UNASSIGNED: This study aimed to investigate the role of galectin-3 (Gal-3; coded by LGALS3 gene), as a biomarker for MCI in T2DM patients and to develop and validate a predictive nomogram integrating galectin-3 with clinical risk factors for MCI prediction. Additionally, microRNA regulation of LGALS3 was explored.
UNASSIGNED: The study employed a cross-sectional design. A total of 329 hospitalized T2DM patients were recruited and randomly allocated into a training cohort (n = 231) and a validation cohort (n = 98) using 7:3 ratio. Demographic data and neuropsychological assessments were recorded for all participants. Plasma levels of galectin-3 were measured using ELISA assay. We employed Spearman\'s correlation and multivariable linear regression to analyze the relationship between galectin-3 levels and cognitive performance. Furthermore, univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for MCI in T2DM patients. Based on these analyses, a predictive nomogram incorporating galectin-3 and clinical predictors was developed. The model\'s performance was evaluated in terms of discrimination, calibration, and clinical utility. Regulatory miRNAs were identified using bioinformatics and their interactions with LGALS3 were confirmed through qRT-PCR and luciferase reporter assays.
UNASSIGNED: Galectin-3 was identified as an independent risk factor for MCI, with significant correlations to cognitive decline in T2DM patients. The developed nomogram, incorporating Gal-3, age, and education levels, demonstrated excellent predictive performance with an AUC of 0.813 in the training cohort and 0.775 in the validation cohort. The model outperformed the baseline galectin-3 model and showed a higher net benefit in clinical decision-making. Hsa-miR-128-3p was significantly downregulated in MCI patients, correlating with increased Gal-3 levels, while Luciferase assays confirmed miR-128-3p\'s specific binding and influence on LGALS3.
UNASSIGNED: Our findings emphasize the utility of Gal-3 as a viable biomarker for early detection of MCI in T2DM patients. The validated nomogram offers a practical tool for clinical decision-making, facilitating early interventions to potentially delay the progression of cognitive impairment. Additionally, further research on miRNA128\'s regulation of Gal-3 levels is essential to substantiate our results.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

BACKGROUND: This study aims to evaluate the add-on effects of oral Chinese herbal medicine (CHM) for mild cognitive impairment (MCI), when used in addition to donepezil compared to donepezil alone.
METHODS: Randomized controlled trials comparing these treatments across all types of MCI were identified from nine databases and three registers until August 2023. Outcome measures were Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and adverse events (AEs). Methodological quality was assessed using Cochrane risk-of-bias tool, and evidence certainty was evaluated using the GRADE method.
RESULTS: Involving 1611 participants across 20 studies, meta-analysis results indicate that oral CHM combined with donepezil significantly improved cognitive function in MCI patients compared to donepezil alone, as evidenced by MMSE (1.88 [1.52, 2.24], I2 = 41%, 12 studies, 993 participants) and MoCA (MD: 2.01 [1.57, 2.44], I2 = 52%, 11 studies, 854 participants). Eleven studies reported details of AEs, identifying gastrointestinal symptoms and insomnia as the most common symptoms. No significant difference in AEs frequency was found between the groups (RR: 0.91 [0.59, 1.39], I2 = 4%, 11 studies, 808 participants). All 20 studies were evaluated as having \"some concerns\" regarding the overall risk of bias. The certainty of evidence for MMSE was \"moderate\" and \"low\" for MoCA. From frequently utilized herbs, two classical CHM formulae were identified: Kai xin san and Si wu decoction. The observed treatment effects of commonly used herbs may be exerted through multiple pharmacological mechanisms, including anti-inflammatory, anti-oxidative stress, anti-apoptotic actions, promotion of neuronal survival and modulation of the cholinergic system.
CONCLUSIONS: The concurrent use of oral CHM and donepezil appears to be more effective than donepezil alone in improving the cognitive function of MCI, without leading to an increase in AEs. While recognizing concerns of overall methodological quality, this combined therapy should be considered as an alternative option for clinical practice.

OBJECTIVE: To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.
METHODS: 371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors.
RESULTS: The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment.
CONCLUSIONS: ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

OBJECTIVE: Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson\'s disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI).
METHODS: A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson\'s Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson\'s Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event.
RESULTS: Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004).
CONCLUSIONS: PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

BACKGROUND: This study aimed to explore the associations between brain structures, cognition, and motor control in participants with mild cognitive impairment (MCI), with a focus on dual-task performance.
METHODS: Thirty MCI patients and thirty healthy controls were enrolled. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Structural magnetic resonance imaging data were analyzed using voxel-based morphometry (VBM) to calculate brain parenchyma volume and gray matter volume (GMV). Participants performed single- and dual-task Timed Up and Go (TUG) tests, and the correlations between significant GMV differences and task execution time was analyzed.
RESULTS: MCI patients showed significantly lower MoCA scores, particularly in visuospatial/executive, attention, and delayed recall domains (p < 0.05). Dual-task TUG execution time was significantly increased in MCI patients (p < 0.05). The GMV in the right anterior lobe of the cerebellum and both insulae was positively correlated with visuospatial/executive scores (FDR-corrected, p < 0.05). The GMV of the right cerebellar anterior lobe and insula were significantly reduced in MCI patients (p < 0.05). The GMV of the right cerebellar anterior lobe was negatively correlated with dual-task execution time (r = -0.32, p = 0.012).
CONCLUSIONS: Smaller GMV in the right anterior lobe of the cerebellum was associated with impaired dual-task performance, which may provide more evidence for the neural mechanisms of cognitive and motor function impairments in MCI.

BACKGROUND: Multicomponent exercise has the potential to improve cognitive function in people with mild cognitive impairment. However, the effects of multicomponent exercise on specific cognitive subdomains in mild cognitive impairment and the optimal combination of exercise components remain unclear.
OBJECTIVE: This systematic review aimed to (a) investigate the effects of multicomponent exercise on different cognitive subdomains in people with mild cognitive impairment and (b) investigate the effects of different combinations of multicomponent exercise on global cognition in people with mild cognitive impairment.
METHODS: A systematic review and meta-analysis.
METHODS: Six electronic databases, including PubMed, Medline, EMBASE, Web of Science, Cochrane Library, and CINAHL were systematically searched from inception to January 1st, 2023. Randomized controlled trials assessing the effect of multicomponent exercise interventions on cognitive function in people with mild cognitive impairment were included. The risk of bias was assessed using the Cochrane collaborative bias assessment tool. A random-effects model was used to calculate standardized mean difference. Subgroup analyses, meta-regression, and sensitive analysis were performed. If a meta-analysis was not feasible, studies were synthesized narratively.
RESULTS: Twenty studies were identified for systematic review and meta-analysis. Multicomponent exercise significantly improved global cognition [SMD = 1.04; 95 % confidence interval (CI): 0.53, 1.55], cognitive flexibility (SMD = -1.04; 95 % CI: -1.81, -0.27), processing speed (SMD = 0.43; 95 % CI: 0.04, 0.82), verbal fluency (SMD = 0.38; 95 % CI: 0.13, 0.63), attention (SMD = -0.90; 95 % CI: -1.68, -0.12) and memory (SMD = 0.36; 95 % CI: 0.04, 0.69) in mild cognitive impairment. The multicomponent exercise including cardiovascular (exercise that promotes cardiovascular health, such as endurance training or aerobic exercise) and motor (exercises that improve physical abilities, such as balance, coordination, agility, flexibility, etc.) components positively affected global cognition in people with mild cognitive impairment (SMD = 1.06; 95 % CI: 0.55, 1.57).
CONCLUSIONS: The findings of this study suggest that multicomponent exercise has a positive impact on various cognitive domains, including global cognition, cognitive flexibility, processing speed, verbal fluency, attention and memory in mild cognitive impairment. Specifically, the combination of exercises including cardiovascular and motor components was found to be effective in improving global cognition. However, further research is needed to investigate the optimal frequency and intensity of the multicomponent exercise intervention, and more detail about exercise combinations of the motor component (not classified in this study) for individuals with mild cognitive impairment.
BACKGROUND: The protocol was registered on PROSPERO (CRD42023400302).

BACKGROUND: Whether brain functional connectivity (FC) is consistently disrupted in individuals with mild cognitive impairment (MCI) with isolated language impairment (ilMCI), and its potential to differentiate between MCI subtypes remains uncertain.
METHODS: Cross-sectional data from 404 participants in two cohorts (the Chinese Preclinical Alzheimer\'s Disease Study and the Alzheimer\'s Disease Neuroimaging Initiative) were analyzed, including neuropsychological tests, resting-state functional magnetic resonance imaging (fMRI), cerebral amyloid positivity, and apolipoprotein E (APOE) status.
RESULTS: Temporo-frontoparietal FC, particularly between the bilateral superior temporal pole and the left inferior frontal/supramarginal gyri, was consistently decreased in ilMCI compared to amnestic MCI (aMCI) and normal controls, which was correlated with semantic impairment. Using mean temporo-frontoparietal FC as a classifier could improve accuracy in identifying ilMCI subgroups with positive cerebral amyloid deposition and APOE risk alleles.
CONCLUSIONS: Temporal-frontoparietal hypoconnectivity was observed in individuals with ilMCI, which may reflect semantic impairment and serve as a valuable biomarker to indicate potential mechanisms of underlying neuropathology.
CONCLUSIONS: Temporo-frontoparietal hypoconnectivity was observed in impaired language mild cognitive impairment (ilMCI). Temporo-frontoparietal hypoconnectivity may reflect semantic impairment. Temporo-frontoparietal functional connectivity can classify ilMCI subtypes.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson\'s correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain\'s operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs\' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI\'s K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson\'s correlation (K-PC) has some advantage over deep learning.

In Alzheimer\'s disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer\'s Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD.