Current Alzheimer Research - Online First

The primary objective of this study was to examine changes in brain network architecture across multiple frequency bands using spectral analysis of both weighted and binarized functional connectivity networks. This cross-sectional observational study, conducted as a secondary analysis of a publicly available EEG dataset, analyzed spectral coherence measurements from 25 patients with Alzheimer’s disease (AD) and 25 age- and sex-matched healthy controls (HC). Nevertheless, the modest sample size and cultural homogeneity of the dataset may limit the statistical power and generalizability of the results. A data-driven thresholding approach was employed to generate binary networks, allowing a robust comparison of connectivity disruptions associated with AD.

Brain network features derived from the graph Laplacian, including weighted Fiedler value, spectral range, and Middle Eigenvalue, were analyzed across seven frequency layers: delta, theta, alpha1, alpha2, beta1, beta2, and gamma. For binary networks, the Fiedler value was calculated after thresholding. Statistical group comparisons between AD and HC were performed using t-tests (p < 0.05), and each feature was assessed based on the number of frequency bands showing significant differences.

Among all features, the weighted Fiedler value was the most discriminative, showing significant reductions in AD patients within the alpha2 and beta1 bands. In binary networks, the Fiedler value remained significantly lower in AD within the alpha2 band, confirming topological degradation even without edge weight information. Other spectral features showed similar trends, but did not reach statistical significance in the binary networks.

The consistent decline in Fiedler value across both weighted and binary networks indicates a global reduction in connectivity characteristic of AD. These spectral markers offer a quantitative and interpretable framework for understanding the progressive disconnection syndrome in AD.

This study demonstrates significant alterations in Laplacian spectral features of brain networks between the AD and HC groups across specific frequency bands. These exploratory findings indicate that the spectral features, particularly the Fiedler value, consistently differentiate AD patients from healthy controls across frequency bands, suggesting its potential as a biomarker. However, larger and longitudinal studies are needed to confirm its diagnostic and prognostic utility. The combined use of weighted and binarized connectivity matrices enhances analytical sensitivity and facilitates the application of spectral graph theory for the early detection and monitoring of AD.

Type 2 Diabetes Mellitus (T2DM) patients are 50-60% more likely to develop Alzheimer’s Disease (AD). T2DM has many risk factors, including inflammation. Previous studies suggest that CRP was higher in diabetic patients, indicating that it may play a role in diabetogenesis and insulin resistance. Many diseases are prevalent in older age, including T2DM and AD. Moreover, multiple studies suggested a possible association between vitamin A levels, AD, and T2DM. However, the role of Vitamin A in Alzheimer's patients with T2DM has not yet been fully investigated. Therefore, this study aims to measure the association between dietary vitamin A deficiency and AD patients with T2DM in King Abdulaziz Medical City, Jeddah, Western Region, Saudi Arabia, to help expand the preexisting knowledge of the diagnostic risk factors of both the diseases and to determine the significance of vitamin A as a nutritional factor in their management and prevention.

This case-control study investigates the prevalence of vitamin A deficiency (VAD) among Alzheimer's disease (AD) patients with and without type 2 diabetes mellitus (T2DM). Participants included 103 AD patients aged 40 and older from the National Guard Hospital in Saudi Arabia, recruited between 2016 and 2022. Data collection occurred in two phases: first, through a review of medical records to gather demographic and health history information, including retrospective blood tests for systemic C-reactive protein (CRP) levels and comorbidities; second, using the HKI Food Frequency Questionnaire (FFQ) to assess dietary intake of vitamin A-rich foods over the past week, with caregiver interviews facilitating this process. Each subject was also prospectively interviewed to assess the presence of VAD events. The study aims to elucidate the relationship between dietary habits and VAD prevalence in AD patients, contributing to the understanding of nutritional impacts on cognitive health in this population.

This study examined demographic and clinical characteristics of the Alzheimer’s group, with 70.1% having both Alzheimer's with T2DM and 29.9% having Alzheimer's alone. Significant differences in age were found (p-value = 0.03), but gender distribution was similar (p-value = 0.45). Most caregivers were sons, and 81.43% of patients received oral feeding. Comorbidities included hypertension (94.90%) and dyslipidemia (63.4%), with significant differences (p-value < 0.001). Correlation analyses showed weak negative correlations between CRP and vitamin A concentrations in both groups (Alzheimer with T2DM: p-value = 0.713, rho = -0.064; AD only: p-value = 0.223, rho = -0.121). Age and vitamin A levels also exhibited weak correlations: Alzheimer’s with 2DM (p-value = 0.727, rho = 0.053) and Alzheimer’s only (p-value = 0.223, rho = -0.253), neither of them was statistically significant. Symptoms of vitamin A deficiency were noted in Alzheimer's patients with T2DM, with no significant differences between groups. Dietary intake was lower for vitamin B complex, vitamin D, and multivitamins in AD patients with T2DM.

The findings highlight the need for further investigation into the factors influencing vitamin A metabolism in these populations. Additionally, the prevalence of vitamin A deficiency symptoms and low dietary intake of essential nutrients among patients with Alzheimer's with T2DM suggests critical areas for nutritional intervention. Addressing these deficits may improve patient outcomes and enhance overall care strategies for individuals living with Alzheimer's disease.

Vascular Cognitive Impairment (VCI) is a common type of dementia that affects the quality of life and lacks effective treatments. The Dengzhan Shengmian capsule (DZSM), a traditional Chinese medicine, is clinically used to alleviate VCI symptoms, but its therapeutic mechanisms are not fully understood. This study aimed to evaluate the neuroprotective effects of DZSM in VCI patients by investigating its impact on cognitive function and brain structure, thereby providing neuroimaging evidence for its clinical application.

A randomized, double-masked, 6-month trial was conducted with 100 VCI patients, assigned to either the experimental group receiving DZSM (n = 50) or the placebo group (n = 50). The efficacy of DZSM in VCI patients was assessed through cognitive behavioral assessments and neuroimaging data collected at baseline and after 6 months. A comparison was made across groups to determine cognitive and neural changes associated with the intervention.

Participants receiving DZSM exhibited significant improvements across multiple cognitive domains compared to the placebo, including global cognition (MMSE, p = 0.019; ADAS-Cog, p < 0.001), episodic memory (AVLT-N1N5, p < 0.001), visuospatial ability (CDT, p = 0.034), and working memory (DST, p = 0.015). For brain structure, the gray matter volume in the right postcentral and precentral gyrus, bilateral cuneus, left supplementary motor area, superior occipital gyrus, right hippocampus, right thalamus, bilateral lingual gyrus, left precuneus, right inferior frontal gyrus (triangular part), left inferior parietal gyrus, left superior medial frontal gyrus, right superior temporal gyrus, left middle temporal gyrus, and right parahippocampal gyrus increased in the DZSM group (FDR-corrected, p<0.05), with no significant changes in white matter microstructure. Moreover, gray matter volume increases positively correlated with improvements in global cognition and visuospatial function.

DZSM capsules significantly improved multiple cognitive domains in VCI patients, particularly memory, visuospatial, and executive functions. The observed increases in gray matter volume suggest that DZSM may exert neuroprotective effects through structural brain remodeling, which is closely associated with cognitive enhancement.

This study identifies brain structural abnormalities in VCI patients that correlate with cognitive deficits. DZSM capsule treatment significantly improved cognitive function. While the underlying mechanisms remain to be fully elucidated, these effects may be related to structural changes in the brain.

The clinical trial registration number is ChiCTR-IPR-16009289.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that currently affects over 55 million individuals worldwide. Conventional diagnostic approaches often rely on subjective clinical assessments and isolated biomarkers, limiting their accuracy and early-stage effectiveness. With the rising global burden of AD, there is an urgent need for objective, automated tools that enhance diagnostic precision using neuroimaging data.

This study proposes a novel diagnostic framework combining a fine-tuned VGG19 deep convolutional neural network with an eXtreme Gradient Boosting (XGBoost) classifier. The model was trained and validated on the OASIS MRI dataset (Dataset 2), which was manually balanced to ensure equitable class representation across the four AD stages. The VGG19 model was pre-trained on ImageNet and fine-tuned by unfreezing its last ten layers. Data augmentation strategies, including random rotation and zoom, were applied to improve generalization. Extracted features were classified using XGBoost, incorporating class weighting, early stopping, and adaptive learning. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC.

The proposed VGG19-XGBoost model achieved a test accuracy of 99.6%, with an average precision of 1.00, a recall of 0.99, and an F1-score of 0.99 on the balanced OASIS dataset. ROC curves indicated high separability across AD stages, confirming strong discriminatory power and robustness in classification.

The integration of deep feature extraction with ensemble learning demonstrated substantial improvement over conventional single-model approaches. The hybrid model effectively mitigated issues of class imbalance and overfitting, offering stable performance across all dementia stages. These findings suggest the method’s practical viability for clinical decision support in early AD diagnosis.

This study presents a high-performing, automated diagnostic tool for Alzheimer’s disease based on neuroimaging. The VGG19-XGBoost hybrid architecture demonstrates exceptional accuracy and robustness, underscoring its potential for real-world applications. Future work will focus on integrating multimodal data and validating the model on larger and more diverse populations to enhance clinical utility and generalizability.

Arsenic, a metalloid, is well associated as a risk factor for the development and progression of neurodegenerative diseases, including Alzheimer’s Disease (AD), which is characterized by impairment in cognition. However, specific effects of arsenic on Acetylcholinesterase (AChE) activity and inflammatory markers in different brain regions, as well as its impact on behaviour, are not yet fully understood.

Arsenic was administered (20 mg/kg by gavage for 4 weeks) to male and female mice, and its effects on behaviour were assessed by using the object recognition memory test and light-dark box test. AChE activity and neuronal Nitric Oxide (nNOS) were assessed by histoenzymology, and immunohistochemistry was employed for assessment of Glial Fibrillary Acidic Protein (GFAP).

Both the behavioural tests showed significant impairment of learning and memory functions and development of psychiatric abnormalities in arsenic-fed mice. The histoenzymology and immunohistochemistry analysis of the cortex and hippocampus region of these arsenic-fed mice revealed the increment of AChE activity and inflammatory markers, viz. GFAP and nNOS.

The observed increment in AChE activity in the cortex and hippocampus of arsenic-fed mice may contribute to the impairment of learning and memory functions, as well as to the development of psychiatric abnormalities. Furthermore, the enhancement of inflammatory processes in these brain regions may be either a consequence or a contributing factor to the elevated AChE activity, thus establishing a self-fuelling cycle of neuroinflammation and increased AChE activity.

Given the gender bias in neurodegenerative diseases, our findings indicate that arsenic exposure does not lead to significant differences in neuropathological and neurobehavioural outcomes between male and female mice. Moreover, current outcomes underscore the potential of arsenic to act as a neurotoxic agent in AD development.

The complement receptor 1 (CR1) gene is identified as the one closely associated with Alzheimer's disease (AD). However, there has been no exploration of the imaging alterations associated with the CR1 gene in AD patients of the Han population. The purpose of this study is to investigate the association between the rs6656401 mutation and neuroimaging variations in Han AD patients.

We collected nuclear magnetic resonance images from 101 patients with AD and 98 healthy controls (HC). The subjects in this study, based on the different genotypes of rs6656401, were divided into three groups, with the number of AA, AG, and GG genotypes in the AD group being 1, 17, and 83, and 1, 8, and 89 in the HC group. Data were analyzed using the dominant model. Structural differences in the brain tissue between genotypes at the rs6656401 polymorphic locus were compared using voxel-based morphological analysis, cortical thickness, and graph-theoretic analysis to construct structural networks.

Seven regions (namely, right precuneus, right caudal middle frontal cortical, right rostral middle frontal, right superior frontal, right bankssts, right superior parietal, and right paracentral) were significantly different across CR1 rs6656401 genotypes. The voxel-based morphometry analysis revealed that voxel cluster sizes in the left cerebellum, left superior temporal gyrus, right superior frontal gyrus orbital, right precuneus, and right superior parietal were significantly different in the AA, AG, and GG groups. The degree centrality (Dc) of the left inferior frontal gyrus was significantly greater in the GG group than in the AG group after false discovery rate correction in the structural network analysis.

This study demonstrates that the rs6656401 AA genotype primarily induces structural alterations in the frontal, temporal, and parietal lobes of AD patients, with significant changes in the right middle frontal gyrus, precuneus, and superior parietal gyrus, along with Dc index alterations in the left inferior frontal gyrus affecting brain network function. Our findings confirm the association between the rs6656401 polymorphism and AD-related brain structural changes, providing the first evidence of these regional alterations in Han Chinese AD cohorts. Future studies will elucidate the locus's pathological mechanism to inform early diagnosis and targeted therapies.

Our study first indicated that CR1 rs6656401 genotypes significantly influenced the morphological and structural covariate networks in Han AD patients.

This study aimed to explore the value of diffusion tensor imaging (DTI)-based radiomics in the early diagnosis of Alzheimer's disease (AD) and predicting the progression of mild cognitive impairment (MCI) to AD.

A cohort of 186 patients with MCI was obtained from the publicly accessible Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and 49 of these individuals developed AD over a 5-year observation period. The subjects were divided into a training set and a test set in a ratio of 7 to 3. Radiomic features were extracted from the corpus callosum within the DTI post-processed images. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm was employed to develop radiomic signatures. The performance of the radiomic signature was assessed using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).

In the training set, 35 patients were converted, and in the test set, 14 patients were converted. Among all the patients, notable differences were observed in age, CDR-SB, ADAS, MMSE, FAQ, and MOCA between the stable group and the transformed group (p < 0.05). In the test set, the AUCs of the radiomics signatures constructed based on fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were 0.824, 0.852, 0.833, and 0.862, respectively. The AUC of the clinical model was 0.868, and that of the combined model was 0.936. DCA demonstrated that the combined model had the best performance.

The study highlights the corpus callosum as a critical region for detecting early AD-related microstructural changes. Radiomic features, particularly those derived from RD, outperformed traditional DTI parameters in predicting MCI progression. Combining radiomics with clinical data improved prediction accuracy, addressing limitations of single-biomarker approaches. However, the study’s retrospective design, limited sample size, and short follow-up period may affect generalizability.

The combined radiomics and clinical model, utilizing DTI data, can relatively accurately forecast which patients with MCI are likely to progress to AD. This approach offers potential for early AD prevention in MCI patients.