Mild Cognitive Impairment Patients Research Articles

Association of soluble TREM2 with Alzheimer's disease and mild cognitive impairment: a systematic review and meta-analysis.

Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is a potential neuroinflammatory biomarker linked to the pathogenesis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Previous studies have produced inconsistent results regarding sTREM2 levels in various clinical stages of AD. This study aims to establish the correlation between sTREM2 levels and AD progression through a meta-analysis of sTREM2 levels in cerebrospinal fluid (CSF) and blood. Comprehensive searches were conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify observational studies reporting CSF and blood sTREM2 levels in AD patients, MCI patients, and healthy controls. A random effects meta-analysis was used to calculate the standardized mean difference (SMD) and 95% confidence intervals (CIs). Thirty-six observational studies involving 3,016 AD patients, 3,533 MCI patients, and 4,510 healthy controls were included. CSF sTREM2 levels were significantly higher in both the AD [SMD = 0.28, 95% CI (0.15, 0.41)] and MCI groups [SMD = 0.30, 95% CI (0.13, 0.47)] compared to the healthy control group. However, no significant differences in expression were detected between the AD and MCI groups [SMD = 0.09, 95% CI (-0.09, 0.26)]. Furthermore, increased plasma sTREM2 levels were associated with a higher risk of AD [SMD = 0.42, 95% CI (0.01, 0.83)]. CSF sTREM2 levels are positively associated with an increased risk of AD and MCI. Plasma sTREM2 levels were notably higher in the AD group than in the control group and may serve as a promising biomarker for diagnosing AD. However, sTREM2 levels are not effective for distinguishing between different disease stages of AD. Further investigations are needed to explore the longitudinal changes in sTREM2 levels, particularly plasma sTREM2 levels, during AD progression. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024514593.

Visual Event-Related Potentials under External Emotional Stimuli as Early Signs for Mild Cognitive Impairment.

Alzheimer's disease (AD) is a neurodegenerative disorder featured by progressive cognitive decline, which manifests in severe impairment of memory, attention, emotional processing and daily activities, leading to significant disability and social burden. Investigation on Mild Cognitive Impairment (MCI), the prodromal and transitional stage between normal aging and AD, serves as a key in diagnosing and slowing down the progression of AD. Numerous effects have been made up to date, however, the attentional mechanisms under different external emotion stimuli in MCI and AD are still unexplored in deep. To further explore the attentional mechanisms under different external emotion stimuli in both MCI and AD patients. In 51 healthy volunteers (Controls, 24 males and 27 females), 52 MCI (19 males and 33 females), and 47 AD (15 males and 32 females) patients, we administered the visual oddball event-related potentials (ERPs) under three types of external emotional stimuli: Neutral, Happiness and Sadness, in which the components N1, P2, N2 and P3 as well as the abnormal cortical activations corresponding to the significant ERP differences in the three groups were observed. Under all three external emotions, in AD patients, N2 and P3 latencies were significantly prolonged compared to both Controls and MCI. In addition, under Happiness, in MCI, P3 latencies were significantly delayed compared to Controls. Meanwhile, under both Happiness and Sadness, in AD patients, P3 amplitudes were significantly decreased compared to Controls and MCI, respectively. During N2 time window, under Neutral emotion, significant hypoactivation in the right superior temporal gyrus was found in AD patients compared to Controls, and under Happiness, the activation of the right inferior frontal gyrus was significantly attenuated in MCI compared to Controls. Under Sadness, in AD patients, the activation of the right superior frontal gyrus was significantly decreased compared to MCI. During P3 time window, under both Happiness and Sadness, when AD patients compared to MCI, the significantly attenuated activations were located in the right fusiform gyrus and the right middle occipital gyrus, respectively. Our results demonstrated visual attentional deficits under external emotional stimuli in both MCI and AD patients, highlighting the function of Happiness for early detecting MCI, in which the P3 latency and the hypoactivation of right inferior frontal gyrus during N2 time window can be early signs. The current study sheds further light of attentional mechanisms in MCI and AD patients, and indicates the value of emotional processing in the early detection of cognitive dysfunction.

Analysis of current situation and influencing factors of cognitive dysfunction associated with type 2 diabetes and follow-up study on treatment effectiveness.

Many studies have explored the risk factors associated with cognitive impairment in patients with Type 2 diabetes mellitus (T2DM). However, research on determining the optimal threshold for these risk factors and comparative studies on the therapeutic effects of insulin and metformin is limited. This study aims to establish the optimal threshold for cognitive impairment risk factors in T2DM patients and compare the efficacy of insulin and metformin in treating mild cognitive impairment (MCI). A total of 308 patients with T2DM were included. The optimal threshold for cognitive impairment risk factors was determined using receiver operating characteristic curve and binary logistic regression models. MCI patients were divided into three groups: insulin, metformin, and insulin with metformin. The treatment effect was evaluated after a 6-month follow-up. The study identified several factors that influenced cognitive function in T2DM patients, including female gender, duration of diabetes >13.50 years, years of education >7.50 years, and serum sodium level > 141.90 mmol/L. Metformin and insulin with metformin showed superior therapeutic effects compared to insulin alone, but no difference was observed between metformin and combination therapy. Special attention should be given to female and those with diabetes duration >13.50 years, as well as to individuals with educational level ≤ 7.50 years and serum sodium concentration ≤ 141.90 mmol/L. Metformin and insulin with metformin effectively improve MCI in patients with T2DM and outperform insulin monotherapy. The efficacy of metformin and combination therapy was found to be comparable.

The effect of insomnia on mild cognitive impairment in patients with vascular risks: results of a three-year longitudinal study

To assess the dynamics of sleep parameters based on the analysis of three items of HDRS-17, designed to measure the severity of insomnia, during 36 months of follow-up and their relationship with indicators of the cognitive phenotype in patients with vascular risk factors. The longitudinal study included 51 patients (mean age 57.7±6.2 years, 19 (37.3%) men)) who met the inclusion criteria. All participants underwent a general clinical examination with assessment of vascular risks and neuropsychological testing using the Montreal Cognitive Assessment (MoCA) and HDRS-17 at baseline and after 36 months. During the study, patients received stable basic therapy to prevent modifiable vascular risk factors. Sleeping pills were taken sporadically when there were complaints of problems falling asleep. During the 36-month study, as vascular cognitive impairment progressed from 23.7±2.6 to 22.1±2.4 points on the MoCA scale (p=0.01), mainly due to decreased attention (p=0.01), executive functions (EF) (p=0.01), memory (p=0.02), speech (p=0.02), an increase in sleep disturbances was observed (item 4: 0.8±0.02 to 1.9±0.1 points, p=0.01; point 5: 0.6±0.02 to 1.7±0.1 points, p=0.01) and depression (7.5±0.5 to 13.7±3.0 points, p=0.01) in patients with vascular risks. A strong inverse correlation was revealed between the values of items 4, 5, 6 on the HDRS-17 and the average MoCA-total scores (r=-0.85; r=-0.87; r=-0.8 (p<0.05)), memory index (r=-0.8; r=-0.75; r=-0.81 (p<0.05)), attention index (r=-0.88; r=-0.86; r=-0.83 (p<0.05)), index of executive functions (r=-0.87; r=-0.85; r=-0.8 (p<0.05)), respectively. The progression of cognitive impairment is associated with worsening insomnia disorders and depression in patients with vascular risks.

Neurometabolic therapy of mild cognitive impairment in patients with chronic cerebral ischemia

To evaluate the effect of a sequential therapy regimen with Mexidol (500 mg injections intravenously for 14 days) and Mexidol FORTE 250 (250 mg tablets 3 times a day for 60 days) on higher cortical functions in patients with moderate cognitive disorders in chronic cerebral ischemia. A comparative, prospective study included 63 patients with chronic cerebral ischemia with moderate cognitive impairment. All patients received basic therapy aimed at reducing risk factors (antihypertensive, antithrombotic drugs as indicated). Patients of the main group (30 people: 12 men, 18 women) received Mexidol intravenously 500 mg in 100 ml of 0.9% NaCl solution once a day for 14 days, then Mexidol FORTE 250 (film-coated tablets) 250 mg 3 times a day for the next 60 days. The comparison group consisted of 33 patients (14 men, 19 women) who received only basic therapy. The groups were comparable in terms of age, sex characteristics and severity of cognitive deficit. We examined cognitive status (MoCA scale, Frontal Dysfunction Battery, 10 Word Memorization tests), severity of asthenia (MFI-20 scale), anxiety and depression (HADS scale), patient's subjective assessment of the dynamics of the condition (CGI-improvement scale) in 1st, 14th and 74th±5 days of observation. On days 1 and 74±5 of observation, patients were examined using transcranial magnetic stimulation to study the neuronal activity of the cerebral cortex. In the main group, at the time of completion of taking Mexidol and Mexidol FORTE 250, a pronounced cognitive regression was noted (MoCA scale +3 points, difference with the comparison group 1 point (p<0.0001); Frontal Dysfunction Battery test +4 points, difference with comparison group 2 points (p<0.001); memory test «10 words» +2 points, difference with the comparison group 1 point (p<0.05), emotional (HADS anxiety scale -8 points, difference with the comparison group 3 points (p<0.001), depression -3.5 points, difference with the comparison group 1.5 points (p<0.01), asthenic disorders (MFI-20 scale -30 points, difference with the comparison group 15.5 points (p<0.01), improvement in the well-being of patients (CGI-improvement scale -2 points, difference with the comparison group 1 point (p<0.0001). According to the transcranial magnetic stimulation performed, a statistically significant decrease in the central motor conduction time at the level of 1 and 2 motor neurons of the pyramidal tract bilaterally from the start to the end of therapy with Mexidol and Mexidol FORTE 250 was determined (p<0.01). An inverse correlation was found between the time of central motor conduction and the results of the Frontal Dysfunction Battery test at the same time points with left-sided localization of 1 motor neuron (p<0.01). The results of a study of the use of sequential therapy with Mexidol 500 mg IV drip 1 time per day for 14 days followed by oral administration of Mexidol FORTE 250 1 tablet 3 times a day for 60 days indicate its clinical effectiveness and safety in patients with chronic cerebral ischemia with mild cognitive impairment, and also confirm its importance for preventing the progression of cognitive disorders.

Cognitive Interventions: Symptomatic or Disease-Modifying Treatments in the Brain?

Recent findings suggest that brain-stimulating activities may have beneficial effects on both Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). However, whether cognitive interventions merely enhance cognitive reserve or truly attenuate, or even reverse, the disease's pathophysiology is still controversial. The aim of the present article is to discuss the potential for brain-stimulating activities, including cognitive stimulation (CS), cognitive rehabilitation (CR), and cognitive training (CT), to be symptomatic or disease-modifying interventions in the context of cognitive decline. While emerging evidence indicates that CT can enhance synaptic plasticity, suggesting a potential role in augmenting cognitive reserve, its impact on AD pathology remains uncertain. Small-scale studies suggest that CT and CS may slow down neurodegeneration in MCI patients and that multidomain interventions combining physical activity with CT may benefit Aβ pathology. However, the considerable heterogeneity across studies limits the comparability of findings. It underscores the necessity for a more standardized approach to cognitive interventions in future guidelines for preventing and managing cognitive decline.

The Association Between Brain Metabolic Biomarkers Using 18F-FDG and Cognition and Vascular Risk Factors, as well as Its Usefulness in the Diagnosis and Staging of Alzheimer's Disease.

18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is valuable in Alzheimer's disease (AD) workup. To explore the effectiveness of 18F-FDG PET in differentiating and staging AD and associations between brain glucose metabolism and cognitive functions and vascular risk factors. 107 participates including 19 mild cognitive impairment (MCI), 38 mild AD, 24 moderate AD, 15 moderate-severe AD, and 11 frontotemporal dementia (FTD) were enrolled. Visual and voxel-based analysis procedures were utilized. Cognitive conditions, including 6 cognitive function scores and 7 single-domain cognitive performances, and vascular risk factors linked to hypertension, hyperlipidemia, diabetes, and obesity were correlated with glucose metabolism in AD dementia using age as a covariate. 18F-FDG PET effectively differentiated AD from FTD and also differentiated MCI from AD subtypes with significantly different hypometabolism (except for mild AD) (height threshold p < 0.001, all puncorr < 0.05, the same below). The cognitive function scores, notably Mini-Mental State Examination and Montreal Cognitive Assessment, correlated significantly with regional glucose metabolism in AD participants (all p < 0.05), whereas the single-domain cognitive performance and vascular risk factors were significantly associated with regional glucose metabolism in MCI patients (all p < 0.05). This study underlines the vital role of 18F-FDG PET in identifying and staging AD. Brain glucose metabolism is associated with cognitive status in AD dementia and vascular risk factors in MCI, indicating that 18F-FDG PET might be promising for predicting cognitive decline and serve as a visual framework for investigating underlying mechanism of vascular risk factors influencing the conversion from MCI to AD.

An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket.

Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychological tests, commonly used for MCI detection, often lack ecological validity due to their simplistic and quiet testing environments. To address this gap, our study developed an immersive VR supermarket cognitive assessment program (IVRSCAP), simulating daily cognitive activities to enhance the ecological validity of MCI detection. This program involved elderly participants from Chengdu Second People's Hospital and various communities, comprising both MCI patients (N=301) and healthy elderly individuals (N=1027). They engaged in the VR supermarket cognitive test, generating complex datasets including User Behavior Data, Tested Cognitive Dimension Game Data, Trajectory Data, and Regional Data. To analyze this data, we introduced an adaptive ensemble learning method for imbalanced samples. Our study's primary contribution is demonstrating the superior performance of this algorithm in classifying MCI and healthy groups based on their performance in IVRSCAP. Comparative analysis confirmed its efficacy over traditional imbalanced sample processing methods and classic ensemble learning voting algorithms, significantly outperforming in metrics such as recall, F1-score, AUC, and G-mean. Our findings advocate the combined use of IVRSCAP and our algorithm as a technologically advanced, ecologically valid approach for enhancing early MCI detection strategies. This aligns with our broader aim of integrating realistic simulations with advanced computational techniques to improve diagnostic accuracy and treatment efficacy in cognitive health assessments.

Machine Learning-Based Prediction Models for Cognitive Decline Progression: A Comparative Study in Multilingual Settings Using Speech Analysis.

Mild cognitive impairment (MCI) is a condition commonly associated with dementia. Therefore, early prediction of progression from MCI to dementia is essential for preventing or alleviating cognitive decline. Given that dementia affects cognitive functions like language and speech, detecting disease progression through speech analysis can provide a cost-effective solution for patients and caregivers. In our study, we examined spontaneous speech (SS) and written Mini Mental Status Examination (MMSE) scores from a 60-patient dataset obtained from the Mugla University Dementia Outpatient Clinic (MUDC) and a 153-patient dataset from the Alzheimer's Dementia Recognition through Spontaneous Speech (ADRess) challenge. Our study, for the first time, analyzed the impact of audio features extracted from SS in distinguishing between different degrees of cognitive impairment using both an Indo-European language and a Turkic language, which exhibit distinct word order, agglutination, noun cases, and grammatical markers. When each machine learning model was tested on its respective trained language, we attained a 95% accuracy using the random forest classifier on the ADRess dataset and a 94% accuracy on the MUDC dataset employing the multilayer perceptron (MLP) neural network algorithm. In our second experiment, we evaluated the effectiveness of each language-specific machine learning model on the dataset of the other language. We achieved accuracies of 72% for English and 76% for Turkish, respectively. These findings underscore the cross-language potential of audio features for automated tracking of cognitive impairment progression in MCI patients, offering a convenient and cost-effective option for clinicians or patients.

HOPE: Hybrid-Granularity Ordinal Prototype Learning for Progression Prediction of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is often at high risk of progression to Alzheimer's disease (AD). Existing works to identify the progressive MCI (pMCI) typically require MCI subtype labels, pMCI vs. stable MCI (sMCI), determined by whether or not an MCI patient will progress to AD after a long follow-up. However, prospectively acquiring MCI subtype data is time-consuming and resource-intensive; the resultant small datasets could lead to severe overfitting and difficulty in extracting discriminative information. Inspired by that various longitudinal biomarkers and cognitive measurements present an ordinal pathway on AD progression, we propose a novel Hybrid-granularity Ordinal PrototypE learning (HOPE) method to characterize AD ordinal progression for MCI progression prediction. First, HOPE learns an ordinal metric space that enables progression prediction by prototype comparison. Second, HOPE leverages a novel hybrid-granularity ordinal loss to learn the ordinal nature of AD via effectively integrating instance-to-instance ordinality, instance-to-class compactness, and class-to-class separation. Third, to make the prototype learning more stable, HOPE employs an exponential moving average strategy to learn the global prototypes of NC and AD dynamically. Experimental results on the internal ADNI and the external NACC datasets demonstrate the superiority of the proposed HOPE over existing state-of-the-art methods as well as its interpretability. Source code is made available at https://github.com/thibault-wch/HOPE-for-mild-cognitive-impairment.

FORTCARE-MCI study protocol: evaluation of Fortasyn Connect in the management of mild cognitive impairment in primary care.

Neuropsychiatric symptoms are prevalent in patients with mild cognitive impairment (MCI) and are predictive of the conversion to dementia. Fortasyn Connect, a medical food, has shown efficacy in managing cognitive and behavioral symptoms associated with MCI. Early diagnosis and intervention in primary care are essential for managing MCI. However, real-world prospective studies assessing Fortasyn Connect in MCI are still limited. This observational, multicenter, prospective study will enroll 150 patients recently diagnosed with MCI by primary care physicians across several regions in Spain. Participants will be followed-up over a 12-month period, with assessments at baseline, 6 months, and 12 months, as per clinical practice. The study aims to evaluate the impact of Fortasyn Connect on neuropsychiatric symptoms, cognition, and health-related quality of life (HRQoL) using validated neuropsychological tests and machine learning (ML) techniques. The primary outcome measure will be changes in neuropsychiatric symptoms using the Neuropsychiatric Inventory Questionnaire (NPI-Q) at 6 months. Secondary outcome measures include further changes in the NPI-Q at 12 months, and changes in cognition (Fototest, and clock-drawing test) and HRQoL (EQ-5D-5L) at 6 and 12 months. Exploratory outcomes will assess speech using an artificial intelligence (AI)-enhanced ML tool, with a correlation analysis of these findings with traditional neuropsychological test results. This study will provide evidence of the effectiveness of Fortasyn Connect in a real-world setting, exploring its potential to stabilize or improve neuropsychiatric symptoms, cognition, and HRQoL in MCI patients. Results will also contribute to the understanding of AI and ML in identifying early biomarkers of cognitive decline, supporting the timely management of MCI.

A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, almost machine learning and deep learning methods are rarely analyzed from the perspective of spatial structure and temporal dimension. In order to make full use of rs-fMRI data, this study constructed a dynamic spatiotemporal graph neural network model, which mainly includes three modules: temporal block, spatial block, and graph pooling block. Our proposed model can extract the BOLD signal of the subject's fMRI data and the spatial structure of functional connections between different brain regions, and improve the decision-making results of the model. In the study of AD, MCI and NC, the classification accuracy reached 83.78% outperforming previously reported, which manifested that our model could effectively learn spatiotemporal, and dynamic spatio-temporal method plays an important role in identifying different groups of subjects. In summary, this paper proposed an end-to-end dynamic spatio-temporal graph neural network model, which uses the information of the temporal dimension and spatial structure in rs-fMRI data, and achieves the improvement of the three classification performance among AD, MCI and NC.

Association study of brain structure-function coupling and glymphatic system function in patients with mild cognitive impairment due to Alzheimer's disease.

Mild cognitive impairment (MCI) is a critical transitional phase from healthy cognitive aging to dementia, offering a unique opportunity for early intervention. However, few studies focus on the correlation of brain structure and functional activity in patients with MCI due to Alzheimer's disease (AD). Elucidating the complex interactions between structural-functional (SC-FC) brain connectivity and glymphatic system function is crucial for understanding this condition. The aims of this study were to explore the relationship among SC-FC coupling values, glymphatic system function and cognitive function. 23 MCI patients and 18 healthy controls (HC) underwent diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). DTI analysis along the perivascular space (DTI-ALPS) index and SC-FC coupling values were calculated using DTI and fMRI. Correlation analysis was conducted to assess the relationship between Mini-Mental State Examination (MMSE) scores, DTI-ALPS index, and coupling values. Receiver operating characteristic (ROC) curves was conducted on the SC-FC coupling between the whole brain and subnetworks. The correlation of coupling values with MMSE scores was also analyzed. MCI patients (67.74 ± 6.99 years of age) exhibited significantly lower coupling in the whole-brain network and subnetworks, such as the somatomotor network (SMN) and ventral attention network (VAN), than HCs (63.44 ± 6.92 years of age). Whole-brain network coupling was positively correlated with dorsal attention network (DAN), SMN, and visual network (VN) coupling. MMSE scores were significantly positively correlated with whole-brain coupling and SMN coupling. In MCI, whole-brain network demonstrated the highest performance, followed by the SMN and VAN, with the VN, DAN, limbic network (LN), frontoparietal network (FPN), and default mode network (DMN). Compared to HCs, lower DTI-ALPS index was observed in individuals with MCI. Additionally, the left DTI-ALPS index showed a significant positive correlation with MMSE scores and coupling values in the whole-brain network and SMN. These findings reveal the critical role of SC-FC coupling values and the ALPS index in cognitive function of MCI. The positive correlations observed in the left DTI-ALPS and whole-brain and SMN coupling values provide a new insight for investigating the asymmetrical nature of cognitive impairments.

A Novel Approach Analysing the Dynamic Brain Functional Connectivity for Improved MCI Detection.

Dynamic functional connectivity (dFC) of the brain has been explored for the detection of mild cognitive impairment (MCI), preventing potential development of Alzheimer's disease. Deep learning is widely used method for dFC analysis but is unfortunately computationally expensive and unexplainable. Root mean square value (RMS) of the pairwise Pearson's correlation of the dFC is also proposed but is insufficient for accurate MCI detection. The present study aims at exploring the feasibility of several novel features for dFC analysis, and thus, reliable MCI detection. A public resting-state functional magnetic resonance imaging dataset containing healthy controls (HC), early MCI (eMCI), and late MCI (lMCI) patients was used. In addition to RMS, nine features were extracted from the pairwise Pearson's correlation of the dFC, inducing amplitude-, spectral-, entropy-, and autocorrelation-related features, and time reversibility. A Student's t-test and a least absolute shrinkage and selection operator (LASSO) regression were employed for feature dimension reduction. A SVM was then adopted for two classification objectives: HC vs. lMCI and HC vs. eMCI. Accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve were calculated as performance metrics. 6109 out of 66700 features are significantly different between HC and lMCI and 5905 between HC and eMCI. Besides, the proposed features produce excellent classification results for both tasks, outperforming most of the existing methods. This study proposes a novel and general framework for dFC analysis, providing a promising tool for the detection of many neurological brain diseases using different brain signals.

The role of physical function and physical activity on cognitive function in the elderly

The aim of this study is to clarify the role of physical activity level and physical function on cognitive function of the elderly. A nested case–control investigation from a middle-aged and elderly cognitive health cohort was conducted. 103 mild cognitive impairment (MCI) patients were selected for the purpose of the study and matched 1:1 according to age and sex. Handgrip strength, gait speed and 5-time chair stand test were used to evaluate physical function. The physical activity scale for the elderly (PASE) was used to assess physical activity level. The Montreal cognitive assessment (MoCA) was used to quickly screen the mild cognitive impairment of the subjects. Body composition was estimated by bioelectrical impedance analysis. The total score of MoCA and the scores of different cognitive domains in two groups were different, handgrip strength was lower in the MCI group. Multivariate logistic regression model results showed that handgrip strength decreased could increase the risk of MCI(OR = 3.008, 95%CI: 1.421,6.369), higher PASE score was correlated with lower risk of MCI (OR = 0.402, 95%CI: 0.168,0.966). After combining body composition indices, handgrip strength and PASE score remained significantly associated with the occurrence of MCI, and there is an interaction. Logistic regression models were used for receiver operating characteristic curve analysis, all models demonstrate a good level of predictive performance for MCI. Physical activity level and physical function are associated with mild cognitive impairment. Higher physical activity level, normal handgrip strength are correlated with lower risk of MCI.

Differences between sub-second and supra-second durations for the assessment of timing deficits in amnestic mild cognitive impairment

Previous studies have often reported timing deficits in older adults with different degrees of cognitive decline, however, the exact nature of impairments in time perception is still to be elucidated. In particular, it is unclear if the deficits are more pronounced for short or long intervals, consistent with notions that different cognitive processes and neuroanatomical areas are involved in the processing of durations of different ranges. The present study aims to further investigate timing abilities in amnestic mild cognitive impairment (aMCI) patients and age-matched controls. Participants were asked to decide whether an acoustic event occurred within the first or the second half of a reference duration. The results revealed a bias towards larger PSE values and reduced precision in aMCI patients compared to healthy controls. Further analyses showed that the bias towards larger PSE values correlated with memory performance, especially when sub-second durations were tested. Overall, the results demonstrate that memory deficits in aMCI patients coincide with changes in time perception in the sub-second interval range.

Attention-guided 3D CNN With Lesion Feature Selection for Early Alzheimer's Disease Prediction Using Longitudinal sMRI.

Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is critical for early intervention. Towards this end, various deep learning models have been applied in this domain, typically relying on structural magnetic resonance imaging (sMRI) data from a single time point whereas neglecting the dynamic changes in brain structure over time. Current longitudinal studies inadequately explore disease evolution dynamics and are burdened by high computational complexity. This paper introduces a novel lightweight 3D convolutional neural network specifically designed to capture the evolution of brain diseases for modeling the progression of MCI. First, a longitudinal lesion feature selection strategy is proposed to extract core features from temporal data, facilitating the detection of subtle differences in brain structure between two time points. Next, to refine the model for a more concentrated emphasis on lesion features, a disease trend attention mechanism is introduced to learn the dependencies between overall disease trends and local variation features. Finally, disease prediction visualization techniques are employed to improve the interpretability of the final predictions. Extensive experiments demonstrate that the proposed model achieves state-of-the-art performance in terms of area under the curve (AUC), accuracy, specificity, precision, and F1 score. This study confirms the efficacy of our early diagnostic method, utilizing only two follow-up sMRI scans to predict the disease status of MCI patients 24 months later with an AUC of 79.03%.

The effect of physical exercise with cognitive training on inflammation and Alzheimer's disease biomarkers of Mild Cognitive Impairment patients

Physical exercise (PE) was proven crucial for cognitive health of both demented and cognitively intact individuals. Simultaneous cognitive training may augment PE beneficial effects in demented patients. However, it is still debatable how PE and/or cognitive training reflect on mild cognitive impairment (MCI) and pathological factors related to Alzheimer's disease, namely inflammatory cytokines interleukin (IL) 1 beta and 6, tumor necrosis factor alpha (TNF-α), amyloid beta (Αβ) peptides, total tau protein (t-tau) and tau phosphorylated at threonine 181 (p-tau181). In the current study, 74 MCI patients were allocated to three groups: non-intervention (Control), PE, and PE with cognitive training (Mixed). Blood serum was received at the Baseline and after the intervention (at 3 months). Levels of IL-1β, IL-6, TNF-α, Αβ42, Αβ40, tau and p-tau181 were determined with enzyme-linked immunosorbent assay. PE and Mixed interventions reduced the levels of IL-1β, IL-6 and of p-tau181. Augmented levels of Αβ42 and the ratio Αβ42/40, and reduced ratio p-tau181/Αβ42 were verified only in the PE group. Reduced levels of TNF-α, Αβ40 and t-tau were verified only at the Mixed group. In addition, correlation analyses provided a significant interrelation between the alleviation of inflammation and p-tau181/Αβ42 reduction in patients of the Mixed intervention. These results suggest the beneficiary effect of PE and cognitive training on the regulation of inflammation resulting in mitigation of AD-related blood biomarkers and urge for their employment as non-pharmacological alternatives for stalling the progression of dementia.

Optimal dose and type of exercise to improve cognitive function in patients with mild cognitive impairment: a systematic review and network meta-analysis of RCTs.

Mild cognitive impairment (MCI) represents a prodromal stage of dementia, characterized by cognitive decline exceeding that expected with normal aging. Exercise interventions have emerged as a promising approach to counter functional decline and enhance cognitive function in the elderly MCI population. However, the optimal exercise modalities and dosage (dose-response relationship) are understudied. It aims to determine the most effective exercise modality for MCI patients by optimizing the dose-response relationship to ensure sufficient intensity to induce positive neurological adaptations. A systematic search of electronic databases, including PubMed, Embase, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials was conducted from inception to April 15, 2024. Studies evaluating the efficacy of exercise interventions in MCI participants were included. Primary outcomes of interest are global cognition and executive function. Random-effects models will be utilized for both pairwise and network meta-analysis. Following the application of specific inclusion and exclusion criteria, a total of 42 articles, encompassing 2832 participants, were chosen for inclusion in a network meta-analysis. The findings revealed that multi-component exercise demonstrated superior efficacy in mitigating the deterioration of global cognition, as evidenced by standard mean differences (SMDs) of 1.09 (95% CI: 0.68 to 1.51) compared to passive controls. Additionally, multi-component exercise exhibited a significant impact on executive function, with SMDs of 2.50 (95% CI: 0.88 to 4.12) when contrasted with passive controls. Our research has demonstrated that sessions lasting 30 minutes, occurring 3-4 times per week, with interventions lasting 12-24 weeks and an intensity of 60-85% of maximum heart rate, yield higher effect sizes in improving global cognition. However, sessions lasting 30-61 minutes, with interventions lasting 25 weeks or longer, show greater effectiveness in enhancing executive function. A network meta-analysis identified multi-component exercise as the most effective intervention for improving global cognitive and executive function in patients with mild cognitive impairment. Notably, moderate-intensity exercise performed at least three times weekly appears beneficial, with evidence suggesting shorter sessions and higher frequencies may optimize cognitive outcomes. https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024534922.

Critical Values of Daily Sedentary Time and Its Longitudinal Association with Mild Cognitive Impairment Considering APOE ε4: A Prospective Cohort Study.

Long sedentary time and physical inactivity are negatively related to cognition, but the cut-off value remains unclear, and apolipoprotein E polymorphism ε4 (APOE ε4) is a known genetic risk factor of mild cognitive impairment (MCI). To explore longitudinal association of sedentary time and MCI, and to identify a cutoff value that increases the risk of developing MCI, taking into account APOE ε4 stratification and its interactions. A prospective cohort study. Population-based study. We included 4932 older adults from Tianjin Elderly Nutrition and Cognition (TENC) cohort study recruited from March 2018 to June 2021 with 3.11 years of median follow-up time. The primary outcome was newly diagnosed MCI, which was diagnosed by a modified version of the Petersen's criteria. The information of sedentary time (hours/day) and physical activity (MET-h/week) were obtained by questionnaire. Cox proportional hazard regression models and restricted spline curve were conducted. A total of 4932 participants were included (mean [SD] age, 67.85 [4.96] years; 2627 female [53.3%] and 2305 male [46.7%]), 740 newly onset MCI patients were identified. Longer sedentary time was associated with higher risk of MCI for all participants (HR:1.069, 95%CI: 1.034, 1.105), especially in APOE ε4 non-carriers (HR:1.083, 95%CI: 1.045, 1.123) whether adjusted potential confounders. Sedentary time had synergistic interactions with APOE ε4 (β:1.503, 95%CI: 1.163, 1.942) and physical activities (β: 1.495, 95%CI: 1.210, 1.846). Restricted spline curve showed a cut-off value of 3.03 hours/day. Long sedentary time (≥3.03 hours/day) could increase MCI risk, especially in APOE ε4 non-carriers, people with higher PA, aged 65 and above.