Post-stroke Mild Cognitive Impairment Research Articles

Acupuncture of revised acupoint combination around the skull base for post-stroke mild cognitive impairment: a randomized controlled trial

To observe the clinical efficacy of acupuncture of revised acupoint combination around the skull base in treating post-stroke mild cognitive impairment (PSMCI), and preliminary explore its action mechanism. A total of 76 PSMCI patients were randomly divided into an observation group (38 cases, 4 cases dropped off) and a control group (38 cases, 3 cases dropped off, 1 case was removed). In the observation group, acupuncture of revised acupoint combination around the skull base (bilateral Fengchi [GB 20], Wangu [GB 12], Tianzhu [BL 10] and Yamen [GV 15], Baihui [GV 20]) was used for treatment. In the control group, 8 non-meridian and non-acupoint points at the distal end were selected for shallow puncture treatment. Retaining the needles of 30 min, once every other day,3 times a week for 4 weeks in both groups. The scores of Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), Barthel index (BI) and serum levels of cystatin C (Cys-C) and homocysteine (Hcy) were compared in the two groups before and after treatment, and the clinical efficacy was evaluated. After treatment, the scores of MoCA were increased compared with those before treatment in the two groups (P<0.05), and the score in the observation group was higher than that in the control group (P<0.05). The scores of MMSE and BI were increased compared with those before treatment in the observation group (P<0.05), and the score of MMSE in the observation group was higher than that in the control group (P<0.05). After treatment, the serum levels of Cys-C and Hcy were decreased compared with those before treatment in the observation group (P<0.05), and lower than those in the control group (P<0.05). After treatment, the serum level of Cys-C was increased compared with that before treatment in the control group (P<0.05). The total effective rate of the observation group was 88.2% (30/34), which was higher than 32.4% (11/34) of the control group (P<0.05). Acupuncture of revised acupoint combination around the skull base can improve cognitive function and daily living ability of PSMCI patients, which may be related to the down regulation of serum levels of Cys-C and Hcy.

Tongdu Xingshen acupuncture and moxibustion combined with cognitive training in treatment of post-stroke mild cognitive impairment: a randomized controlled trial

To compare the clinical effect of Tongdu Xingshen (promoting the governor vessel and regaining consciousness) acupuncture and moxibustion combined with cognitive training and the simple cognitive training for post-stroke mild cognitive impairment (PSMCI). Eighty-four patients with PSMCI were randomly divided into an observation group and a control group, with 42 cases in each group (3 cases in the observation group and 2 cases in the control group dropped off). The observation group was treated by Tongdu Xingshen acupuncture and moxibustion combined with cognitive training, acupuncture was given at Baihui (GV 20), Sishencong (EX-HN 1), Shenting (GV 24), etc., and moxibustion was given at Shenting (GV 24) , Baihui (GV 20), Shendao (GV 11), Fengfu (GV 16) and Xinshu (BL 15). The control group was only given cognitive training. All the above treatment was given once a day, 5 times a week, for 4 consecutive weeks. The scores of Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), activity of daily living (ADL) and stroke-specific quality of life (SS-QOL) were compared between the two groups before treatment, after treatment, 4 weeks and 12 weeks after treatment. After treatment, 4 weeks and 12 weeks after treatment, the MoCA, MMSE and SS-QOL scores of the two groups were all higher than those before treatment (P<0.05), and the ADL scores were lower than those before treatment (P<0.05). In the observation group, the MoCA and MMSE scores were higher than those of the control group after treatment, 4 weeks and 12 weeks after treatment (P<0.05), and the SS-QOL score was higher than that of the control group 12 weeks after treatment (P<0.05). Both Tongdu Xingshen acupuncture and moxibustion combined with cognitive training and simple cognitive training can improve cognitive function, daily living ability and quality of life in patients with PSMCI, and the combined therapy is superior to simple cognitive training in improving cognitive function and long-term quality of life in patients with PSMCI.

Effect of acupuncture on cerebral blood flow, serum S100β and ALP in the patients with post-stroke mild cognitive impairment in the convalescence stage: 针刺对脑卒中恢复期轻度认知功能障碍患者脑血流、血清S100β、ALP的影响

ObjectiveTo explore the clinical therapeutic effect and the impacts of acupuncture on cerebral blood flow and serum S100β and alkaline phosphatase (ALP) in the patients with post-stroke mild cognitive impairment in convalescence stage. MethodsA total of 66 cases were randomized into a control group (33 cases) and an observation group (33 cases). The conventional treatment was applied in the control group. On the base of the treatment of the control group, acupuncture was added at Shéntíng (神庭GV24), Băihuì (百会GV20), Dàzhuī (大椎GV14), Mìngmén (命门GV4), Shènshū (肾俞BL23), Shénmén (神门HT7) and Xuánzhōng (悬钟GB39) in the observation group. Before and after treatment, separately, Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE) and modified Barthel index (MBI) were assessed in the patients, peak systolic velocity (PSV) and resistance index (RI) and the levels of serum S100β and ALP were measured. At the end of treatment, the clinical therapeutic effect was evaluated in patients of the both groups. ResultsAfter treatment, the scores of MoCA, MMSE and MBI in the observation group were all higher than those in the control group. PSV in the observation group was higher than the control group, while RI, S100β and ALP were lower than those in the control group, indicating the statistical differences (all P < 0.05). The total effective rate was 93.75% in the observation group, higher than 67.74% in the control group, showing statistical difference (P < 0.05). ConclusionAcupuncture achieves the definite therapeutic effect on post-stroke mild cognitive impairment in convalescence stage. This therapy improves the cognitive function, the quality of life and cerebral blood flow in the patients and also down-regulates the levels of serum S100β and ALP. Hence, this acupuncture therapy is worthy of clinical promotion.

Apathy symptoms increase the risk of dementia conversion: a case-matching cohort study on patients with post-stroke mild cognitive impairment in China.

Apathy is a neuropsychiatric symptom frequently observed in patients with cognitive impairment. It has been found to be a predictor of conversion from mild cognitive impairment (MCI) to dementia of Alzheimer disease type. However, this association between apathy and dementia conversion has not yet been confirmed in vascular MCI, especially post-stroke MCI. The aim of this study was to evaluate whether apathy would increase the risk of dementia conversion in patients with post-stroke MCI after 6 months. A prospective multi-centre cohort study was performed in 14 clinics in seven provinces and cities of China. A total of 989 subjects were included 2 weeks to 6 months after stroke, and met the diagnostic criteria of International Working Group for MCI. Symptoms of apathy were assessed using the apathy subscale of Geriatric Depression Scale. Subjects were divided into an apathy group (n = 128) and a non-apathy group (n = 861). The primary outcome was the dementia conversion after 6 months. To eliminate potential biases, subjects were chosen from 861 non-apathy patients with similarity in seven potential predictors of cognitive impairment to match with the apathy group (n = 128) at a 1:1 ratio, as a matched non-apathy group (n = 128). The dementia conversion rate was compared between the apathy group (n = 128) and its correspondingly matched non-apathy group (n = 128), and the relative risk (RR) was calculated. The prevalence of apathy in post-stroke MCI was 12.9%. After 6 months, 5.2% of patients with post-stroke MCI converted to dementia. The dementia conversion rate of the apathy group was significantly higher than that of the non-apathy group before case-matching (17.2% vs 3.4%, P < 0.001), and also after case-matching (17.2% vs 6.3%, P < 0.001). Symptoms of apathy increased the risk of conversion from MCI to dementia (RR 2.75, 95% CI 1.272-5.947, P < 0.001). For patients with post-stroke MCI, apathy symptoms increase the risk of conversion from MCI to dementia.

Effect of DPP-4 inhibitor on elderly patients with T2DM combined with MCI

The aim of this study was to investigate the effect of dipeptidyl peptidase-4 (DPP-4) inhibitor on blood sugar level and cognitive ability in elderly patients with type 2 diabetes mellitus (T2DM) combined with post-stroke mild cognitive impairment (MCI). Thirty patients received DPP-4 inhibitor (study group), while another thirty received sulfonylurea (control group). Six months after treatment, markers regarding blood sugar were improved in both groups (all P<0.05) without intergroup differences (all P>0.05); scores regarding cognitive ability improved in the study group (both P<0.05) and were higher versus the control group (both P<0.01); the study group had higher Aβ1-42/Aβ1–40 value versus the pretreatment value (P<0.001), which differed from the control group (P<0.05); tumor necrosis factor-α and interleukin-6 concentrations decreased in both groups, while the study group had greater reductions; C-reactive protein value decreased after treatment in the study group (all P<0.05). Using DPP-4 inhibitor in elderly patients with T2DM combined with post-stroke MCI can lower blood sugar and improve cognitive ability. The mechanism may be associated with the improvement of Aβ gathering and reduction in inflammatory response.

Stroke : Highlights of Selected Articles

<i>Stroke</i> : Highlights of Selected Articles

Prevalence of Amyloid Positron Emission Tomographic Positivity in Poststroke Mild Cognitive Impairment

Mild cognitive impairment (MCI) is common after stroke and associated with poor outcome. However, the mechanisms underlying poststroke MCI (PS-MCI) are insufficiently understood. We performed amyloid-β positron emission tomography (PET) in a prospective cohort of stroke survivors to determine the role of amyloid pathology in PS-MCI. We studied 178 consecutive patients enrolled into the prospective DEDEMAS study (Determinants of Dementia After Stroke). Follow-up visits 6 months post stroke included detailed cognitive testing, standardized magnetic resonance imaging, and amyloid-β imaging using flutemetamol ((18)F) PET. MCI was defined by the modified Petersen criteria. Amyloid-positivity was assessed visually and quantitatively. Fifty-six (31%) patients agreed to undergo PET imaging. Thirty-eight (68%) patients who consented to PET imaging had PS-MCI. Visual assessment revealed amyloid PET positivity in 2 (5%) of the 38 PS-MCI patients and in 2 (11%) of the 18 cognitively healthy stroke survivors. There was no correlation between flutemetamol ((18)F) standardized uptake value ratios and cognitive scores in the 56 patients. PS-MCI patients had significant cognitive impairments on executive function (P<0.01) and memory tests (P<0.01) when compared with cognitively healthy stroke survivors (P<0.01). The prevalence of amyloid-pathology in patients with PS-MCI is not increased when compared with cognitively healthy stroke survivors and to recent estimates for cognitively healthy elderly subjects. Factors other than amyloid-pathology likely contribute to the development of PS-MCI. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01334749.

Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.

Biomedical Signal and Image Processing for Clinical Decision Support Systems

Clinicians and other health care providers are currently being expected to make an increasing number of consecutive and complex decisions based on a very large amount of complex data collected from a variety of heterogeneous sources, often produced asynchronously. This is complicated by the addition of a growing number of new diagnostic and monitoring devices further highlighting the potential for an ever-growing data stream as well as the challenge of diagnosing and treating multiple patients at one time. It can be argued that there is much hidden knowledge in various clinical data such as images, physiologic signals, and others that simply cannot be rapidly extracted by the human eye. During the last decade, the need for computational methods, in particular signal and image processing algorithms, to analyze these complex data sets and provide health care providers with recommendations and/or predictions has been further highlighted. However, due to the size and complexity of the data produced by monitoring and imaging systems, the need for more effective methods to extract knowledge from these images has not grown with the same rate. This, of course, will impede the development of clinical decision support systems. This special issue of Computational and Mathematical Methods in Medicine serves as a brief update to the current status of and advances in methods and approaches in biomedical signal and image processing methods used for clinical decision support systems. The computational methods presented in this special issue cover a wide spectrum of algorithmic approaches applied to a wide range of clinical applications. The paper by X. Li et al. provides a system to identify patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERP which may improve our assessment of some of the long-term impacts of stroke. E. Swanly et al. present a methodology to process CT images in order to spot pulmonary TB in a more effective manner. F. Li and F. Porikli give the description of their method that allows tracking of lung tumors in orthogonal X-rays. The paper by N. Saidin et al. presents a method for computer-aided detection of breast density for more accurate detection of breast cancer. This paper also presents a methodology for visualization of other breast anatomical regions on mammogram. H. Jiang et al. provide a hybrid method, based on level-set methods, for extraction of pancreas images from CT scanning with may clinical applications where CT is used for detection of potential damages to pancreas. The study by M. Jiang et al. focuses on parameter optimization for support vector regression in solving the inverse ECG problem, while H.-T. Wu et al. present the results of their study on quantification of the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. Both of these methods may have wide ranging implications for new physiologic diagnostic approaches for patients. The paper presented by Y.-W. Chen et al. focuses on a computer-aided diagnosis and quantification of cirrhotic livers based on morphological analysis and machine learning. I. Cruz-Aceves et al. present their unsupervised cardiac image segmentation that applies multiswarm active contours with a shape prior. Finally, the paper by H. Jiang et al. provides a liver segmentation method, based on snakes model and improved GrowCut algorithm that is applied to abdominal CT images. As more advanced imaging and monitoring systems are designed, it is expected that the algorithms to process the data produced by these systems need to be evolved accordingly. These novel approaches may not only help extract new knowledge that are not readily possible through current traditional interpretation but also set the stage for providing rapid predictive information assisting health care providers in making better informed decisions. As shown in the papers presented in this special issue, these changes need to address both size and complexity of the produced data. The opportunities are rich as are the challenges. Kayvan Najarian Kevin R. Ward Shahram Shirani

Post‐stroke subjective cognitive impairment is associated with acute lacunar infarcts in the basal ganglia

While recent studies have examined neuroimaging correlates of post-stroke mild cognitive impairment (MCI), no studies have examined neuroimaging correlates of post-stroke subjective cognitive impairment (SCI). Consecutive patients with magnetic resonance imaging-confirmed acute lacunar strokes at a tertiary institute were recruited for this cross-sectional study. All patients underwent cognitive testing, and those with MCI were excluded from these analyses. Two independent neuroradiologists ascertained data on the number and location of any infarcts, as well as the degree of white matter hyperintensities. Multivariate logistic regression analyses were performed to study the association between neuroimaging markers and SCI. Only variables that were significant in the univariate stage and clinically relevant potential confounders were included in multivariable analyses. Of 145 patients evaluated, 48 patients with MCI were excluded from the study. Of the remaining 97 patients, 30 patients had SCI. In multivariable analyses, only mini-mental state examination (OR 0.61; CI 0.38-0.98) and basal ganglia infarcts (OR 8.19; CI 1.18-56.6) were significant predictors of SCI. In patients with acute lacunar strokes, we find that basal ganglia infarcts are associated with SCI. As the basal ganglia have been previously shown to be involved with learning of tasks, we hypothesize that infarcts in basal ganglia may affect learning speeds thereby contributing to the development of SCI. Larger studies are needed to confirm these results.

Cognitive Dysfunction in Stroke Survivors: A Community-Based Prospective Study from Kolkata, India

The frequency of cognitive dysfunction among community stroke survivors (SS) is not known in India. This prospective study investigated the prevalence of poststroke mild cognitive impairment (psMCI) and poststroke dementia (psDem), the annual progression rate to dementia, and pertinent risk factors in a sample population of SS in Kolkata, India between September 2006 and July 2010. From a community-based stroke registry, 281 SS were assessed at baseline year after excluding attrition due to various causes. Validated cognitive tools were applied by trained field workers under supervision of a neuropsychologist. The assessment was repeated annually for consecutive three years. The mean follow-up period was 1.89 years (range, 1-3 years). The period prevalence rate of psDem was 13.88% (95% confidence interval [CI], 9.91%-18.90%) at baseline, and the average annual progression rate to dementia was 3.53% (95% CI, 2.09%-5.58%). Compared with subjects without dementia, those with psDem were significantly older at first-ever stroke and more likely to have cortical atrophy. The period prevalence rate of psMCI was 6.05% (95% CI, 1.45%-13.64%) at baseline, and 10.6% (95% CI, 4.57%-20.88%) of these subjects converted to psDem annually. Survival analysis of psDem patients showed a greater risk of death in psDem SS as compared to nondemented SS (hazard ratio, 2.65; 95% CI, 1.72-6.15). Our data suggest that the overall prevalence of psDem is higher than that of psMCI, possibly related to nonexclusion of prestroke dementia, but that the average annual progression rate of SS to psDem and that of psMCI to psDem are comparable. Older age at first-ever stroke and cortical atrophy are associated with increased risk for dementia among SS.

The determinants and longitudinal course of post-stroke mild cognitive impairment

While post-stroke dementia has been extensively investigated, the large number of patients with mild cognitive impairment (MCI) following stroke has received less attention, and reports on the longitudinal course of such impairment are inconsistent in their findings. We examined patients with MCI (n = 45) or no cognitive impairment (NCI) (n = 59), based on consensus criteria following detailed neuropsychological assessments and magnetic resonance imaging (MRI) scans, and compared them with healthy control subjects (n = 84), all of whom were assessed at two time points, 3 years apart. The MCI at baseline in this group was judged to be vascular in etiology (vaMCI). Incident dementia was diagnosed in 24.4% of vaMCI and 8.5% of NCI subjects and no control subjects over 3 years, giving a rate of conversion of approximately 8% per year in post-stroke vaMCI. The vaMCI group showed greater decline in logical memory than the NCI group. Within the vaMCI group, those who developed dementia had great decline in language and executive function. Compared with NCI patients, those with vaMCI had more vascular risk factors and more white matter hyperintensities on MRI at baseline, but did not differ in their brain or hippocampal volumes. Neither MRI volumetric measures nor interval cerebrovascular events predicted decline in function. The major determinant of decline and categorical transition was impaired performance at baseline, suggesting that those with mild impairment post-stroke are more vulnerable to subsequent decline.

The Association between Psychiatric and Cognitive Symptoms after Stroke: A Prospective Study

Background: Stroke patients often have neuropsychiatric symptoms and cognitive deficits. The aim of this study was to investigate whether stroke patients with psychiatric symptoms are at increased risk of developing cognitive deficits. Methods: Cognitive function (assessed with a neuropsychological test battery) and presence of psychiatric symptoms (assessed with the 90-item Symptom Checklist) were evaluated at 1, 6, 12 and 24 months after stroke. Results: At baseline, 156 patients entered the study, 15 had a diagnosis of vascular dementia, 113 one of post-stroke mild cognitive impairment. Patients with psychiatric symptoms were found to be at increased risk of being diagnosed as having vascular dementia at baseline (OR = 6.9, CI = 1.3–36.8) and showed more decline on cognitive function 6 months after stroke. Conclusions: Patients with psychiatric symptoms after stroke are at increased risk of cognitive deficits and decline in cognitive functioning.

Predictors of reversible mild cognitive impairment after stroke: a 2-year follow-up study

Many studies have investigated mild cognitive impairment (MCI) in the context of prodromal dementia, but few have investigated recovery from MCI. The aim of this study was to determine the prevalence of reversible MCI after stroke and to identify factors related to recovery. Methods One hundred and eighteen patients with a first ever cerebral stroke were followed up for 2 years. Neuropsychological assessment was performed at 1, 6, 12, and 24 months poststroke. Possible predictors of reversible MCI were demographic variables, baseline MMSE scores, presence of stroke risk factors, and CT variables. Poststroke MCI was diagnosed when there was a deficit in at least one cognitive domain, without their being demented. Recovery was considered when MCI was no longer present. Results Twenty-four (20.3%) patients were classified as having permanent reversible MCI and were compared with patients without recovery. Most patients recovered from MCI between the first and second assessments (19.7% versus 13.1% and 2.0% later on). Higher baseline MMSE scores and female sex were independent predictors of recovery (OR High MMSE=9.9; OR female sex=2.8). Neither stroke-related risk factors nor CT variables were predictors of favorable outcome. Conclusion About 20% of patients with poststroke MCI recover from MCI. Higher MMSE scores at baseline and female sex are independent predictors of this recovery.

Cognitive Functioning after Stroke: A One-Year Follow-Up Study

Cognitive disorders after stroke are one of the main causes of disability in daily activities. The main aim of this study was to investigate the frequency of post-stroke dementia, post-stroke mild cognitive impairment (MCI) and post-stroke amnestic MCI at different times after first-ever stroke; 196 patients were included in the study. In addition, cognitive disorders and their clinical course were studied. Frequency of post-stroke dementia was about 10% at all evaluation times; most patients had post-stroke MCI. Of the cognitive functions investigated, mental speed and calculation were most frequently affected. Performance on almost all cognitive tests was improved 6 and 12 months after stroke. Thus, while the frequency of post-stroke dementia is low, the frequency of post-stroke MCI is high, but improvement of cognitive function is possible.