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.

Neuroinflammation in mild respiratory COVID‑19:insights into cognitive impairment in milder cases

Severe acute respiratory syndrome coronavirus 2(SARSCoV-2)infection has been extensively shown to cause many neurological sequelae,and cognitive deficits(known as“brain fog”)may particularly and widely occur even in individuals with mild symptoms[1].Peripheral hyperinflammation as well as central nervous system(CNS)local immune responses may synergistically contribute to brain autoimmune injury.In addition to the direct neuroinvasion of SARS-CoV-2 and nonimmune effects such as severe systemic hypoxemia and vascular thrombosis,the central mechanism by which SARSCoV-2 accelerates cognitive-related symptoms may be related to immune effects[2].However,the precise neuroinflammatory mechanisms of SARS-CoV-2 infection have not been fully established.Fernández-Casta-da et al.[3]provided direct evidence and unique insights into the potential mechanism of cognitive impairment in mild respiratory coronavirus disease 2019(COVID-19)cases.

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.

Usefulness of the Japanese version of Rapid Dementia Screening Test for mild cognitive impairment in older patients with cardiovascular disease:a cross-sectional study

BACKGROUND Cognitive decline is common among older patients with cardiovascular disease(CVD) and can decrease their self-management abilities. However, the instruments for identifying mild cognitive impairment(MCI) are not always feasible in clinical practice. Therefore, this study evaluated whether MCI could be detected using the Japanese version of the Rapid Dementia Screening Test(RDST-J), which is a simple screening tool for identifying cognitive decline.METHODS This retrospective single-center study included patients who were ≥ 65 years old and hospitalized because of CVD.Patients with a pre-hospitalization diagnosis of dementia were excluded. Each patient's cognitive function had been measured at discharge using the RDST-J and the Japanese version of the Montreal Cognitive Assessment(Mo CA-J), which is a standard tool for MCI screening. The correlation between the two scores was evaluated using Spearman's rank correlation coefficient. Receiver operating characteristic(ROC) analysis was also to evaluate whether the RDST-J could identify MCI, which was defined as a Mo CA-J score of ≤ 25 points.RESULTS The study included 78 patients(mean age: 77.2 ± 8.9 years). The RDST-J and Mo CA-J scores were strongly correlated(r = 0.835, P < 0.001). The ROC analysis revealed that an RDST-J score of ≤ 9 points provided 75.4% sensitivity and 95.2% specificity for identifying MCI, with an area under the curve of 0.899(95% CI: 0.835-0.964). The same cut-off value was identified when excluding patients with a high probability of dementia(RDST-J score of ≤ 4 points).CONCLUSIONS The RDST-J may be a simple and effective tool for identifying MCI in older patients with CVD.

Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment(MCI)in T2DM.To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM,six entropies combining empirical mode decomposition(EMD),including Approximate entropy(ApEn),Sample entropy(SaEn),Fuzzy entropy(FEn),Permutation entropy(PEn),Power spectrum entropy(PsEn)and Wavelet entropy(WEn)were used in the study.A feature extraction technique based on maximization of the area under the curve(AUC)and a support vector machine(SVM)were subsequently used to for features selection and classi¯cation.Finally,Pearson's linear correlation was employed to study associations between these entropies and cognitive functions.Compared to other entropies,FEn had a higher classification accuracy,sensitivity and specificity of 68%,67.1% and 71.9%,respectively.Top 43 salient features achieved classification accuracy,sensitivity and speci¯city of 73.8%,72.3% and 77.9%,respectively.P4,T4 and C4 were the highest ranking salient electrodes.Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7,F8 and P4,and PsEn based on EMD was positively correlated to Montreal cognitive assessment(MoCA)and memory at electrode T4.In sum,FEn based on EMD in righttemporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.

AMPLITUDE OF LOW FREQUENCY FLUCTUATION AT DIFFERENT FREQUENCY BANDS IN EARLY AMNESTIC MILD COGNITIVE IMPAIRMENT: RESULTS FROM ADNI

We used resting-state functional magnetic resonance imaging(fMRI)to determine whether there are any abnormalities in different frequency bands between amplitude of low-frequency fluctuations(ALFF)and fractional ALFF(fALFF)and between 10 early amnestic mild cognitive impairment(EMCI)patients and eight normal controls participating in the Alzheimer’s Disease Neuroimaging Initiative(ADNI).We showed widespread difference in ALFF/fALFF between two frequency bands(slow-4:0.027-0.073 Hz,slow-5:0.01-0.027 Hz)in many brain areas including posterior cingulate cortex(PCC),medial prefrontal cortex(MPFC),suprasellar cistern(SC)and ambient cistern(AC).Compared to the normal controls,the EMCI patients showed increased ALFF values in PCu,cerebellum,occipital lobe and cerebellum posterior lobe in frequency band slow-4.While in frequency band slow-5,the EMCI patients showed decreased ALFF values in temporal lobe,left cerebrum and middle temporal gyrus5.Moreover,the EMCI patients showed increased fALFF values in frontal lobe and inferior frontal gyrus in band slow-5.While in frequency band slow-4,the EMCI patients showed decreased fALFF values in limbic lobe,cingulate gyrus and corpus callosum.These results demonstrated that EMCI patients had widespread abnormalities of amplitude of LFF in different frequency bands.

Comparative efficacy of various exercise interventions on cognitive function in patients with mild cognitive impairment or dementia:A systematic review and network meta-analysis

Background:Exercise is a promising nonpharmacological therapy for cognitive dysfunction,but it is unclear which type of exercise is most effective.The objective of this study was to compare and rank the effectiveness of various exercise interventions on cognitive function in patients with mild cognitive impairment(MCI)or dementia and to examine the effects of exercise on the symptoms relevant to cognitive impairment.Methods:We searched PubMed,Web of Science,Embase,Cochrane Central Register of Controlled Trials,SPORTDiscus,and PsycInfo through September 2019 and included randomized controlled trials that examined the effectiveness of exercise interventions in patients with MCI or dementia.Primary outcomes included global cognition,executive cognition,and memory cognition.Secondary outcomes included activities of daily living,neuropsychiatric symptoms,and quality of life.Pairwise analyses and network meta-analyses were performed using a random effects model.Results:A total of 73 articles from 71 trials with 5606 participants were included.All types of exercise were effective in increasing or maintaining global cognition,and resistance exercise had the highest probability of being the most effective intervention in slowing the decrease in global cognition(standard mean difference(SMD)=1.05,95%confidence interval(95%CI):0.56-1.54),executive function(SMD=0.85,95%CI:0.21-1.49),and memory function(SMD=0.32,95%CI:0.01-0.63)in patients with cognitive dysfunction.Subgroup analyses for patients with MCI revealed different effects,and multicomponent exercise was most likely to be the optimal exercise therapy for preventing the decline of global cognition(SMD=0.99,95%CI:0.44-1.54)and executive function(SMD=0.72,95%CI:0.06-1.38).However,only resistance exercise showed significant effects on memory function for patients with MCI(SMD=0.35,95%CI:0.01-0.69).Exercise interventions also showed various effects on the secondary outcomes.Conclusion:Resistance exercise has the highest probability of being the optimal exercise type for slowing cognitive declin e in patients withcognitive dysfunction,especially in patients with dementia.Multicomponent exercise tends to be most effective in protecting global cognition and executive function in patients with MCI.

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.

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.

Acupuncture at the Taixi(KI3) acupoint activates cerebral neurons in elderly patients with mild cognitive impairment

Our previous ifndings have demonstrated that acupuncture at the Taixi （KI3） acupoint in healthy youths can activate neurons in cognitive-related cerebral cortex. Here, we investigated whether acupuncture at this acupoint in elderly patients with mild cognitive impairment can also activate neurons in these regions. Resting state and task-related functional magnetic resonance imaging showed that the pinprick senstation of acupuncture at the Taixi acupoint differed signiifcantly between elderly patients with mild cognitive impairment and healthy elderly controls. Results showed that 20 brain regions were activated in both groups of participants, including the bi-lateral anterior cingulate gyrus （Brodmann areas [BA] 32, 24）, left medial frontal cortex （BA 9, 10, 11）, left cuneus （BA 19）, left middle frontal gyrus （BA 11）, left lingual gyrus （BA 18）, right medial frontal gyrus （BA 11）, bilateral inferior frontal gyrus （BA 47）, left superior frontal gyrus （BA11）, right cuneus （BA 19, 18）, right superior temporal gyrus （BA 38）, left subcallosal gyrus （BA 47）, bilateral precuneus （BA 19）, right medial frontal gyrus （BA 10）, right superior frontal （BA 11）, left cingulate gyrus （BA 32）, left precentral gyrus （BA 6）, and right fusiform gyrus （BA 19）. These results suggest that acupuncture at the Taixi acupoint in elderly patients with mild cogni-tive impairment can also activate some brain regions.

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.

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.

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.