Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

Trends in the Application of Dyadic Coping in Older Adults with Mild Cognitive Impairment and Their Spouses

Dyadic coping plays an important role in older adults with mild cognitive impairment and their spouses. Significant correlations were found between dyadic coping and self-efficacy, anxiety and depression, marital quality, and quality of life in elderly patients with mild cognitive impairment and their spouses, and there were gender differences, with a 36.1% [P = 0.028, OR = 0.639, 95% CI (0.429, 0.952)] and 54% [P = 0.004, OR = 0.460, 95% CI (0.269, 0.785)] reduction in the risk of MCI and dementia for older men aged 65 - 69 years with a spouse and for those aged 80 years and older with a spouse, respectively. In contrast, there was no significant difference in the association between having or not having a spouse and developing MCI and dementia in older women (all P > 0.05). Psychosocial interventions, skills interventions, and exercise from the perspective of dyadic relationships were effective in improving the physical and mental health of older adults with mild cognitive impairment and their spouses. However, there is a lack of specific intervention programs for dyadic relationships in the local cultural context as an entry point. Therefore, it is necessary to draw on internal and external relevant literature to treat both partners as a whole for intervention, provide personalized social, cognitive and motor therapy for patients and promote the integration and participation of caregivers, help patients and spouses to improve the sense of well-being and intimacy, reduce the burden of caregivers, and build a dyadic coping intervention program suitable for elderly patients with mild cognitive impairment in China. The current article aims to provide a conceptual review focusing on dyadic coping care to inform the development of a dyadic intervention program suitable for older adults with mild cognitive impairment in China. This review outlines the theoretical concepts, assessment tools, current state of research, and intervention methods for mild cognitive impairment and dyadic coping.

Estimating High-Order Functional Connectivity Networks for Mild Cognitive Impairment Identification Based on Topological Structure

Functional connectivity networks (FCNs) are important in the diagnosis of neurological diseases and the understanding of brain tissue patterns. Recently, many methods, such as Pearson’s correlation (PC), Sparse representation (SR), and Sparse low-rank representation have been proposed to estimate FCNs. Despite their popularity, they only capture the low-order connections of the brain regions, failing to encode more complex relationships (i.e. , high-order relationships). Although researchers have proposed high-order methods, like PC + PC and SR + SR, aiming to build FCNs that can reflect more real state of the brain. However, such methods only consider the relationships between brain regions during the FCN construction process, neglecting the potential shared topological structure information between FCNs of different subjects. In addition, the low-order relationships are always neglected during the construction of high-order FCNs. To address these issues, in this paper we proposed a novel method, namely Ho-FCNTops, towards estimating high-order FCNs based on brain topological structure. Specifically, inspired by the Group-constrained sparse representation (GSR), we first introduced a prior assumption that all subjects share the same topological structure in the construction of the low-order FCNs. Subsequently, we employed the Correlation-reserved embedding (COPE) to eliminate noise and redundancy from the low-order FCNs. Meanwhile, we retained the original low-order relationships during the embedding process to obtain new node representations. Finally, we utilized the SR method on the obtained new node representations to construct the Ho-FCNTops required for disease identification. To validate the effectiveness of the proposed method, experiments were conducted on 137 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to identify Mild Cognitive Impairment (MCI) patients from the normal controls. The experimental results demonstrate superior performance compared to baseline methods.

Proteomics of serum exosomes identified fibulin-1 as a novel biomarker for mild cognitive impairment

Mild cognitive impairment(MCI)is a prodrome of Alzheimer’s disease pathology.Cognitive impairment patients often have a delayed diagnosis because there are no early symptoms or conventional diagnostic methods.Exosomes play a vital role in cell-to-cell communications and can act as promising biomarkers in diagnosing diseases.This study was designed to identify serum exosomal candidate proteins that may play roles in diagnosing MCI.Mass spectrometry coupled with tandem mass tag approach-based non-targeted proteomics was used to show the differentially expressed proteins in exosomes between MCI patients and healthy controls,and these differential proteins were validated using immunoblot and enzyme-linked immunosorbent assays.Correlation of cognitive performance with the serum exosomal protein level was determined.Nanoparticle tracking analysis suggested that there was a higher serum exosome concentration and smaller exosome diameter in individuals with MCI compared with healthy controls.We identified 69 exosomal proteins that were differentially expressed between MCI patients and healthy controls using mass spectrometry analysis.Thirty-nine exosomal proteins were upregulated in MCI patients compared with those in control patients.Exosomal fibulin-1,with an area under the curve value of 0.81,may be a biomarker for an MCI diagnosis.The exosomal protein signature from MCI patients reflected the cell adhesion molecule category.In particular,higher exosomal fibulin-1 levels correlated with lower cognitive performance.Thus,this study revealed that exosomal fibulin-1 is a promising biomarker for diagnosing MCI.

Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

Receded near point of convergence as a predictor of mild cognitive impairment in the general geriatric population:results from a population-based study

AIM:To investigate the relationship between near point of convergence(NPC)and mild cognitive impairment(MCI)in the general elderly population.METHODS:The present report is a part of the Tehran Geriatric Eye Study(TGES):a population-based crosssectional study conducted on individuals 60 years of age and above living in Tehran,Iran using the multi-stage stratified random cluster sampling method.Cognitive status was assessed using the Persian version of the Mini-Mental State Examination(MMSE).All study participants underwent complete ocular examination including measurement of uncorrected and best-corrected visual acuity,objective and subjective refraction,cover testing,NPC measurement,and slit-lamp biomicroscopy.RESULTS:The data of 1190 individuals were analyzed for this report.The mean age of the participants analyzed was 66.82±5.42(60-92y)and 728(61.2%)of them were female.Patients with MCI had a significantly more receded NPC compared to subjects with normal cognitive status(10.89±3.58 vs 7.76±2.71 cm,P<0.001).In the multivariable logistic regression model and in the presence of confounding variables,a receded NPC was statistically significantly associated with an increased risk of MCI(odds ratio:1.334,95%confidence interval:1.263 to 1.410,P<0.001).According to receiver operating characteristic(ROC)analysis,a cut point NPC>8.5 cm(area under the curve:0.764,P<0.001)could predict the presence of MCI with a sensitivity and specificity of 70.9%and 69.5%,respectively.CONCLUSION:A receded NPC can be clinically proposed as a predictor of MCI in older adults.It is recommended that elderly with a receded NPC>8.50 cm undergo detailed cognitive screening for a definite diagnosis of MCI.In this case,the necessary interventions can be carried out to slow down MCI progression to dementia.

The Effect of Sleep and Cognition Enhancement Multimodal Intervention for Mild Cognitive Impairment with Sleep Disturbance in the Community-Dwelling Elderly

Dementia prevalence has soared due to population aging. In Mild Cognitive Impairment (MCI) as a pre-dementia stage, sleepdisturbances have raised much interest as a factor in a bidirectional relationship with cognitive decline. Thus, this studydeveloped the Sleep and Cognition Enhancement Multimodal Intervention (SCEMI) based on Lazarus’ multimodal approachand conducted a randomized controlled experiment to investigate the effects of the novel program on sleep and cognition inMCI elderly. The participants were 55 MCI elderly with sleep disturbances at two dementia care centers located in S-city,Gyeonggi-do, South Korea (n = 25 in the experimental group and n = 30 in the control group). The study period was fromNovember 01 to December 27, 2022. The experimental group received 8 sessions of SCEMI, 60 min per session once a week.The control group received general education and guidance using a simplified booklet on the sleep and cognitive improvement.For data collection, a self-reported questionnaire was used to investigate sleep quality, presleep arousal, cognitive function,stress, and depression. The results showed that, compared to the control group, the experimental group had significantlyimproved across all variables: sleep quality (U = 109.50, p < 0.001), presleep arousal (U = 11.50, p < 0.001), cognitive function(U = 72.00, p < 0.001), stress (U = 139.00, p < 0.001), and depression (U = 231.50, p = 0.015). Thus, the SCEMI appears topositively affect symptomatic improvement and delays the progression to dementia as an integrated intervention to enhancesleep and cognition in community-dwelling MCI elderly with sleep disturbances.

A Novel Computerized Cognitive Test for the Detection of Mild Cognitive Impairment and Its Association with Neurodegeneration in Alzheimer’s Disease Prone Brain Regions

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.

Cardiovascular risk factors among older persons with cognitive frailty in middle income country

BACKGROUND Cognitive frailty,characterized by the coexistence of cognitive impairment and physical frailty,represents a multifaceted challenge in the aging population.The role of cardiovascular risk factors in this complex interplay is not yet fully understood.AIM To investigate the relationships between cardiovascular risk factors and older persons with cognitive frailty by pooling data from two cohorts of studies in Malaysia.METHODS A comprehensive approach was employed,with a total of 512 communitydwelling older persons aged 60 years and above,involving two cohorts of older persons from previous studies.Datasets related to cardiovascular risks,namely sociodemographic factors,and cardiovascular risk factors,including hypertension,diabetes,hypercholesterolemia,anthropometric characteristics and biochemical profiles,were pooled for analysis.Cognitive frailty was defined based on the Clinical Dementia Rating scale and Fried frailty score.Cardiovascular risk was determined using Framingham risk score.Statistical analyses were conducted using SPSS version 21.RESULTS Of the study participants,46.3%exhibited cognitive frailty.Cardiovascular risk factors including hypertension(OR:1.60;95%CI:1.12-2.30),low fat-free mass(OR:0.96;95%CI:0.94-0.98),high percentage body fat(OR:1.04;95%CI:1.02-1.06),high waist circumference(OR:1.02;95%CI:1.01-1.04),high fasting blood glucose(OR:1.64;95%CI:1.11-2.43),high Framingham risk score(OR:1.65;95%CI:1.17-2.31),together with sociodemographic factors,i.e.,being single(OR 3.38;95%CI:2.26-5.05)and low household income(OR 2.18;95%CI:1.44-3.30)were found to be associated with cognitive frailty.CONCLUSION Cardiovascular-risk specific risk factors and sociodemographic factors were associated with risk of cognitive frailty,a prodromal stage of dementia.Early identification and management of cardiovascular risk factors,particularly among specific group of the population might mitigate the risk of cognitive frailty,hence preventing dementia.

Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study

Mild cognitive impairment(MCI)is a precursor to Alzheimer’s disease.It is imperative to develop a proper treatment for this neurological disease in the aging society.This observational study investigated the effects of acupuncture therapy on MCI patients.Eleven healthy individuals and eleven MCI patients were recruited for this study.Oxy-and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy.Before acupuncture treatment,working-memory experiments were conducted for healthy control(HC)and MCI groups(MCI-0),followed by 24 sessions of acupuncture for the MCI group.The acupuncture sessions were initially carried out for 6 weeks(two sessions per week),after which experiments were performed again on the MCI group(MCI-1).This was followed by another set of acupuncture sessions that also lasted for 6 weeks,after which the experiments were repeated on the MCI group(MCI-2).Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed.The highest classification accuracies obtained using binary connectivity maps were 85.7%HC vs.MCI-0,69.5%HC vs.MCI-1,and 61.69%HC vs.MCI-2.The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum(i.e,max(5:28 seconds))values were 60.6%HC vs.MCI-0,56.9%HC vs.MCI-1,and 56.4%HC vs.MCI-2.The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture.This was reflected by a reduction in the classification accuracy after the therapy,indicating that the patients’brain responses improved and became comparable to those of healthy subjects.A similar trend was reflected in the classification using the image feature.These results indicate that acupuncture can be used for the treatment of MCI patients.

Inter- and intra-hemispheric EEG coherence in patients with mild cognitive impairment at rest and during working memory task

Objective： To assess functional relationship by calculating inter- and intra-hemispheric electroencephalography （EEG） coherence at rest and during a working memory task of patients with mild cognitive impairment （MCI）. Methods： The sample consisted of 69 subjects： 35 patients （n = 17 males, n = 18 females; 52-71 years old） and 34 normal controls （n = 17 males, n = 17 females; 51 -63 years old）. Mini-mental state examination （MMSE） of two groups revealed that the scores of MCI patients did not differ significantly from those of normal controls （P〉0.05）. In EEG recording, subjects were performed at rest and during working memory task. EEG signals from F3-F4, C3-C4, P3-P4, T5-T6 and O1-O2 electrode pairs are resulted from the inter-hemispheric action, and EEG signals from F3-C3, F4-C4, C3-P3, C4-P4, P3-O1, P4-O2, T5-C3, T6-C4, T5-P3 and T6-P4 electrode pairs are resulted from the intra-hemispheric action for delta （1.0-3.5 Hz）, theta （4.0-7.5 Hz）, alpha-1 （8.0-10.0 Hz）, alpha-2 （10.5-13.0 Hz）, beta-1 （13.5-18.0 Hz） and beta-2 （18.5-30.0 Hz） frequency bands. The influence of inter- and intra-hemispheric coherence on EEG activity with eyes closed was examined using fast Fourier transformation from the 16 sampled channels. Results： During working memory tasks, the inter- and intra-hemispheric EEG coherences in all bands were significantly higher in the MCI group in comparison with those in the control group （P〈0.05）. However, there was no significant difference in inter- and intra-hemispheric EEG coherences between two groups at rest. Conclusion： Experimental results comprise evidence that MCI patients have higher degree of functional connectivity between hemispheres and in hemispheres during working condition, It suggests that MCI may be associated with compensatory processes during working memory tasks between hemispheres and in hemispheres. Moreover, failure of normal cortical connections may exist in MCI patients.

Study of virtual reality for mild cognitive impairment:A bibliometric analysis using CiteSpace

Objectives Non-invasive and low-cost virtual reality(VR)technology is important for early evaluation and intervention in mild cognitive impairment(MCI).This study aimed to demonstrate the current status of overseas and domestic research as well as the focus and frontier of VR technology among individuals with MCI through a bibliometric analysis.Methods Studies from the core collection of Web of Science™between 1995 and 2020 were used;furthermore,CiteSpace 5.7 R3 was utilized to analyse information on authors/cited authors,keywords,burst words,and cited references.Results In total,230 publications were identified.Most studies were published in the USA(45 publications)and Italy(41 publications),where Guiseppe Riva ranks first(14 publications),and Tarnanas I is the author with the highest centrality(0.44).The hot topics in VR applications in the MCI population are‘physical activity,’‘people,’‘single-blind,’‘disease,’‘walking,’‘technology,’‘working memory,’and‘risk’in recent years.The keyword‘mild cognitive impairment’has attracted extensive attention since 2012,showing the strongest citation outbreak(8.28).The clustering results of the literature show the research types and emerging trends,including‘exergame,’‘serious games,’‘spatial navigation,’‘activities of daily living,’‘exercise,’‘enriched environment’and‘wayfinding.‘Conclusions Cognitive assessment and nonpharmacological intervention research on patients with MCI have become the focus of dementia prevention in recent years.Virtual technology,combined with traditional methods such as exercise therapy,provides new ideas for innovative cognitive evaluation and cognitive intervention.

Modulatory effects of acupuncture on brain networks in mild cognitive impairment patients

Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong（EX-HN1）, Yintang（EX-HN3）, Neiguan（PC6）, Taixi（KI3）, Fenglong（ST40）, and Taichong（LR3）, or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.

Brain imaging of mild cognitive impairment and Alzheimer's disease

The rapidly increasing prevalence of cognitive impairment and Alzheimer＇s disease has the potential to create a major worldwide healthcare crisis. Structural MRI studies in patients with Alzheimer＇s disease and mild cognitive impairment are currently attracting considerable interest. It is extremely important to study early structural and metabolic changes, such as those in the hippocampus, entorhinal cortex, and gray matter structures in the medial temporal lobe, to allow the early detection of mild cognitive impairment and AIzheimer＇s disease. The microstructural integrity of white matter can be studied with diffusion tensor imaging. Increased mean diffusivity and decreased fractional anisotropy are found in subjects with white matter damage. Functional imaging studies with positron emission tomography tracer compounds enable detection of amyloid plaques in the living brain in patients with Alzheimer＇s disease. In this review, we will focus on key findings from brain imaging studies in mild cognitive impairment and Alzheimer＇s disease, including structural brain changes studied with MRI and white matter changes seen with diffusion tensor imaging, and other specific imaging methodologies will also be discussed.

Individual identification using multi-metric of DTI in Alzheimer's disease and mild cognitive impairment

Accurate identification of Alzheimer＇s disease （AD） and mild cognitive impairment （MCI） is crucial so as to improve diagnosis techniques and to better understand the neurodegenerative process. In this work, we aim to apply the machine learning method to individual identification and identify the discriminate features associated with AD and MCI. Diffusion tensor imaging scans of 48 patients with AD, 39 patients with late MCI, 75 patients with early MCI, and 51 age-matched healthy controls （HCs） are acquired from the Alzheimer＇s Disease Neuroimaging Initiative database. In addition to the common fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity metrics, there are two novel metrics, named local diffusion homogeneity that used Spearman＇s rank correlation coefficient and Kendall＇s coefficient concordance, which are taken as classification metrics. The recursive feature elimination method for support vector machine （SVM） and logistic regression （LR） combined with leave-one-out cross validation are applied to determine the optimal feature dimensions. Then the SVM and LR methods perform the classification process and compare the classification performance. The results show that not only can the multi-type combined metrics obtain higher accuracy than the single metric, but also the SVM classifier with multi-type combined metrics has better classification performance than the LR classifier. Statistically, the average accuracy of the combined metric is more than 92% for all between-group comparisons of SVM classifier. In addition to the high recognition rate, significant differences are found in the statistical analysis of cognitive scores between groups. We further execute the permutation test, receiver operating characteristic curves, and area under the curve to validate the robustness of the classifiers, and indicate that the SVM classifier is more stable and efficient than the LR classifier. Finally, the uncinated fasciculus, cingulum, corpus callosum, corona radiate, external capsule, and internal capsule have been regarded as the most important white matter tracts to identify AD, MCI, and HC. Our findings reveal a guidance role for machine-learning based image analysis on clinical diagnosis.

Study on EEG power and coherence in patients with mild cognitive impairment during working memory task

To investigate the features of electroencephalography （EEG） power and coherence at rest and during a working memory task of patients with mild cognitive impairment （MCI）. Thirty-five patients （17 males, 18 females; 52-71 years old） and 34 sex- and age-matched controls （17 males, 17 females; 51-63 years old） were recruited in the present study. Mini-Mental State Examination （MMSE） of 35 patients with MCI and 34 normal controls revealed that the scores of MCI patients did not differ significantly from those of normal controls （P〉0.05）. Then, EEGs at rest and during working memory task with three levels of working memory load were recorded. The EEG power was computed over 10 channels： fight and left frontal （F3, F4）, central （C3, C4）, parietal （P3, P4）, temporal （T5, T6） and occipital （O1, O2）; inter-hemispheric coherences were computed from five electrode pairs of F3-F4, C3-C4, P3-P4, T5-T6 and O1-O2 for delta （1.0-3.5 Hz）, theta （4.0-7.5 Hz）, alpha-1 （8.0-10.0 Hz）, alpha-2 （10.5-13.0 Hz）, beta-1 （13.5-18.0 Hz） and beta-2 （18.5-30.0 Hz） frequency bands. All values of the EEG power of MCI patients were found to be higher than those of normal controls at rest and during working memory tasks. Furthermore, the values of EEG power in the theta, alpha-1, alpha-2 and beta-1 bands of patients with MCI were significantly high （P〈0.05） in comparison with those of normal controls. Correlation analysis indicated a significant negative correlation between the EEG powers and MMSE scores. In addition, during working memory tasks, the EEG coherences in all bands were significantly higher in the MCI group in comparison with those in the control group （P〈0.05）. However, there was no significant difference in EEG coherences between two groups at rest. These findings comprise evidence that MCI patients have higher EEG power at rest, and higher EEG power and coherence during working conditions. It suggests that MCI may be associated with compensatory processes at rest and during working memory tasks. Moreover, failure of normal cortical connections may be exist in MCI patients.

Cognitive Training in Older Adults with Mild Cognitive Impairment

Objective We investigated the feasibility and efficacy of cognitive training for older adults in rura settings and with low education levels, who have mild cognitive impairment （MCl）. Methods Forty-five older adults （ages 〉65 years） with MCI were assigned to treatment or control groups, at a 2：1 ratio. Cognitive training occurred in the treatment group for 2 months. The cognitive abilities of the participants were assessed at pre-training, metaphase, and post-training time points, using the Mini-Mental State Examination （MMSE）, Montreal Cognitive Assessment （MoCA）, Loewenstein Occupational Therapy Cognitive Assessment （LOTCA）, and Hamilton Depression Scale （HAM-D）. Results Following training, cognitive abilities improved in the treatment group, based on the total scores of all 4 measures, as well as specifically on the MoCA and LOTCA. There were differences in the main effects of group and time point on some subscales, but these differences had little, if any, effect on the overall analyses. Conclusion The present study demonstrated that cognitive training has beneficial effects on attention, language, orientation, visual perception, organization of visual movement, and logical questioning in patients with MCI. Furthermore, the observed effects are long-term changes.

Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

BACKGROUND Large-scale functional connectivity(LSFC)patterns in the brain have unique intrinsic characteristics.Abnormal LSFC patterns have been found in patients with dementia,as well as in those with mild cognitive impairment(MCI),and these patterns predicted their cognitive performance.It has been reported that patients with type 2 diabetes mellitus(T2DM)may develop MCI that could progress to dementia.We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM,using connectome-based predictive modeling(CPM)and a support vector machine.AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls.Cognitive function was assessed using the Montreal Cognitive Assessment(MoCA).Patients with T2DM were divided into two groups,according to the presence(T2DM-C;n=16)or absence(T2DM-NC;n=26)of MCI.Brain regions were marked using Harvard Oxford(HOA-112),automated anatomical labeling(AAL-116),and 264-region functional(Power-264)atlases.LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique.Subsequently,we used a support vector machine based on LSFC patterns for among-group differentiation.The area under the receiver operating characteristic curve determined the appearance of the classification.RESULTS CPM could predict the MoCA scores in patients with T2DM(Pearson’s correlation coefficient between predicted and actual MoCA scores,r=0.32,P=0.0066[HOA-112 atlas];r=0.32,P=0.0078[AAL-116 atlas];r=0.42,P=0.0038[Power-264 atlas]),indicating that LSFC patterns represent cognition-level measures in these patients.Positive(anti-correlated)LSFC networks based on the Power-264 atlas showed the best predictive performance;moreover,we observed new brain regions of interest associated with T2DM-related cognition.The area under the receiver operating characteristic curve values(T2DM-NC group vs.T2DM-C group)were 0.65-0.70,with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value(0.70).Most discriminative and attractive LSFCs were related to the default mode network,limbic system,and basal ganglia.CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Association of chronic obstructive pulmonary disease with mild cognitive impairment and dementia risk:A systematic review and meta-analysis

BACKGROUND Chronic obstructive pulmonary disease(COPD)is a common public health issue that has been linked to cognitive dysfunction.AIM To investigate the relationship between COPD and a risk of mild cognitive impairment(MCI)and dementia.METHODS A comprehensive literature search of the PubMed,Embase,Google Scholar,and Cochrane Library electronic databases was conducted.Pooled odds ratios(OR)and mean differences(MD)with 95%confidence intervals(CIs)were calculated using a random or fixed effects model.Studies that met the inclusion criteria were assessed for quality using the Newcastle Ottawa Scale.RESULTS Twenty-seven studies met all the inclusion criteria.Meta-analysis yielded a strong association between COPD and increased risk of MCI incidence(OR=2.11,95%CI:1.32-3.38).It also revealed a borderline trend for an increased dementia risk in COPD patients(OR=1.16,95%CI:0.98-1.37).Pooled hazard ratios(HR)using adjusted confounders also showed a higher incidence of MCI(HR=1.22,95%CI:-1.18 to-1.27)and dementia(HR=1.32,95%CI:-1.22 to-1.43)in COPD patients.A significant lower mini-mental state examination score in COPD patients was noted(MD=-1.68,95%CI:-2.66 to-0.71).CONCLUSION Our findings revealed an elevated risk for the occurrence of MCI and dementia in COPD patients.Proper clinical management and attention are required to prevent and control MCI and dementia incidence in COPD patients.

Bibliometric analysis of papers on mild cognitive impairment nursing in China

Purpose: To supply further references by analyzing the status of research on mild cognitive impairment nursing in China.Methods: Papers on mild cognitive impairment nursing published between 2005 and 2014 were collected from China National Knowledge Infrastructure,Wanfang Data,and China Biological Medicine database,while their publication dates,journals,and types were subjected to a bibliometric analysis using NoteExpress and Excel.Results: A total of 68 papers were collected from the selected databases.The number of papers on mild cognitive impairment nursing increased annually.The selected papers were published in 44 journals,55.88% of them were published in core journals,35.29% received funding support,35.29% were published by hospitals affiliated to colleges,47.06% were published by other local hospitals,author collaboration is 2.66,and 66.18% showed co-authorship.These papers covered a wide range of topics,but were only conducted based on clinical interventions.Around 29.41% of these papers had a citation frequency of over 5,the highest citation frequency was 29,and the highest h-index was 23.Conclusion: Beijing and Shanghai established core author groups for mild cognitive impairment nursing research.These studies should focus on the community and psychological nursing of such impairment.Targeted nursing interventions on different types of mild cognitive impairment should be adopted,new avenues for research should be opened,and various research methods should be introduced.