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.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by synaptic dysfunction and the accumulation of amyloid plaques. The molecular mechanisms linking gene dysregulation, pathogenic variants, and protein interaction networks to these core pathologies remain incompletely understood. This study aimed to integrate transcriptomic data with mutation and structural modeling to uncover disease mechanisms and identify therapeutic targets.

We performed differential gene expression analysis on the GSE138260 microarray dataset using GEO2R to identify DEGs in AD brain tissue. Missense mutations in DEGs were retrieved from the Alzheimer’s Disease Variant Portal (ADVP). Protein-protein interaction networks were constructed using the STRING database to identify connections with the amyloid precursor protein (APP). Molecular dynamics simulations were conducted to evaluate the structural consequences of the BDNF V66M mutation.

A total of 1,588 DEGs were identified, including upregulation of immune-related genes and downregulation of neuroplasticity-associated genes (e.g., BDNF, GRIN2B, GRM8). PPI analysis revealed a core network centered on APP, including BDNF as a direct interactor. The V66M variant in BDNF, confirmed to be downregulated in AD brains, showed increased rigidity and localized flexibility in structural models.

The integration of transcriptomics and protein modeling revealed a critical link between BDNF dysfunction and APP interaction in AD. The V66M mutation was found to structurally alter BDNF, potentially disrupting its neuroprotective roles. The findings suggested that impaired BDNF signaling, driven by transcriptional repression and structural mutation, contributes to amyloid pathology and synaptic failure.

This multi-omics investigation has identified BDNF as a converging point of gene dysregulation and pathogenic mutation within an APP-centric network. Structural alterations induced by the V66M mutation may exacerbate amyloid accumulation and neuronal dysfunction, supporting therapeutic strategies aimed at enhancing BDNF signaling in AD.

Primary progressive aphasia (PPA) is a clinical syndrome characterized by progressive language impairment. Three subtypes have been identified: semantic (svPPA), nonfluent (nfPPA), and logopenic (lvPPA). Although clinical criteria exist to classify these subtypes, the specific ways in which semantic cognition is impaired across these variants have not yet been fully elucidated. This cross-sectional study aimed to analyze the effects of cognitive demand and imaginability on semantic cognition in patients with PPA.

Fifteen patients with PPA (five per variant) and 20 healthy controls completed a semantic association task comprising 20 items. The task included two levels of cognitive demand (low and high) and two types of concepts (concrete and abstract). Participants selected the word with the strongest semantic link to a probe word, based on synonymy, categorical relations, or shared features. Accuracy and reaction times were recorded and analyzed using nonparametric statistics.

All PPA groups performed significantly worse than controls, showing fewer correct responses and longer reaction times. svPPA patients exhibited the greatest impairment across all conditions. nfPPA patients performed similarly to controls with concrete concepts but showed deficits with abstract words. lvPPA patients experienced greater difficulty under high cognitive demand, particularly with abstract words, indicating impaired semantic control.

These findings suggest that svPPA is characterized by global impairment of conceptual knowledge, whereas nfPPA and lvPPA exhibit more selective deficits depending on concept type and cognitive demand.

The research herein highlights the importance of considering cognitive demand and imaginability when assessing semantic cognition in PPA.

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.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-β (Aβ) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD.

The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD.

To dissect the proteomic perturbations induced by Aβ and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts.

Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks.

This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.