There are many commonalities between the clinical symptoms of dementia with Lewy bodies (DLB) and those of Alzheimer's disease (AD). The accurate differentiation of these two diseases is an important neuropsychological issue. The Mini-Mental State Examination (MMSE) is often used as a screening test for dementing disorders. We created evaluation items for the pentagon copy test of MMSE and developed a simple, highly accurate evaluation method for differentiating DLB in combination with conventional evaluation items such as the Qualitative Scoring MMSE Pentagon Test (QSPT). Subjects were divided into three groups: DLB (n = 119), AD (n = 50), and Normal (n = 26). The severities of DLB and AD ranged from mild cognitive impairment (MCI) to mild dementia. We compared the results of the pentagon copy test. We found that the rates of patients with abnormalities in "motor incoordination" and "gestalt destruction" were higher in the DLB group than the AD group. Furthermore, receiver operating characteristic curve analysis suggested the differentiation of DLB with high accuracy (sensitivity: 0.70, specificity: 0.78) using the criterion of patients meeting one of the following three characteristics: "the number of angles on QSPT: scores other than 4," "major tremor (Parkinsonism-related tremor) is present," and "gestalt destruction (distortion in overall coherence) is present." This evaluation method may be clinically useful for evaluating MCI to mild DLB patients because the burden on patients is low.

Lecanemab, a monoclonal antibody targeting β-amyloid protofibrils, has shown promising results in a Phase III clinical trial for the treatment of early stages of Alzheimer's disease (AD) and has been approved by the European Medicines Agency. An Early Market Authorization could be submitted to the French regulatory agencies, potentially allowing for the drug's use in clinical practice in France in 2025. To guide French clinicians in administering lecanemab in a standardized way, the French Federation of Memory Clinics has developed appropriate use recommendations for lecanemab that highlight relevant questions established to ensure an optimal risk-benefit ratio. The recommendations emphasize that lecanemab treatment requires a comprehensive individualized evaluation of the risk-benefit ratio, which should occur in multidisciplinary meetings. When approved, the guidelines support the use of blood biomarkers, proposing specific cutoffs for patients eligible for lecanemab under restricted conditions. In addition to the European Medicines Agency restrictions in patients on anticoagulants, and APOE4 homozygotes, the guidelines recommend against lecanemab treatment for patients with high amyloid-related hemorrhagic risk such as probable cerebral amyloid angiopathy (Boston criteria v1.5) until further data become available. Additionally, we recommend that MRI monitoring be started before the third infusion to account for early Amyloid Related Imaging Abnormalities (ARIA) occurring on lecanemab. It is recommended to establish a specific clinical care pathway with protocols for patients with ARIA, with trained physicians and radiologists with expertise in neurological emergency and intensive care. Finally, a discontinuation protocol based on dementia severity assessment after 18 months of lecanemab treatment is suggested. Access to lecanemab requires a personalized biological and genetic diagnosis of AD, which is currently not necessary in most cases. Therefore, the healthcare system must rapidly adjust to new diagnostic procedures and treatment delivery to ensure equal access for all individuals.

Magnetic resonance imaging (MRI) is pivotal to the evaluation of cerebral atrophy in Alzheimer's disease (AD). Although there are several scoring systems to identify atrophy in areas of interest, the possibility of having a simple and practical scale focused on evaluating parietal atrophy by MRI in a single axial slice in T2-Flair sequencing would be highly advantageous. We develop and validate the Mimenza-Aguilar-Herrera (MAH) visual scale to determine the degree of modified parietal atrophy by analyzing the posterior cingulate gyrus in an MRI axial slice in T2 weighted sequencing in individuals with early AD. Patients with AD, mild cognitive impairment, and other dementias were included. Interrater and intrarater concordance was analyzed with Cohen's kappa. ROC analysis was used to determine sensitivity/specificity. The relationship between medial parietal atrophy (MPA) and the MMSE and MoCA scores, age, educational level, and sex was determined by linear regression. Interrater concordance in the MPA-MAH was moderate, 0.83 (p = 0.001). The area under the curve in the mild AD group was 0.74, with a sensitivity of 71.67% and a specificity of 76.19% (p < 0.001). An association was detected between a MPA score > 1 [OR 5.1 (95% CI: 2-12.9, p < 0.001)], MMSE < 24 pts, [OR 4.5 (95% CI: 1.2-17.6, p = 0.026)], and MoCA < 24 pts, after adjusting for age and sex. The MAH scale proved useful in diagnosing early AD, with comparable performance to validated scales such as Koedam's and Scheltens'. This instrument could allow a quick and easy evaluation.

Late-life depression is a prevalent public health issue among the elderly. Imbalances in trace elements are increasingly recognized as associated with depression; however, the majority of current research has concentrated on examining the link between blood-based trace elements levels and depressive symptoms. Our objective was to determine if a similar correlation is observed between urinary trace elements levels and depressive symptoms.

We employed stratified, multi-stage random sampling to recruit 400 participants, aged 60 years or older, from a community-based population in a city located in southern China. The Patient Health Questionnaire-9 Items (PHQ-9) was utilized to evaluate depressive symptoms. The concentration of trace elements in urine was detected by inductively coupled plasma mass spectrometry. Multiple logistic regression analysis was conducted to assess the association between urinary trace elements levels and depressive symptoms, as well as the interactions between these levels and potential covariates. The Restricted Cubic Spline (RCS) model with four knots to further explore the association between urinary trace elements and depressive symptoms risk after adjusting for the confounders.

A total of 391 participants were investigated, including 50 (12.6 %) in depressive symptom group and 341 (87.4 %) in non-depressive symptom group. Urinary copper levels were positively correlated with depressive symptoms. Compared with the lowest tertile of urinary copper, the multivariate adjusted odds ratios of depressive symptom were 2.58 (1.18-5.64) in tertile 3. Furthermore, we found the interactions between urinary copper and gender were p < 0.05. The multivariate correction OR for T3 versus T1 in males was 21.10 (1.79-248.13) (Pfor trend = 0.002). RCS analysis revealed a positive association between copper levels and depressive symptoms (P-overall association = 0.025, and P-nonlinear = 0.161). No significant difference was observed in the risk of developing depressive symptoms among individuals with urinary copper concentrations below 8.22 μg/g creatinine. However, the risk of depressive symptoms increases progressively as the urinary copper concentration exceeds this threshold.

Urinary copper levels are correlated with the development of depressive symptoms, and copper exposure in men is more sensitive to depressive symptoms. Urinary copper, as a non-invasive test, is a promising indicator of depression symptoms in environmental exposure.

Mild cognitive impairment (MCI), prevalent among older adults, often precedes Alzheimer's disease (AD) or Alzheimer's disease-related dementias (ADRD), emphasizing the need for effective interventions. Early intervention in MCI is crucial, not only to alleviate symptoms but to potentially delay the progression of cognitive decline. The lack of definitive treatments for MCI has prompted the exploration into alternative non-pharmacological therapeutic approaches. Specifically, noninvasive brain stimulation using repetitive transcranial magnetic stimulation (rTMS) has demonstrated promise in improving cognition in MCI and AD.

Our study will test the feasibility of using intermittent theta burst stimulation (iTBS) technique of rTMS in MCI, pilot test the study design, and collect pilot data on the effect of iTBS over three different brain regions on working memory, new learning, and executive function in MCI. Exploratory objectives are to assess the feasibility and usefulness of functional magnetic resonance imaging (fMRI), high-density electroencephalography (HD-EEG), and sleep architecture as potential biomarkers in response to iTBS.

A pilot randomized double-blind controlled cross-over trial of iTBS on 20 MCI participants randomized to 10 days of active iTBS (left dorsolateral prefrontal cortex or left lateral parietal cortex) or control (vertex). After 4-6-week washout period, they cross over to the alternative treatment arm for another 10 days. Each participant will undergo a total of 20 iTBS sessions. Pre- and post-iTBS assessments include neuropsychological tests, fMRI, HD-EEG, and sleep architecture.

This innovative study aims to test the feasibility of iTBS as a cognitive enhancement strategy in MCI. If our study is feasible, it could lead to a future larger trial to further test whether iTBS can modulate underlying neurobiology and offer a therapeutic avenue to remediate cognitive decline in MCI or ultimately delay progression to dementia.

ClinicalTrials.gov, NCT05327257. Registered 04 April 2022.

Magnetic resonance imaging (MRI) often uses gadolinium-based contrast agents (GBCAs) to improve the characterization of imaging contrast, owing to their strong paramagnetic properties. Magnetic resonance electrical properties tomography (MREPT) visualizes the conductivity distribution of biological tissues at the Larmor frequency using the [Formula: see text] field phase signal. In this paper, we investigate the effect of GBCA on brain conductivity. To compare the differences of reconstructed noisy conductivity maps before and after the GBCA injection, we propose a method to remove the background low-frequency noise artifact based on an elliptic partial differential equation. By analyzing the relationship between electrical conductivity and magnetic permeability, the objective of this study is to develop a cost-effective and accessible initial screening imaging tool for diagnosing and monitoring the treatment of Alzheimer's disease (AD) pathophysiology. To investigate vascular damage in AD, we define a conductivity heterogeneity volume fraction (CHVF) caused by GBCA leakage. Using CHVF, we develop three indices to characterize mild cognitive impairment (MCI) and AD. To verify the proposed method, we studied a total of 42 participants, including 14 individuals diagnosed with AD, 18 participants with MCI, and 10 cognitively normal (CN) participants. Finally, we designed a radar chart informed by the CHVF analysis, to exhibit the pertinent parameters for MCI and AD patients, facilitating the evaluation and ongoing monitoring of each patient's diagnosis and treatment regimen.

Digital cognitive training (DCT) has been found to be more effective than traditional paper-and-pencil training in enhancing overall cognitive function. However, a significant barrier to its long-term implementation is that older adults with mild cognitive impairment (MCI) do not continue to use it or even show a dropoff in usage after the initial engagement. Such short-term engagement may limit the potential benefits of DCT, as sustained use is required to achieve more pronounced cognitive improvements. Exploring the reasons for the shift in discontinuous usage behavior is crucial for promoting successful DCT implementation and maximizing its positive effects.

This study aimed to explore the intrinsic reasons for the transition from initial acceptance to discontinuous usage behavior among older adults with MCI throughout the DCT process, by employing the extended model of IT continuance (ECM-ITC).

We employed a qualitative research methodology and conducted 38 semistructured interviews before and after the use of DCT (3 times per week over 1 month, with each session lasting 30 minutes) with 19 older adults with MCI (aged 60 years or older) and 4 family members between January and March 2024. Thematic analysis and deductive framework analysis were used to identify the reasons for the discontinuous usage of DCT, with mapping to the ECM-ITC.

Most participants failed to complete the standard dosage of DCT. Data analysis revealed the reasons for the shift to discontinuous usage. Despite their need to improve cognitive function, participants found the cognitive training confusing and discovered that DCT did not align with their preferred method of training upon actual use. The disparity between their vague expectations and reality, combined with the contradiction between the "delayed gratification" of DCT and their desire for "immediate gratification," made it difficult for them to discern the usefulness of DCT. Participants also viewed DCT as an additional financial burden and tended to avoid training under family pressure. They relied on motivational measures, which further weakened their intention to continue DCT, ultimately leading to the inability to develop continuous usage behavior.

Continuous usage behavior differs from initial acceptance as it evolves dynamically with user experience over time. To encourage older adults with MCI to persistently engage with DCT, it is essential to not only thoroughly consider their genuine preferences and the potential disruptions DCT may bring to their lives but also bridge the gap between expectations and actual experiences. While ensuring that older adults receive appropriate external incentives and encouragement, it is equally important to foster their intrinsic motivation, thereby gradually cultivating the habit of sustained DCT usage.

BackgroundStructural changes in medial temporal lobes including the fusiform gyrus, a critical area in face recognition, precede the progression of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD). However, how the neural correlates of face processing altered in aMCI, as well as their association with cognitive impairments, remain unclear.ObjectiveUsing electroencephalogram (EEG), we explored the electrophysiological markers of face-specific visual processing alterations in aMCI and examined their relationship with cognitive deficits.MethodsWe recruited participants with aMCI (n = 32) and healthy controls (HC, n = 41) and used a passive viewing task to measure the event-related potential (ERP) in response to faces and non-face objects. To compare face processing in aMCI patients and HCs, we adopted mass univariate analysis and representational similarity analysis (RSA) to explore aMCI-related alterations in ERPs.ResultsWe found that face inversion effect (FIE) in P1 amplitudes was absent in aMCI patients. Also, compared to HCs, aMCI patients exhibited a lack of right hemisphere advantage in N170 in response to faces. Furthermore, representation similarity analysis of ERP in posterior-temporal regions revealed that aMCI patients represent face and non-face objects distinctively from HCs in the early processing stage. Additionally, the FIE in P1 amplitude positively correlated to aMCI patients' visuospatial functions.ConclusionsThese findings showed aMCI-related changes in the early perceptual processing of faces and highlights the potential of the FIE in P1 amplitude and ERP patterns over occipital-temporal regions as electrophysiological markers for aMCI and AD.

Alzheimer disease (AD) is a complex neurodegenerative disorder. Proteomic studies have been instrumental in identifying AD-related proteins present in the brain, cerebrospinal fluid, and plasma. This study comprehensively examined 6,905 plasma proteins in more than 3,300 well-characterized individuals to identify new proteins, pathways, and predictive model for AD. With three-stage analysis (discovery, replication, and meta-analysis) we identified 416 proteins (294 novel) associated with clinical AD status and the findings were further validated in two external datasets including more than 7,000 samples and seven previous studies. Pathway analysis revealed that these proteins were involved in endothelial and blood hemostatic (ACHE, SMOC1, SMOC2, VEGFA, VEGFB, SPARC), capturing blood brain barrier (BBB) disruption due to disease. Other pathways were capturing known processes implicated in AD, such as lipid dysregulation (APOE, BIN1, CLU, SMPD1, PLA2G12A, CTSF) or immune response (C5, CFB, DEFA5, FBXL4), which includes proteins known to be part of the causal pathway indicating that some of the identified proteins and pathways are involved in disease pathogenesis. An enrichment of brain and neural pathways (axonal guidance signaling or myelination signaling) indicates that, in fact, blood proteomics capture brain- and disease-related changes, which can lead to the identification of novel biomarkers and predictive models. Machine learning model was employed to identify a set of seven proteins that were highly predictive of both clinical AD (AUC > 0.72) and biomarker-defined AD status (AUC > 0.88), that were replicated in multiple external cohorts as well as with orthogonal platforms. These extensive findings underscore the potential of using plasma proteins as biomarkers for early detection and monitoring of AD, as well as potentially guiding treatment decisions.

This study identifies neuropsychiatric syndromes and investigates their relationship with neuroimaging in Alzheimer's disease dementia (AD), dementia with Lewy bodies (DLB), and mild cognitive impairment (MCI).

Magnetic resonance imaging and perfusion single-photon emission computed tomography data were collected for 281, 68, and 180 patients with AD, DLB, and MCI, respectively, from three Japanese institutions. Neuropsychiatric Inventory was used for exploratory factor analysis in each group. Statistical Parametric Mapping was exploited to reveal the relationships between each factor score and cerebral volume or perfusion with age, sex, dementia severity, and the other factor scores as covariates.

Three factors (psychosis, agitation, and affective disturbance) were extracted for AD. For DLB, nighttime behavior and aberrant motor behavior were extracted as independent symptoms with the same three factors. Four factors (psychosis, agitation, anxiety, and apathy) were extracted for MCI. A positive relationship between agitation and cerebral volume in the left middle frontal gyri and left caudate was observed in AD. In DLB, agitation was positively correlated with cerebral perfusion in the left dominant regions, including the middle frontal gyri and caudate. Psychosis and perfusion were negatively correlated in the left extent regions, including the temporo-parieto-occipital lobe, insula, and inferior frontal gyri in DLB. Psychosis was significantly associated with lower perfusion in the bilateral occipital lobes, whereas apathy was significantly correlated with a lower volume of the right dominant bilateral frontal lobes in MCI.

Three neuropsychiatric syndromes - psychosis, agitation, and affective disturbance - may heterogeneously associate with AD, DLB, and MCI.

Background: Mild Cognitive Impairment (MCI) is a critical transitional phase between normal aging and dementia, and early detection is essential to mitigate cognitive decline. Traditional cognitive assessment tools, such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), exhibit limitations in feasibility, which potentially and partially affects results for early-stage MCI detection. This study developed and tested a supportive cognitive assessment system for MCI auxiliary identification, leveraging eye-tracking features and convolutional neural network (CNN) analysis. Methods: The system employed eye-tracking technology in conjunction with machine learning to build a multimodal auxiliary identification model. Four eye movement tasks and two cognitive tests were administered to 128 participants (40 MCI patients, 57 elderly controls, 31 young adults as reference). We extracted 31 eye movement and 8 behavioral features to assess their contributions to classification accuracy using CNN analysis. Eye movement features only, behavioral features only, and combined features models were developed and tested respectively, to find out the most effective approach for MCI auxiliary identification. Results: Overall, the combined features model achieved a higher discrimination accuracy than models with single feature sets alone. Specifically, the model's ability to differentiate MCI from healthy individuals, including young adults, reached an average accuracy of 74.62%. For distinguishing MCI from elderly controls, the model's accuracy averaged 66.50%. Conclusions: Results show that a multimodal model significantly outperforms single-feature models in identifying MCI, highlighting the potential of eye-tracking for early detection. These findings suggest that integrating multimodal data can enhance the effectiveness of MCI auxiliary identification, providing a novel potential pathway for community-based early detection efforts.

Abnormalities in brain activity patterns during episodic memory tasks have been inconsistently reported in amnestic mild cognitive impairment (aMCI) patients using functional magnetic resonance imaging (fMRI). This study applied traditional Chinese medicine (TCM) syndrome differentiation to categorize aMCI patients into distinct subgroups, aiming to clarify the neural mechanisms underlying their cognitive profiles.

Participants included aMCI patients categorized into the turbid phlegm clouding the orifices (PCO) or spleen-kidney deficiency (SKD) syndrome subgroups, alongside cognitively normal controls (NC) matched for age and gender. Neuropsychological assessments were performed, and fMRI scans were acquired during an episodic memory task involving the recognition of new and old vocabulary. Brain activity across different stages of episodic memory was analyzed using SPM12 and DPABI 7.0 software.

A total of 57 aMCI patients (34 with SKD and 23 with PCO) and 54 healthy controls were involved in the final task-based fMRI analysis. Compared with the NC group, the PCO group exhibited increased brain activation during both encoding and retrieval phases, primarily involving the prefrontal cortex and occipital lobe. Compared with the SKD group, the PCO group demonstrated the elevated activation in the right central sulcus and right insula during the encoding phase. Correlation analysis indicated a specific association between PCO symptom scores and insula activation. No statistically significant differences were found between the SKD and NC groups.

Distinct patterns of fMRI brain activity found in aMCI patients with PCO and SKD syndromes during episodic memory tasks suggest differing neural mechanisms that may contribute to the clinical heterogeneity of aMCI.

Minor neurocognitive disorders (NCDs) represent a transitional phase between normal cognitive aging and dementia, highlighting the importance of early interventions. This study assessed the efficacy of a structured 6-month computerized cognitive training (CCT) program in stabilizing cognitive decline among older adults with minor NCDs. One hundred participants were randomly assigned to an intervention group or a non-intervention group. The intervention group underwent weekly, personalized CCT sessions using the MeMo program, which targeted memory, attention, and adaptability. Cognitive performance was measured at baseline and after six months using the Cambridge Cognitive Examination (CAMCOG). Statistical analysis showed significant cognitive decline in the non-intervention group in orientation (p = 0.032), language expression (p = 0.008), praxis (p = 0.008), and memory (p = 0.01). In contrast, the intervention group showed no significant changes, except for a minor decline in perception (p = 0.003). These results suggest that CCT may help delay cognitive deterioration in minor NCDs. However, while cognitive decline was stabilized, no significant improvement was observed. Further research is recommended to investigate the long-term benefits and the transferability of cognitive gains. The findings support the use of CCT as a non-pharmacological health promotion strategy for enhancing cognitive resilience in aging populations. The novelty of this research lies in its focus on adaptive CCT as a non-pharmacological intervention, highlighting the potential role of neuroplasticity in delaying cognitive decline and offering new insights into personalized cognitive health strategies for aging populations.

Type 2 diabetes mellitus (T2DM) is a significant risk factor for cognitive impairment. Zinc deficiency contributes to T2DM development, while copper may exacerbate diabetes through prooxidant mechanisms. Higher zinc levels may protect against copper toxicity. This study investigates the association of plasma zinc and copper levels with mild cognitive impairment (MCI) in T2DM patients.

T2DM patients admitted to Tongji Hospital from 2012 to 2018 were classified into MCI (n = 136) and control (n = 136) groups, matched by age (± 3 years) and gender. Conditional logistic regression was used to assess the associations between plasma zinc, copper levels and MCI. A generalized additive model (GAM) evaluated the dose-response relationship between plasma zinc, copper levels and Mini-Mental State Examination (MMSE) scores.

The median of plasma metal levels in MCI and control groups were 831.31 μg/L and 936.29 μg/L for zinc, 932.07 μg/L and 860.47 μg/L for copper, and 0.91 and 1.11 for the zinc-to-copper (Zn/Cu) ratio. Compared to participants in the lowest tertile, the multivariable-adjusted odds ratios with 95% confidence intervals (CI) for MCI in the highest tertile were 0.33 (0.13, 0.79) for zinc, 3.56 (1.42, 8.94) for copper, and 0.37 (0.15, 0.93) for the Zn/Cu ratio. Plasma Aβ40 levels were significantly lower (p = 0.009) and plasma Aβ42/40 levels were significantly higher (p = 0.008) in MCI group compared with those in control group. Zinc concentration was positively associated with Aβ42. For per SD (327.71 μg/L) increase in plasma zinc levels, the percent change (95% CI) of Aβ42 were 2.90 (0.85, 4.99).

Higher plasma zinc levels and higher Zn/Cu ratio were associated with lower odds of MCI in T2DM patients, while higher copper levels increased the risk of MCI. This study provides insights on plasma zinc, copper, and Zn/Cu ratio and Aβ of MCI, further studies are needed to clarify the underlying mechanisms for novel therapies that could prevent or cure multiple T2DM-related cognitive impairments.

Definitive structural elucidation of lipids is pivotal for unraveling the functions of lipids in biological systems. Despite advancements in mass spectrometry (MS) for lipid analysis, challenges in annotation scope and efficiency remain, especially in resolving isomers. Herein, we introduce an optimized method using liquid chromatography coupled with electron impact excitation of ions from organic tandem mass spectrometry (LC-EIEIO-MS/MS) for comprehensive analysis and structural annotation of lipids. This approach integrates a six-step analytical protocol for precise lipid annotation, including (1) extracting MS information, (2) classifying lipids, (3) aligning sum composition, (4) determining sn-positions, (5) locating C═C positions, and (6) ascertaining annotation levels. In analyzing 34 lipid standards spiked into serum, our method achieved 100% and 82.4% annotation accuracy at the sn- and C═C isomer levels, respectively, compared to 26.5% and 0% in the CID mode using MS-DIAL. A total of 1312 sn-positions and 1033 C═C locations of lipids were annotated in quality control plasma pooled from healthy individuals and patients with Alzheimer's disease. The isomers of lipids revealed more pronounced differences between the healthy and diseased groups compared to the sum compositions of the lipids. Overall, the LC-EIEIO-MS/MS approach provides a comprehensive profiling and efficient annotation method for lipidomics, promising to shed new light on lipid-related biological pathways and disease mechanisms.

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer's disease (AD). This study aimed to compare deep learning (DL) and traditional machine learning (ML) methods in predicting MCI using plasma proteomic biomarkers. A total of 239 adults were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with a pool of 146 plasma proteomic biomarkers. We evaluated seven traditional ML models (support vector machines (SVMs), logistic regression (LR), naïve Bayes (NB), random forest (RF), k-nearest neighbor (KNN), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) and six variations of a deep neural network (DNN) model-the DL model in the H2O package. Least Absolute Shrinkage and Selection Operator (LASSO) selected 35 proteomic biomarkers from the pool. Based on grid search, the DNN model with an activation function of "Rectifier With Dropout" with 2 layers and 32 of 35 selected proteomic biomarkers revealed the best model with the highest accuracy of 0.995 and an F1 Score of 0.996, while among seven traditional ML methods, XGBoost was the best with an accuracy of 0.986 and an F1 Score of 0.985. Several biomarkers were correlated with the APOE-ε4 genotype, polygenic hazard score (PHS), and three clinical cerebrospinal fluid biomarkers (Aβ42, tTau, and pTau). Bioinformatics analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed several molecular functions and pathways associated with the selected biomarkers, including cytokine-cytokine receptor interaction, cholesterol metabolism, and regulation of lipid localization. The results showed that the DL model may represent a promising tool in the prediction of MCI. These plasma proteomic biomarkers may help with early diagnosis, prognostic risk stratification, and early treatment interventions for individuals at risk for MCI.

Background: Mild cognitive impairment (MCI) represents an early stage of cognitive loss that significantly increases the risk of dementia. The aim of this study was to comprehensively synthesize the current evidence on the effect of combined physical and cognitive therapies in older adults with MCI. Methods: A systematic review with meta-analysis was conducted by searching for specific keywords in the PubMed, Scopus, Cinhal, and Web of Science databases. This meta-analysis included a total of 2256 participants distributed across 21 studies that evaluated the benefits of combining physical exercise with cognitive stimulation. Results: This review revealed that these types of therapies present a significant improvement in memory, attention, and executive functions. Participants showed notable improvements in these cognitive areas, highlighting the synergistic effects of physical exercise and cognitive stimulation, which exceeded the benefits of each therapy separately. These results contribute to the understanding of how these combined therapies can improve cognitive health in this population, offering robust evidence supporting their application in clinical practice. Conclusions: This meta-analysis shows that combined physical exercise and cognitive stimulation interventions may be an effective strategy for improving cognitive health in older adults with MCI. The findings of this study offer a valuable contribution to the field, highlighting the potential of these combined therapies to prevent cognitive decline and improve the quality of life of this population. The results may be of interest to health professionals and guide future research and clinical applications.

Mild cognitive impairment (MCI) is a slight cognitive decline with the ability to perform normal activities in daily life and an increased risk of dementia. Land-based exercise enhances cognitive abilities, but combining cognitive and physical interventions offers greater benefits in MCI. Water-based exercise is a low-impact activity that minimizes joint strain and reduces injury risk. This study investigated the effects of water-based exercise with or without cognitive training on cognition in older adults with MCI. Thirty-seven participants aged 65 years or older with MCI were randomly assigned to two groups: water-based exercise (W; n = 18) and water-based exercise combined with cognitive training (W-COG; n = 19). Both groups performed 60 min water-based aerobic sessions 3 days per week for 12 weeks. Cognitive assessments were conducted at baseline and after 12 weeks. Post-intervention, 77.77% of the W group and 89% of the W-COG group had improved their cognitive ability. Both groups showed significant improvements in their global cognition (p < 0.001, p < 0.001) and visuospatial ability (p < 0.01 for W, p < 0.05 for W-COG), respectively. In addition, cognitive flexibility and shifting abilities improved only in the W-COG group (p < 0.05). These findings suggest that water-based exercise combined with cognitive training enhances cognitive functions more effectively than exercise alone in older adults with MCI.

Physical activity (PA) is a nonpharmacological intervention for dementia prevention. The association between PA and Alzheimer disease (AD) plasma biomarkers remains underexplored.

To investigate the associations among PA; plasma biomarkers, including β-amyloid 42/40 (Aβ42/40), phosphorylated-tau217 (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL); and cognition.

This cross-sectional study included participants with and without cognitive impairment recruited from multiple memory clinics in South Korea between May 2019 and May 2022. Data were analyzed from June to December 2024.

PA was assessed as metabolic equivalent task minutes per week using the International Physical Activity Questionnaire and categorized into quartiles from the lowest (Q1) to the highest (Q4).

Plasma Aβ42/40, ptau217, GFAP, and NfL were measured. Cognition was assessed using the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating-Sum of Boxes (CDR-SB).

Among 1144 participants (mean [SD] age 70.9 [8.7] years; 744 [65.0%] female), the highest PA quartile showed significantly lower ptau217 (estimate [SE], -0.14 [0.06]; P = .01) and NfL (estimate [SE], -0.12 [0.05]; P = .01) compared with the lowest quartile. Higher PA quartiles were associated with higher MMSE scores (estimate [SE]: Q2, 0.93 [0.31]; P = .003; Q3, 0.82 [0.32]; P = .009; Q4, 0.94 [0.32]; P = .004) and lower CDR-SB scores (estimate [SE]: Q2, -0.33 [0.16]; P = .04; Q3, -0.37 [0.16]; P = .02; Q4, -0.55 [0.16]; P = .001) after adjusting for age, sex, education years, and β-amyloid uptake. In subgroup analyses according to age and cognitive status, the associations of PA and plasma biomarkers with cognition were more pronounced in the older (age ≥65 years) and cognitively impaired groups compared with the younger and cognitively unimpaired groups.

These findings suggest that PA may help delay cognitive decline by modulating neurodegeneration and AD-specific tau pathologies. However, the cross-sectional design limits causal inference, and longitudinal studies are needed to confirm and clarify these associations.

The frequent presentation of Alzheimer's disease (AD) with neuropsychiatric symptoms (NPS) in the context of normal or minimally-impaired cognitive function led to the concept of Mild Behavioral Impairment (MBI). While MBI's impact on subsequent cognitive decline is recognized, its association with brain network changes in biologically-defined AD remains unexplored.

To investigate the correlation of structural covariance networks with MBI-C checklist sub-scores in biologically-defined AD patients.

We analyzed 33 biologically-defined AD patients, ranging from mild cognitive impairment to early dementia, all characterized as amyloid-positive through cerebrospinal fluid analysis or amyloid positron emission tomography scans. Regional network properties were assessed through graph theory.

Affective dysregulation correlated with decreased segregation and integration in the right inferior frontal gyrus (IFG). Impulse dyscontrol and social inappropriateness correlated positively with centrality and efficiency in the right posterior cingulate cortex (PCC). Global network properties showed a preserved small-world organization.

This study reveals associations between MBI subdomains and structural brain network alterations in biologically-confirmed AD. The IFG's involvement is crucial for mood dysregulation, while the PCC could be involved in compensatory mechanisms for social cognition and impulse control. These findings underscore the significance of biomarker-based neuroimaging for the characterization of NPS across the AD spectrum.

Computerized cognitive training tools are an alternative to preventive treatments related to cognitive impairment and aging. In this study, the transfer of 3D multiple object tracking (3D-MOT) training on manual dexterity concerning fine and gross motor skills in 38 elderly participants, half of them with mild cognitive impairment (MCI) and the other half with mild dementia (MD) was explored. A total of 36 sessions of the 3D-MOT training program were administered to the subjects. The Montreal Cognitive Assessment (MoCA) test was used to assess the baseline cognitive status of the participants. Two batteries of manual motor skills (GPT and MMDT) were applied before and after the 3D-MOT training program. The results showed an interaction effect of training and improvement in manual dexterity tests, from the first training session until the fifteenth session, and after this range of sessions, the interaction effect was lost. However, the training effect continued to the end of the thirty-six-session program. The experimental results show the effect of cognitive training on the improvement of motor skills in older adults. This type of intervention could have a broad impact on the aging population in terms of their attention, executive functions, and therefore, their quality of life.

Amyotrophic lateral sclerosis (ALS) is a fatal multi-system neurodegenerative disorder with no effective treatment or cure. Although primarily characterized by motor degeneration, cognitive dysfunction is an important non-motor symptom that has a negative impact on patient and caregiver burden. Mild cognitive deficits are present in a subgroup of non-demented patients with ALS, often preceding motor symptoms. Detailed neuropsychological assessments reveal deficits in a variety of cognitive domains, including those of verbal fluency and retrieval, language, executive function, attention and verbal memory. Mild cognitive impairment (MCI), a risk factor for developing dementia, affects between 10% and over 50% of ALS patients. Neuroimaging revealed atrophy of frontal and temporal cortices, disordered white matter Integrity, volume reduction in amygdala and thalamus, hypometabolism in the frontal and superior temporal gyrus and anterior insula. Neuronal loss in non-motor brain areas, associated with TDP-43 deposition, one of the morphological hallmarks of ALS, is linked to functional disruption of frontostriatal and frontotemporo-limbic connectivities as markers for cognitive deficits in ALS, the pathogenesis of which is still poorly understood. Early diagnosis by increased cerebrospinal fluid or serum levels of neurofilament light/heavy chain or glial fibrillary acidic protein awaits confirmation for MCI in ALS. These fluid biomarkers and early detection of brain connectivity signatures before structural changes will be helpful not only in establishing early premature diagnosis but also in clarifying the pathophysiological mechanisms of MCI in ALS, which might serve as novel targets for prohibition/delay and future adequate treatment of this debilitating disorder.

Particle swarm optimization (PSO) is an algorithm that involves the optimization of Non-linear and Multidimensional problems to reach the best solutions with minimal parameterization. This metaheuristic model has frequently been used in the Pathological domain. This optimization model has been used in diverse forms while predicting Alzheimer's disease. It is a robust algorithm that works on linear and multi-modal data while predicting Alzheimer's disease. PSO techniques have been in action for quite some time for detecting various diseases and this paper systematically reviews the papers on various kinds of PSO techniques.

To perform the systematic review, PRISMA guidelines were followed and a Boolean search ("particle swarm optimization" OR "PSO") AND Neuroimaging AND (Alzheimer's disease prediction OR classification OR diagnosis) were performed. The query was run in 4-reputed databases: Google Scholar, Scopus, Science Direct, and Wiley publications.

For the final analysis, 10 papers were incorporated for qualitative and quantitative synthesis. PSO has shown a dominant character while handling the uni-modal as well as the multi-modal data while predicting the conversion from MCI to Alzheimer's. It can be seen from the table that almost all the 10 reviewed papers had MRI-driven data. The accuracy rate was accentuated while adding other modalities or Neurocognitive measures.

Through this algorithm, we are providing an opportunity to other researchers to compare this algorithm with other state-of-the-art algorithms, while seeing the classification accuracy, with the aim of early prediction and progression of MCI into Alzheimer's disease.

Mild behavioral impairment (MBI) represents a recently introduced diagnostic concept that focuses on behavioral and personality changes occurring in late life and associated with cognitive decline. Nevertheless, the relationship between these dimensions remains unclear. This systematic review and meta-analysis aim to analyze the relationship between MBI and cognitive functioning. The review process was conducted according to the PRISMA-Statement. Restrictions were made, selecting the studies published in peer-review journals, including at least one cognitive measure and presenting the measurement of MBI. Studies that included participants with neurological disorders, dementia, or psychiatric disorders or that only did a neuroimaging or genetic study were excluded. Twenty-two studies were included in the systematic review, while in the meta-analysis seventeen studies featured data to be included in the analyses. The results were classified according to the following cognitive domains: global cognitive functioning, memory, language, attention executive functions, visuospatial skills, and processing speed. In the quantitative analysis, only global cognitive functioning, executive function, attention, and memory were evaluated. The results of both qualitative and quantitative analysis indicate that individuals with MBI exhibited diminished performance on cognitive tasks when compared to those without MBI symptoms. These results are stronger when evaluating the various domains individually (particularly memory and executive functions) than when a global assessment was made. These findings highlight the potential role of MBI symptoms as early indicators of neurodegenerative processes, reinforcing the necessity for comprehensive assessments that encompass both behavioral and cognitive evaluations. The early detection of these symptoms in prodromal phases can be very useful for the development of non-pharmacological interventions and may provide relevant guidelines for clinicians in the management and diagnosis of neurodegenerative disorders.

This study was undertaken to evaluate the diagnostic performance of a novel plasma phosphorylated tau (p-tau) 217/amyloid beta (Aβ) 42 ratio test for Alzheimer's disease (AD).

The diagnostic performance of the Lumipulse G plasma p-tau217/Aβ42 ratio was evaluated using Aβ and tau positron emission tomography (PET) as reference standards in a clinic cohort (n = 391) and a community cohort (n = 121).

Plasma p-tau217/Aβ42 exhibited high performance for abnormal statuses of Aβ PET (area under the curve [AUC]: 0.963 to 0.966) and tau PET (AUC: 0.947 to 0.974), which were clinically equivalent to those of cerebrospinal fluid (CSF) p-tau181/Aβ42 and Aβ42/Aβ40 and higher than those of blood p-tau217, Aβ42/Aβ40, p-tau181, and p-tau181/Aβ42 in both clinic and community cohorts. Applying a two-cutoff approach improved the specificity without reducing sensitivity. The p-tau217/Aβ42 ratio had a lower intermediate percentage than p-tau217 alone in both clinic (10.6% vs 13.0%) and community (16.5% vs 31.4%) cohorts.

Plasma p-tau217/Aβ42 has high performance in detecting cerebral AD pathologies, thus offering a promising tool for clinical diagnosis and community screening of AD.

Lumipulse G plasma p-tau217 and the p-tau217/Aβ42 ratio accurately identified abnormal Aβ and tau PET statuses in both clinical and community cohorts. The performance of plasma p-tau217 and p-tau217/Aβ42 ratio were equivalent to CSF tests. Plasma p-tau217/Aβ42 ratio outperformed p-tau217 alone in identifying Aβ PET positivity, and this superiority is more obvious in the community cohort, suggesting an advantage in the early diagnosis of AD. Two cut points of p-tau217/Aβ42 were established in the Chinese population for clinical laboratory and community screening uses.

Prospective memory (PM) is the ability to remember to perform planned actions in a future moment and it is of fundamental importance for an independent and autonomous lifestyle from development to late adulthood. Deficits in episodic memory and executive functions, which are involved in PM are characteristic features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Considering that the number of older adults is drastically increasing over the next decades, it is of great interest to understand how PM decline in healthy older adults and patients with different degree of cognitive decline. The present meta-analysis included 46 studies investigating PM performance in AD patients (17 studies) and people with MCI (24 studies); 5 studies included both clinical conditions in the same article. The 46 studies contributed a total of 63 independent samples and 129 effect sizes from 4668 participants (2115 patients and 2553 controls). Unlike previous reviews of the literature, our results with a larger and updated sample of studies confirmed lower PM abilities in AD compared to MCI and controls, although we did not observe conclusive differences between event-based and time-based PM in patients. Surprisingly, PM deficits shown by MCI and AD patients have decreased across years, in parallel to a reduction of the evidence of publication bias and an increase in the number of observations per task. We propose the use of more reliable research designs as one plausible explanation for the reduction of PM impairments.

People with mild cognitive impairment (MCI) carry a considerable risk of developing dementia. Studies have shown that female sex hormones have long-lasting neuroprotective and anti-aging properties, and the increased risk of MCI and AD is associated with the lack of estrogen during menopause. Previous studies have shown that Tiao Geng Decoction (TGD) may have antioxidant and anti apoptotic properties, which may prevent neurodegenerative diseases. However, whether TGD is effective in improving mild cognitive impairment due to postmenopausal estrogen deficiency and its potential pharmacological mechanisms remain unclear. The aim of this study was to investigate the possible pharmacological mechanisms of TGD in preventing postmenopausal MCI. We utilized RNA-seq technology to screen for differentially expressed genes (DEGs) and enrichment pathways in the hippocampal tissue of different groups of mice. Additionally, we adopted single-cell sequencing technology to study the cell types of Alzheimer's disease (AD) group and Normal Control (NC) group, the differential marker genes of each cell subgroup, and the GO enrichment analysis of each cell type. Both RNA sequencing and single-cell sequencing results showed a significant correlation between TGD and NF-κb pathway in improving mild cognitive impairment in postmenopausal women. The experimental verification results showed that the spatial learning and memory abilities of APP/PS1 model mice were weakened after ovariectomy, and the reproductive cycle on vaginal smears was in the interphase of diestrus. The levels of serum E2, and P-tau181 in mice were significantly down regulated, while the levels of brain tissue homogenate A β 42, IL-1 β, and IL-18 were significantly up-regulated, indicating successful modeling. Combining Western blotting, RT-qPCR, and transmission electron microscopy analyses, it was found that the low estrogen environment induced by oophorectomy can activate the NF-κb signaling pathway, activate the expression of NLRP3 inflammasome and A β secretase BACE1, and induce neuroinflammatory damage in hippocampal astrocytes. These results conform to the modeling characteristics of MCI. After TGD intervention, the spatial learning and memory abilities of MCI mice were significantly improved. The pharmacological validation results indicated that high concentration doses of TGD had a more significant effect on MCI. Subsequently, we used high concentration TGD (0.32 g/ml) as the traditional Chinese medicine group for further validation, protein blotting and RT-qPCR results indicated that TGD can effectively stimulate the secretion of ER α and ER β, inhibit the NF-κb pathway, downregulate BACE1, and inhibit the expression of NLRP3 inflammasome related proteins. In addition, the immunofluorescence results of hippocampal astrocytes showed that TGD can effectively facilitate the expression of AQP1 and significantly lower the sedimentation of A β compared with the model group. Our research suggests that there is a high correlation between a low estrogen environment and the occurrence and development of MCI. TGD may regulate the ERs/NF - κ b/AQP1 signaling pathway, promote estrogen secretion, activate AQP1, reduce A β deposition, reverse MCI neuroinflammatory injury, improve mild cognitive impairment, and prevent the occurrence of AD. This study revealed for the first time that TGD may be a potential new alternative drug for preventing and improving menopausal MCI.

Alzheimer's Disease (AD) is a debilitating neurocognitive disorder with an unclear underlying mechanism. Recent studies have implicated gut microbiota dysbiosis with the onset and progression of AD. The connection between gut microbiota and AD can significantly affect the prevention and treatment of AD patients. This systematic review summarizes primary outcomes of human and mouse AD models concerning gut microbiota alterations. A systematic literature search in February through March 2023 was conducted on PubMed, Embase, and Web of Science. We identified 711 as potential manuscripts of which 672 were excluded because of irrelevance to the identified search criteria. Primary outcomes include microbiota compositions of control and AD models in humans and mice. In total, 39 studies were included (19 mouse and 20 human studies), published between 2017 and 2023. We included studies involving well-established mice models of AD (5xFAD, 3xTg-AD, APP/PS1, Tg2576, and APPPS2) which harbor mutations and genes that drive the formation of Aß plaques. All human studies were included on those with AD or mild cognitive impairment. Among alterations in gut microbiota, most studies found a decreased abundance of the phyla Firmicutes and Bifidobacteria, a genus of the phylum Actinomycetota. An increased abundance of the phyla Bacteroidetes and Proteobacteria were identified in animal and human studies. Studies indicated that gut microbiota alter the pathogenesis of AD through its impact on neuroinflammation and permeability of the gastrointestinal tract. The ensuing increase in blood-brain barrier permeability may accelerate Aβ penetrance and formation of neuritic plaques that align with the amyloid hypothesis of AD pathogenesis. Further studies should assess the relationship between gut microbiota and AD progression and therapy preserving beneficial gut microbiota.

Individuals with mild cognitive impairment (MCI) exhibit poorer performance in cognition and dual-task paradigm, while the related cortical thickness and surface area alterations remains unclear.

Thirty participants with MCI and thirty healthy controls (HC) were recruited. Magnetic resonance imaging, cognitive assessments and dual-task Timed Up and Go test (DT-TUG) were performed to assess cerebral cortical thickness and surface area, cognitive functions, and dual-task cost (DTC) of the execution time in TUG. Spearman correlations were conducted to assess the relationships between the cognitive, TUG performance with the cortical morphological measures.

MCI participants performed worse in the Montreal cognitive assessment (MoCA), WAIS Digit Span, TMT and the modified Posner peripheral cuing task. Their execution time on the DT-TUG was also prolonged. WAIS Digit Span Backwards was correlated with DT-TUG in HC group. A significant between-group difference was observed in the surface area of the left SPC. The cortical thickness of this brain region was positively correlated with the total scores and attention subdomain of MoCA in HC group. The cortical thickness and the surface area were correlated with the time of DT-TUG in HC group only.

Individuals with MCI demonstrated declines in both cognitive function and dual-task walking performance. This study provides further evidence of surface-based structural differences in the left SPC in individuals with and without MCI, and supports the role of the left SPC in cognition and dual-task walking.

We aimed to compare amyloid-β (Aβ) accumulation rates between different tracers and investigate whether the relationship between changes in Aβ uptake and cognitive decline varies depending on tracer type. Two cohorts were analyzed: (1) a head-to-head longitudinal cohort using 18F-Florbetaben (FBB) and 18F-Flutemetamol (FMM) tracers (n = 13), and (2) separate longitudinal cohorts for each tracer (n = 174 for both FMM and FBB), matched by propensity score. Aβ uptake was measured using regional direct comparison of Centiloid (rdcCL) values. In the head-to-head cohort, subtracting changes in FMM rdcCL from FBB rdcCL yielded median values above zero in all regions except the cingulate. In the individual tracer cohorts, FBB rdcCL showed faster accumulation than FMM rdcCL in all cortical regions except the striatum (β [SE] =  - 2.49 to - 1.56 [0.47-0.54], p < 0.001). Mini-Mental State Examination changes were associated with annualized FMM rdcCL changes in the temporal cortex (p = 0.02) and striatum (p = 0.01); however, no such differences were found in the FBB cohort. Our findings suggest that longitudinal Aβ positron emission tomography studies should consider the specific characteristics of tracers depending on the context of use.

Background: Healthy cognitive functioning is a primary component of well-being, independence, and successful aging. Cognitive deficits can arise from various conditions, such as brain injury, mental illness, and neurological disorders. Rehabilitation is a highly specialized service limited to patients who have access to institutional settings. In response to this unmet need, telehealth solutions are ideal for triggering the migration of care from clinics to patients' homes. Objectives: The aim of EARLY-COGN^3 will be threefold: (1) to test the efficacy of a digital health at-home intervention (tele@cognitive protocol) as compared to an unstructured cognitive at-home rehabilitation in a cohort of patients with Chronic Neurological Diseases (CNDs); (2) to investigate its effects on the biomolecular and neurophysiological marker hypothesizing that people with CNDs enrolled in this telerehabilitation program will develop changes in biological markers and cortical and subcortical patterns of connectivity; (3) to analyze potential cognitive, neurobiological, and neurophysiological predictors of response to the tele@cognitive treatment. Method: In this single-blind, randomized, and controlled pilot study, we will assess the short- and long-term efficacy of cognitive telerehabilitation protocol (tele@cognitive) as compared to an unstructured cognitive at-home rehabilitation (Active Control Group-ACG) in a cohort of 60 people with Mild Cognitive Impairment (MCI), Subjective Cognitive Complaints (SCCs), or Parkinson's Disease (PD). All participants will undergo a clinical, functional, neurocognitive, and quality of life assessment at the baseline (T0), post-treatment (5 weeks, T1), and at the 3-month (T2) follow-up. Neurophysiological markers and biomolecular data will be collected at T0 and T1. Conclusions: EARLY-COGN^3 project could lead to a complete paradigm shift from the traditional therapeutic approach, forcing a reassessment on how CNDs could take advantage of a digital solution. (clinicaltrials.gov database, ID: NCT06657274).

Aβ deposition in the brain does not necessarily lead to cognitive impairment, and that blood supply may have other unexplained regulatory effects on Aβ. Therefore, there appears to be a more complex relationship between blood supply, Aβ deposition, and cognitive impairment that warrants further exploration.

This cohort study collected four longitudinal follow-up datasets, including a total of 281 subjects, followed for four years. Three-dimensional time-of-flight angiography and pseudo-continuous arterial spin labeling were used to assess hippocampal vascularization pattern (VP) and hippocampal cerebral blood flow (CBF). 11 C-Pittsburgh compound B (PiB)-PET/CT-based spatial measurements were used detect hippocampal PiB uptake as a reflection of hippocampal Aβ deposition. We explored the relationships between hippocampal blood supply (VP and CBF), hippocampal PiB uptake, and the occurrence of mild cognitive impairment (MCI) using a generalized nonlinear model.

We demonstrated the synergistic effect of hippocampal VP and CBF on predicting the occurrence of MCI. We conducted confirmation and quantification of the relationship between hippocampal blood supply and hippocampal PiB uptake. Additionally, the predicted value of PiB uptake based on hippocampal blood supply not only exhibited strong predictive efficacy for the occurrence of MCI (AUC = 0.831, p < 0.001), but was also validated in cerebral small vessel disease cohorts (AUC = 0.792, p < 0.001) and well validated in an independent cohort (Kappa = 0.741, p < 0.001).

Overall, we reveal that hippocampal blood supply at baseline can regulate hippocampal PiB uptake, which reflects hippocampal tolerable amount of Aβ deposition and serves as an effective predictor for the occurrence of MCI, providing an important extension on the relationship between hippocampal blood supply and Aβ deposition.

Mild cognitive impairment (MCI) is an early stage of Alzheimer's disease (AD), crucial for early diagnosis. BIN1, a key AD susceptibility gene after APOE, has higher brain expression in AD and interacts with tau, affecting its pathology. Specific BIN1 SNPs are linked to AD and MCI, but mechanisms are unclear. This study will explore how BIN1 polymorphisms might influence MCI development and correlate with hippocampal integrity in MCI patients using MRI.

This study enrolled a total of 52 elderly individuals with MCI and 55 cognitively CN individuals from five communities in Zhongshan Torch Development Zone. Blood samples were collected for analysis of BIN1 rs10200967, rs1060743, and rs4663093 gene polymorphisms, and MRI scans were conducted to assess the volume of hippocampal subregions. The study also seeks to examine the distribution of BIN1 genotypes in both MCI and healthy control populations, as well as to investigate the potential association between BIN1 rs10200967, rs1060743, and rs4663093 genotypes and hippocampal subregion structure in individuals with MCI.

Significant structural atrophy was observed in multiple hippocampal subregions, including left cornu ammonis (lCA), left dentate gyrus (lDG), left hippocampal-amygdaloid transition area (lHATA), left subiculum (lSubc), right ornu ammonis (rCA), right dentate gyrus (rDG), right subiculum (rSubc), left entire hippocampus complex (lHIP), and right entire hippocampus complex (rHIP) in seniors with MCI compared to those in the CN (p < 0.05), after adjusting for age, gender, education level, and APOEε4 status. Conversely, no significant differences were observed in left entorhinal cortex (lEC), right entorhinal cortex (rEC), right hippocampal-amygdaloid transition area (rHATA), and total intracranial volume (TIV) (p > 0.05). Notably, there were no significant differences in the distribution of BIN1 rs10200967, rs1060743, and rs4663093 genotypes among elderly individuals (p > 0.05). Furthermore, the association between the BIN1 rs10200967 genotype and lHATA atrophy significant in the MCI after adjusting for age, gender, education level, APOEε4 status, and TIV (p < 0.05).

This study presents novel findings indicating an association between the BIN1 rs10200967 genotype and lHATA atrophy, with the rs10200967 CC genotype showing a higher volume of lHATA in individuals with MCI. These results suggest that the rs10200967 CC genotype may confer a protective effect against MCI, offering a potential basis for early detection and prevention of AD.

Background/Objectives: This study evaluates the potential of electroencephalography (EEG) as a noninvasive tool for distinguishing between healthy individuals (n = 79), those with mild cognitive impairment (MCI; n = 36), and dementia patients (n = 7). Methods: Using a 14-channel Emotiv EPOC-X headset, we analyzed power spectral density during a 2-min eyes-closed resting state. Results: Our results demonstrated that while EEG effectively differentiated dementia patients from healthy controls, it did not show significant differences between MCI and healthy controls. This indicates that EEG holds promise for identifying advanced cognitive decline but faces challenges in early-stage detection. Conclusions: The study contributes to the growing body of literature by highlighting EEG's potential as a cost-effective alternative to invasive diagnostic methods while also identifying the need for larger sample sizes and task-oriented approaches to improve its diagnostic precision.

Mild cognitive impairment (MCI) is characterized by early symptoms of attentional decline and may be distinguished through motor learning results. A relationship was reported between dexterous hand movements and cognitive function in older adults. Therefore, this study focuses on motor learning involving dexterous hand movements. As motor learning engages two distinct types of attention, external and internal, we aimed to develop an evaluation method that separates these attentional functions within motor learning. The objective of this study was to develop and verify the effectiveness of this evaluation method. The effectiveness was assessed by comparing two motor learning variables between a normal cognitive (NC) and MCI groups.

To evaluate motor learning through dexterous hand movements, we utilized the iWakka device. Two types of visual tracking tasks, repeat and random, were designed to evaluate motor learning from different aspects. The tracking errors in both tasks were quantitatively measured, and the initial and final improvement rates during motor learning were defined as the evaluation variables. The study included 28 MCI participants and 40 NC participants, and the effectiveness of the proposed method was verified by comparing results between the groups.

The repeat task revealed a significantly lower learning rate in MCI participants (p <0.01). In contrast, no significant difference was observed between MCI and NC participants in the random task (p =0.67).

The evaluation method proposed in this study demonstrated the possibility of obtaining evaluation variables that indicate the characteristics of MCI.

The methods proposed in this work are clinically relevant because the proposed evaluation system can make evaluation variables for discriminating cognitive decline in MCI. That it, the proposed approach can also be used to provide discrimination for cognitive decline in MCI.