein E4 on Cognitive Impairment

BACKGROUND

Asymmetrical patterns of cerebral damage have been widely observed in a range of neurodegenerative diseases, including Alzheimer's disease (AD).

OBJECTIVE

To elucidate the clinical associations of asymmetrical white matter hyperintensities (WMH) in mild cognitive impairment (MCI) and AD.

METHODS

Regional WMH asymmetry of 340 participants (90 healthy controls, 132 MCI, 118 AD) was calculated as the difference in normalized hemispheric WMH volume (WMH/ICV) adjusted for structural brain asymmetry of respective lobar regions and total WMH. WMH asymmetry was analyzed in relation to disease classification, cognition, and APOE4 status using ANCOVA and multiple regression analysis, controlling for gender, age, ethnicity, and correcting for multiple comparisons using Bonferroni correction. Moderation analysis examined interaction effects of APOE4 on associations between cognition and WMH asymmetry.

RESULTS

Greater left-dominant occipital WMH asymmetry was observed in AD, compared to healthy controls and MCI, and was associated with poorer global cognition, memory, language, and executive functions among cognitively impaired participants (MCI and AD). Cognitively impaired APOE4 carriers displayed greater left-dominant WMH asymmetry in the whole brain and frontal lobe, compared to non-carriers. Importantly, effects were independent of structural brain asymmetry, global cerebral atrophy, and overall WMH burden. Moderation analysis demonstrated associations between left-dominant WMH asymmetry and cognition in cognitively impaired APOE4 non-carriers, but not APOE4 carriers.

CONCLUSION

Leftward asymmetry of WMH may be more pathological in nature, compared to symmetrical WMH. Furthermore, the detrimental effects of WMH asymmetry was more relevant in APOE4-negative cognitive impairment, compared to APOE4-positive which may be driven primarily by AD pathology.

Evaluation of the Mini-Mental State Examination and the Montreal Cognitive Assessment for Predicting Post-stroke Cognitive Impairment During the Acute Phase in Chinese Minor Stroke Patients

Objective: To assess the value of the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) during acute phase in predicting post-stroke cognitive impairment (PSCI) at 3–6 months.

Methods: We prospectively recruited 229 patients who had suffered their first-ever ischemic stroke. PSCI was determined in 104 of these patients by a comprehensive neuropsychological battery performed at 3–6 months. Receiver operating characteristic (ROC) curve analysis was then performed to compare the discriminatory ability of the MMSE and MoCA. Also, we applied a decision tree generated by the classification and regression tree methodology.

Results: In total, 66 patients had PSCI when evaluated 3–6 months after the onset of minor stroke. Logistic regression analysis revealed that education, body mass index (BMI), and baseline MoCA scores were independently associated with PSCI. ROC curve analysis showed that the ability to predict PSCI was similar when compared between baseline MoCA scores [area under curve (AUC), 0.821; 95% confidence interval (CI), 0.743–0.898] and baseline MMSE scores (AUC, 0.809; 95% CI, 0.725–0.892, P = 0.75). Both MMSE and MoCA exhibited similar predictive values at their optimal cut-off points (MMSE ≤27; sensitivity, 0.682; specificity, 0.816; MoCA ≤21; sensitivity, 0.636; specificity, 0.895). Classification and regression tree-derived analysis yielded an AUC of 0.823 (sensitivity, 0.803; specificity, 0.842).

Conclusion: When applied within 2 weeks of stroke, the MMSE and MoCA are both useful and have similar predictive value for PSCI 3–6 months after the onset of minor stroke.

Care priorities for stroke patients developing cognitive difficulties: a Delphi survey of UK professional views

Background

Post stroke cognitive difficulties are common but generally prioritised below other impairments. In the UK, clinical guidelines recommend a holistic review at six-months post-stroke including an assessment of cognitive function. In order to assist clinicians to provide better care for patients with post-stroke cognitive deficits and assist with service planning, our aim was to establish professional consensus on key actions at the six-month review.

Methods

An electronic Delphi survey was developed with ten potential actions for clinicians to prioritise across five different clinical scenarios describing patients with cognitive difficulties. Scenarios varied in terms of age of the stroke-survivor, stroke severity and use of dementia risk assessment. A panel of professional volunteers was obtained through the British Association of Stroke Physicians and the UK National Stroke Nursing Forum.

Results

Forty-five stroke clinicians completed round one, with 21 participants completing round two. Priorities consistently supported by professionals included access to psychological services, screening for a mood disorder and ensuring multi-professional input. Direct access to specialist memory services was not generally supported unless a dementia risk assessment tool indicated that the individual was at high risk of dementia.

Conclusions

Assessment of post-stroke cognitive deficits needs to be routinely considered during the six-month review. A formal risk assessment tool could be a way to streamline direct access to memory clinic services to ensure that individuals at-risk of dementia receive ongoing care.

Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Supplemental Digital Content is available in the text.

Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity.

No evidence for amyloid pathology as a key mediator of neurodegeneration post-stroke - a seven-year follow-up study

Background

Cognitive impairment (CI) with mixed vascular and neurodegenerative pathologies after stroke is common. The role of amyloid pathology in post-stroke CI is unclear. We hypothesize that amyloid deposition, measured with Flutemetamol (¹⁸F-Flut) positron emission tomography (PET), is common in seven-year stroke survivors diagnosed with CI and, further, that quantitatively assessed ¹⁸F-Flut-PET uptake after 7 years correlates with amyloid-β peptide (Aβ₄₂) levels in cerebrospinal fluid (CSF) at 1 year, and with measures of neurodegeneration and cognition at 7 years post-stroke.

Methods

208 patients with first-ever stroke or transient Ischemic Attack (TIA) without pre-existing CI were included during 2007 and 2008. At one- and seven-years post-stroke, cognitive status was assessed, and categorized into dementia, mild cognitive impairment or normal. Etiologic sub-classification was based on magnetic resonance imaging (MRI) findings, CSF biomarkers and clinical cognitive profile. At 7 years, patients were offered ¹⁸F-Flut-PET, and amyloid-positivity was assessed visually and semi-quantitatively. The associations between ¹⁸F-Flut-PET standardized uptake value ratios (SUVr) and measures of neurodegeneration (medial temporal lobe atrophy (MTLA), global cortical atrophy (GCA)) and cognition (Mini-Mental State Exam (MMSE), Trail-making test A (TMT-A)) and CSF Aβ₄₂ levels were assessed using linear regression.

Results

In total, 111 patients completed 7-year follow-up, and 26 patients agreed to PET imaging, of whom 13 had CSF biomarkers from 1 year. Thirteen out of 26 patients were diagnosed with CI 7 years post-stroke, but only one had visually assessed amyloid positivity. CSF Aβ₄₂ levels at 1 year, MTA grade, GCA scale, MMSE score or TMT-A at 7 years did not correlate with ¹⁸F-Flut-PET SUVr in this cohort.

Conclusions

Amyloid binding was not common in 7-year stroke survivors diagnosed with CI. Quantitatively assessed, cortical amyloid deposition did not correlate with other measures related to neurodegeneration or cognition. Therefore, amyloid pathology may not be a key mediator of neurodegeneration 7 years post-stroke.

Trial registration

Clinicaltrials.gov (NCT00506818). July 23, 2007. Inclusion from February 2007, randomization and intervention from May 2007 and trial registration in July 2007.

Early Cognitive Impairment after Minor Stroke: Associated Factors and Functional Outcome

OBJECTIVES

Evaluation of cognitive status is not performed routinely in the acute stroke setting. This study aimed to evaluate the frequency of early cognitive impairment in patients with minor ischemic stroke, analyze the factors associated with early cognitive impairment, and assess functional outcomes.

METHODS

In this prospective study, 112 consecutive patients with acute minor ischemic stroke were enrolled. Neuroimages were assessed for semiquantitative evaluation of brain atrophy and small vessel disease (SVD) markers. Cognitive performance was measured within 5 days of onset using Montreal Cognitive Assessment (MoCA) scores. Functional outcome analyses were adjusted for demographic variables, premorbid cognitive status, education level, vascular risk factors, neuroimaging characteristics, stroke severity, and MoCA scores.

RESULTS

The median MoCA score was 22, and 63% of patients had cognitive impairment. Factors independently associated with cognitive impairment were education (odds ratios [OR], .79; confidence intervals [CI], .63-.99), smoking (OR, .26; 95%CI, .073-.89), and temporal horn atrophy (OR, 4.73; 95% CI, 1.66-13.49). Factors independently associated with poor functional outcome were total MoCA score (OR, .78; 95%CI, .62-.95) and the sum of 4 MoCA subscores (visuospatial/executive, attention, language, and orientation; OR, .72; 95%CI, .53-.92). The cutoff value of the sum of 4 MoCA subscores for predicting poor outcome was 13 points with 76.5% sensitivity and 81.1% specificity.

CONCLUSIONS

Early cognitive impairment was common after minor ischemic stroke and was associated with preexisting temporal horn atrophy but not SVD markers. The sum of 4 MoCA subscores was useful in predicting the functional outcome.

Relationship between β-amyloid protein 1-42, thyroid hormone levels and the risk of cognitive impairment after ischemic stroke

BACKGROUND

Post-stroke cognitive impairment (PSCI) is not only a common consequence of stroke but also an important factor for adverse prognosis of patients. Biochemical indicators such as blood lipids and blood pressure are affected by many factors, and the ability of evaluating the progress of patients with PSCI is insufficient. Therefore, it is necessary to find sensitive markers for predicting the progress of patients and avoiding PSCI. Recent studies have shown that β-amyloid protein 1-42 (Aβ1-42) and thyroid hormone levels are closely related to PSCI, which may be the influencing factors of PSCI, but there are few related studies.

AIM

To investigate the relationship between serum levels of Aβ and thyroid hormones in acute stage and PSCI and its predicted value.

METHODS

A total of 195 patients with acute cerebral infarction confirmed from June 2016 to January 2018 were enrolled in this study. Baseline data and serological indicators were recorded to assess cognitive function of patients. All patients were followed up for 1 year. Their cognitive functions were evaluated within 1 wk, 3 mo, 6 mo and 1 yr after stroke. At the end of follow-up, the patients were divided into PSCI and non-PSCI according to Montreal cognitive assessment score, and the relationship between biochemical indexes and the progression of PSCI was explored.

RESULTS

Compared with patients with non-PSCI, the levels of Aβ1-42, triiodothyronine (T₃) and free thyroxin were lower in the patients with PSCI. Repeated measures analysis of variance showed that the overall content of Aβ1-42 and T₃ in PSCI was also lower than that of the non-PSCI patients. Further analysis revealed that Aβ1-42 (r = 0.348), T₃ (r = 0.273) and free thyroxin (r = 0.214) were positively correlated with disease progression (P < 0.05), suggesting that these indicators have the potential to predict disease progression and outcome. Cox regression analysis showed that Aβ1-42 and T₃ were important factors of PSCI. Then stratified analysis showed that the lower the Aβ1-42 and T₃, the higher risk of PSCI in patients who were aged over 70, female and illiterate.

CONCLUSION

Aβ1-42 and T3 have the ability to predict the progression of PSCI, which is expected to be applied clinically to reduce the incidence of PSCI and improve the quality of life of patients.

Post-stroke memory deficits and barriers to seeking help: views of patients and carers

BACKGROUND

Memory and cognitive deficits post stroke are common and associated with increased risk of future dementia. Rehabilitation tends to focus on physical recovery; however, once in the community, it is unclear what happens in the longer term to the stroke-survivor with new memory difficulties.

OBJECTIVE

The aim of this qualitative study was to examine in stroke-survivors what factors influence contact with health professionals.

METHOD

Semi-structured interviews were conducted with stroke-survivors and their family carers where memory difficulties were reported at 6 months post stroke. A topic guide was used which sought to critically examine participants care experience following their stroke diagnosis. All participants were interviewed at baseline (around 6 months post stroke) and offered an interview at around 12 months post stroke. All interviews were conducted in the North East of England. All transcripts were coded and thematically analysed.

RESULTS

Ten stroke-survivors (age range 72-84 years) were interviewed alongside five carers at baseline; eight stroke-survivors and four carers agreed to a follow-up interview. Three main barriers were identified: (i) fear of a dementia diagnosis; (ii) denial or minimization of symptoms leading to adaptation and (iii) obstacles to seeking help in the community.

CONCLUSIONS

With an ageing population and increase in stroke-survival, the burden of post-stroke cognitive impairment and dementia will only increase. Stroke-survivors and their family carers in this study have identified issues that may hinder their presentation to health care professionals at a personal and organizational level. Health professionals need to be aware of these potential issues when planning services for stroke-survivors.

The views of public and clinician stakeholders on risk assessment tools for post-stroke dementia: a qualitative study

OBJECTIVE

Stroke-survivors are at increased risk of future dementia. Assessment to identify those at high risk of developing a disease using predictive scores has been utilised in different areas of medicine. A number of risk assessment scores for dementia have been developed but none has been recommended for use clinically. The aim of this qualitative study was to assess the acceptability and feasibility of using a risk assessment tool to predict post-stroke dementia.

DESIGN

Qualitative semi-structured interviews were conducted and analysed thematically. The patients and carers were offered interviews at around 6 (baseline) and 12 (follow-up) months post-stroke; clinicians were interviewed once.

SETTING

The study was conducted in the North-East of England with stroke patients, family carers and healthcare professionals in primary and secondary care.

PARTICIPANTS

Thirty-nine interviews were conducted (17 clinicians and 15 stroke patients and their carers at baseline. Twelve stroke patients and their carers were interviewed at follow-up, some interviews were conducted in pairs).

RESULTS

Barriers and facilitators to risk assessment were discussed. For the patients and carers the focus for facilitators were based on the outcomes of risk assessment for example assistance with preparation, diagnosis and for reassurance. For clinicians, facilitators were focused on the process that is, familiarity in primary care, resource availability in secondary care and collaborative care. For barriers, both groups focused on the outcome including for example, the anxiety generated from a potential diagnosis of dementia. For the patients/carers a further barrier included concerns about how it may affect their recovery. For clinicians there were concerns about limited interventions and how it would be different from standard care.

CONCLUSIONS

Risk assessment for dementia post-stroke presents challenges given the ramifications of a potential diagnosis of dementia. Attention needs to be given to how information is communicated and strategies developed to support the patients and carers if risk assessment is used.

Copolymer-1 enhances cognitive performance in young adult rats

Cognitive impairment is a dysfunction observed as a sequel of various neurodegenerative diseases, as well as a concomitant element in the elderly stages of life. In clinical settings, this malfunction is identified as mild cognitive impairment. Previous studies have suggested that cognitive impairment could be the result of a reduction in the expression of brain-derived neurotrophic factor (BDNF) and/or immune dysfunction. Copolymer-1 (Cop-1) is an FDA-approved synthetic peptide capable of inducing the activation of Th2/3 cells, which are able to release BDNF, as well as to migrate and accumulate in the brain. In this study, we evaluated the effect of Cop-1 immunization on improvement of cognition in adult rats. For this purpose, we performed four experiments. We evaluated the effect of Cop-1 immunization on learning/memory using the Morris water maze for spatial memory and autoshaping for associative memory in 3- or 6-month-old rats. BDNF concentrations at the hippocampus were determined by ELISA. Cop-1 immunization induced a significant improvement of spatial memory and associative memory in 6-month-old rats. Likewise, Cop-1 improved spatial memory and associative memory when animals were immunized at 3 months and evaluated at 6 months old. Additionally, Cop-1 induced a significant increase in BDNF levels at the hippocampus. To our knowledge, the present investigation reports the first instance of Cop-1 treatment enhancing cognitive function in normal young adult rats, suggesting that Cop-1 may be a practical therapeutic strategy potentially useful for age- or disease-related cognitive impairment.