Understanding Of Alzheimer's Disease Research Articles

Synaptic and synchronic impairments in subcortical brain regions associated with early non-cognitive dysfunction in Alzheimer's disease.

For many decades, Alzheimer's disease research has primarily focused on impairments within cortical and hippocampal regions, which are thought to be related to cognitive dysfunctions such as memory and language deficits. The exact cause of Alzheimer's disease is still under debate, making it challenging to establish an effective therapy or early diagnosis. It is widely accepted that the accumulation of amyloid-beta peptide in the brain parenchyma leads to synaptic dysfunction, a critical step in Alzheimer's disease development. The traditional amyloid cascade model is initiated by accumulating extracellular amyloid-beta in brain areas essential for memory and language. However, while it is possible to reduce the presence of amyloid-beta plaques in the brain with newer immunotherapies, cognitive symptoms do not necessarily improve. Interestingly, recent studies support the notion that early alterations in subcortical brain regions also contribute to brain damage and precognitive decline in Alzheimer's disease. A body of recent evidence suggests that early Alzheimer's disease is associated with alterations (e.g., motivation, anxiety, and motor impairment) in subcortical areas, such as the striatum and amygdala, in both human and animal models. Also, recent data indicate that intracellular amyloid-beta appears early in subcortical regions such as the nucleus accumbens, locus coeruleus, and raphe nucleus, even without extracellular amyloid plaques. The reported effects are mainly excitatory, increasing glutamatergic transmission and neuronal excitability. In agreement, data in Alzheimer's disease patients and animal models show an increase in neuronal synchronization that leads to electroencephalogram disturbances and epilepsy. The data indicate that early subcortical brain dysfunctions might be associated with non-cognitive symptoms such as anxiety, irritability, and motivation deficits, which precede memory loss and language alterations. Overall, the evidence reviewed suggests that subcortical brain regions could explain early dysfunctions and perhaps be targets for therapies to slow disease progression. Future research should focus on these non-traditional brain regions to reveal early pathological alterations and underlying mechanisms to advance our understanding of Alzheimer's disease beyond the traditionally studied hippocampal and cortical circuits.

Oligodendrocytes in Alzheimer's disease pathophysiology.

Our understanding of Alzheimer's disease (AD) has transformed from a purely neuronal perspective to one that acknowledges the involvement of glial cells. Despite remarkable progress in unraveling the biology of microglia, astrocytes and vascular elements, the exploration of oligodendrocytes in AD is still in its early stages. Contrary to the traditional notion of oligodendrocytes as passive bystanders in AD pathology, emerging evidence indicates their active participation in and reaction to amyloid and tau pathology. Oligodendrocytes undergo a functional transition to a disease-associated state, engaging in immune modulation, stress responses and cellular survival. Far from being inert players, they appear to serve a dual role in AD pathogenesis, potentially offering defense mechanisms against pathology while also contributing to disease progression. This Review explores recent advancements in understanding the roles of oligodendrocytes and their myelin sheaths in the context of AD, shedding light on their complex interactions within the disease pathology.

Influence of birthplace and age at migration on cognitive aging among Hispanic/Latino populations in the U.S.: Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA).

While Hispanic/Latino populations in the U.S. are remarkably diverse in terms of birthplace and age at migration, we poorly understand how these factors are associated with cognitive aging. Our research seeks to operationalize a life course perspective of migration and health and contribute new understanding of Alzheimer's disease / Alzheimer's disease related dementias among U.S.-based Hispanic/Latino older adults. Harnessing the Hispanic Community Health Study/Study of Latinos (n=16,415) and the Study of Latinos-Investigation of Neurocognitive Aging (n=6,377) data, we compare baseline cognition and 7-year cognitive change among U.S./mainland-born Hispanic/Latino adults relative to foreign/island-born immigrants by age of migration (four groups: born in mainland U.S., immigrated <16 years, 16-34 years, >34 years). Global cognition was calculated as a composite measure, and domain specific measures were considered in secondary analyses. We employed linear regressions, ANOVA contrasts and Blinder-Oaxaca decomposition techniques. All Hispanic/Latino immigrant adults, regardless of age at migration, have a cognitive health disadvantage (at each visit and over time) relative to U.S./mainland-born Hispanic/Latino individuals. Differences did not endure the inclusion of covariates and were explained predominantly by first socio-economic and then acculturative factors, and far less by health and health behaviors. Acculturative factors are particularly important for individuals who migrated after childhood. Socio-economic and acculturation factors have outsized roles in explaining gaps in cognitive aging among U.S.-born and migrant Hispanic/Latino adults. It is then vital to examine whether disrupting socio-economic and acculturation inequalities closes such gaps in cognitive aging.

Fourier Transform Infrared Spectroscopy Analysis as a Tool to Address Aβ Impact on Extracellular Vesicles.

Alzheimer's disease is a challenge in modern healthcare due to its complex etiology and increasing prevalence. Despite advances, further understanding of Alzheimer's disease pathophysiology is needed, particularly the role of Aβ neurotoxic peptide. Fourier transform infrared spectroscopy (FTIR) has shown potential as a screening tool for several pathologies, including Alzheimer's disease. Nonetheless, limited research has explored Aβ direct effects on neurons and extracellular vesicles metabolic profiles. Hence, this study aims to investigate Aβ impact on the spectroscopic profiles of neuronal-like cells (N2a) and N2a-derived extracellular vesicles, employing FTIR spectroscopy and focusing on the 1280-900 cm-1 region. A comprehensive analysis of spectral data was carried out, including multivariate partial least squares (PLS) analysis and peak intensities analysis. PLS analysis revealed moderate to strong correlations within this spectral region for both N2a and N2a-derived extracellular vesicles. The peak intensity analysis revealed additional peaks with significant differences in EVs' spectra relative to N2a, following Aβ treatment. Specifically, Aβ seems to cause alterations in protein phosphorylation and in the nucleic acids content of extracellular vesicles. These findings support that Aβ's role in Alzheimer's disease pathology may be mediated by extracellular vesicles and highlight FTIR's potential for advancing Alzheimer's disease research and clinical applications.

Building a Diagnostic Standard for Alzheimer’s Disease Based on Decision Trees and Analyzing Key Factors with Random Forests

Research Background and Significance: Alzheimer's disease (AD) is a widespread neurodegenerative disorder that poses a significant threat to the health of millions of older adults worldwide. Despite the availability of various methods to assess the severity of dementia, the need for a high-precision diagnostic model remains crucial to enhancing patient outcomes. Accurate diagnosis is essential not only for the well-being of individuals but also for the effective management and treatment of the disease. This study aims to address this critical need by developing a more precise diagnostic framework for AD, utilizing advanced machine learning techniques in combination with comprehensive clinical data. Study Contributions: In this research, a decision tree model was constructed based on the principle of information gain, using a meticulously pre-processed sample of 2,149 patients from a public dataset. The model achieved a diagnostic accuracy of 93.47%, markedly outperforming traditional diagnostic methods. Additionally, a random forest model was employed to identify key risk factors influencing AD, such as age and lifestyle habits. These findings not only equip clinicians with more accurate diagnostic tools but also provide a robust scientific foundation for developing AD prevention and treatment strategies. The study also acknowledges its limitations and suggests directions for future research to further improve the diagnosis and understanding of Alzheimer's disease.

The Knight Alzheimer Research Imaging dataset: a comprehensive multimodal resource for exploring aging, preclinical, and symptomatic Alzheimer disease pathology

AbstractBackgroundThe Knight Alzheimer Research Imaging (KARI) dataset, a compilation of data from projects conducted at Washington University in St. Louis, represents a comprehensive effort to advance our understanding of Alzheimer disease (AD) through multimodal data collection. The overarching goal is to characterize normal aging and disease progression to contribute insights into the biological changes preceding AD symptom onset.MethodsThe dataset comprises cross‐sectional and longitudinal measures of magnetic resonance imaging (MRI), including T1 and T2‐weighted structural, diffusion, and resting‐state acquisitions, from over 1,600 participants aged 42 to 103 years. Additionally, a subset of participants underwent amyloid (1,100+) and tau (500+) tracer positron emission tomography (PET) acquisitions, along with cognitive, genetic, and cerebrospinal fluid (CSF) biofluid measures.ResultsIn addition to raw MRI and PET acquisitions, this dataset also includes processed output that has undergone rigorous quality assessment, anatomical segmentation, and quantification procedures using FreeSurfer, as well as post‐processing quantification of amyloid and tau burden. All data are publicly available in an online repository and can be accessed upon request.ConclusionsThe KARI dataset offers a valuable resource for investigating biomarkers, genetic factors, and imaging data in normal aging, preclinical, and symptomatic Alzheimer’s disease. Expanded use could contribute to the development of early diagnostic tools, treatment strategies, and a deeper understanding of the complexities associated with neurodegenerative diseases.

Large‐scale proteomic and genomic analysis identify plasma proteins influencing human brain structure and Alzheimer’s disease risk

AbstractBackgroundRecent advances in proteomics technology enable us to comprehensively examine the impact of the proteome on Alzheimer’s disease (AD) and intermediate phenotypes, such as brain imaging measures. Here, by leveraging large‐scale proteomic, brain imaging, and AD genome‐wide association studies (GWAS), we performed Mendelian randomization (MR) and colocalization analysis to investigate the impact of plasma protein levels on brain imaging measures and the link between plasma protein levels, brain imaging, and AD.MethodWe leverage the protein quantitative trait loci (pQTL) datasets from the UK Biobank Pharma Proteomics Project (UKB‐PPP; 2,625 proteins by Olink assay) and deCODE genetics (Ferkingstad et al. 2021; 4,472 proteins by SomaLogic assay). UKB‐PPP and the deCODE study represent the largest plasma proteomics GWAS to date based on two different proteomics platforms, where the total sample sizes are over 30,000 for each. For the brain imaging and AD genetic datasets, we used the brain imaging GWAS from UKB (453 T1 MRI and DTI imaging; Smith et al. 2021), and AD GWAS from Bellenguez et al. 2022.ResultAfter extensive data processing and quality control, we performed two‐sample MR and identified significant associations in 170 protein‐DTI imaging pairs and 38 protein‐MRI pairs for 60 unique proteins from UKB‐PPP and 41 unique proteins from deCODE. Colocalization analysis further prioritized proteins related to AD implicated in previous studies. We further extended the association between plasma protein and brain imaging to AD by performing MR and colocalization analyses using pQTL and AD GWAS. As an example, we showed that plasma progranulin (P28799) level showed a significant effect on AD risk (P = 6.79×10−12), while supported by associations between plasma progranulin level and caudate volumes (right and left T1 DTI, P = 8.41×10−6).ConclusionIn summary, we presented the largest to date study for the impact of plasma protein levels on brain imaging measures and a novel approach to identify biomarkers for AD. Overall, the integration of large‐scale MRI, proteomics, AD phenotype, and genome‐wide data holds great promise for enhancing our understanding of AD, and ultimately facilitates biomarker discovery and therapeutic development.

Integration of Single-Cell and Spatial Transcriptomic Data Reveals Spatial Architecture and Potential Biomarkers in Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by the gradual loss of neurons and the accumulation of amyloid plaques and neurofibrillary tangles. Despite advancements in the understanding of AD's pathophysiology, the cellular organization and interactions in the prefrontal cortex (PFC) remain elusive. Eight single-cell RNA sequencing (scRNA-seq) datasets from both normal controls and individuals with AD were harmonized. Stringent preprocessing protocols were implemented to uphold dataset integrity. Unsupervised clustering and annotation revealed 22 distinct cell clusters corresponding to 19 unique cell types. The spatial architecture of the PFC region was constructed using the CARD tool. Further analyses encompassed trajectory examination of Oligodendrocyte subtypes, evaluation of regulon activity scores, and spot clustering within white matter regions (WM). Differential expression analysis and functional enrichment assays unveiled molecular signatures linked to AD progression and were validated using microarray data sourced from neurodegenerative disorder patients. Our investigation employs scRNA-seq and spatial transcriptomics to uncover the cellular atlas and spatial architecture of the human PFC in AD. Moreover, our results indicate that Oligodendrocytes are more prevalent in AD patients, showcasing diverse subtypes and spatial organization within WM regions. Each subtype appears to be associated with distinct biological processes and transcriptional regulators, shedding light on their involvement in AD pathology. Notably, the Oligodendrocyte_C6 subtype is linked to neurological damage in AD patients, characterized by heightened expression of genes involved in cell-cell connections, cell membrane stability, and myelination. Additionally, 12 target genes regulated by NFIA were identified, which are upregulated in AD patients and associated with disease progression. Elevated PLXDC2 expression in peripheral blood was also identified, suggesting its potential as a non-invasive biomarker for early AD detection. Our study provides novel insights into the role of Oligodendrocytes in AD and highlights the potential of PLXDC2 as a blood biomarker for non-invasive diagnosis and monitoring of AD patients.

Two for tau: Automated model discovery reveals two-stage tau aggregation dynamics in Alzheimer’s disease

Alzheimer’s disease is a neurodegenerative disorder characterized by the presence of amyloid-β plaques and the accumulation of misfolded tau proteins and neurofibrillary tangles in the brain. A thorough understanding of the local accumulation of tau is critical to develop effective therapeutic strategies. Tau pathology has traditionally been described using reaction–diffusion models, which succeed in capturing the global spread, but fail to accurately describe the local aggregation dynamics. Current mathematical models enforce a single-peak behavior in tau aggregation, which does not align well with clinical observations. Here we identify a more accurate description of tau aggregation that reflects the complex patterns observed in patients. We propose an innovative approach that uses constitutive neural networks to autonomously discover bell-shaped aggregation functions with multiple peaks from clinical positron emission tomography (PET) data of misfolded tau protein. Our method reveals previously overlooked two-stage aggregation dynamics by uncovering a two-term ordinary differential equation that links the local accumulation rate to the tau concentration. When trained on data from amyloid-β positive and negative subjects, the neural network clearly distinguishes between both groups and uncovers a more subtle relationship between amyloid-β and tau than previously postulated. In line with the amyloid–tau dual pathway hypothesis, our results show that the presence of toxic amyloid-β influences the accumulation of tau, particularly in the earlier disease stages. We expect that our approach to autonomously discover the accumulation dynamics of pathological proteins will improve simulations of tau dynamics in Alzheimer’s disease and provide new insights into disease progression.Significance StatementIn Alzheimer’s disease, understanding the local dynamics of tau protein aggregation is crucial for developing effective treatments. Traditional models for tau protein dynamics use reaction–diffusion models that fail to accurately capture these local patterns. Our study introduces a novel approach that leverages constitutive neural networks to autonomously discover the complex, multi-peak aggregation dynamics from clinical PET data. This method reveals a previously overlooked two-stage tau accumulation process and a nuanced relationship between amyloid-β and tau. By distinguishing between amyloid-β positive and negative subjects, our model supports the amyloid–tau dual pathway hypothesis and offers novel insights into tau protein aggregation that have the potent to advance our understanding of Alzheimer’s disease progression.

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers. Differentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer's disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms. The study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes-choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)-emerged as potential biomarkers for Alzheimer's disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease. The discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer's disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD's complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies.

Elucidating the neuropathological and molecular heterogeneity of amyloid-beta and tau in Alzheimer's disease through machine learning and transcriptomic integration.

Discerning functional brain network variations related to neuropathological aggregates in Alzheimer's disease (AD), including amyloid-beta (Abeta) and phosphorylated tau (p-tau), is crucial for understanding their link to cognitive decline and underlying molecular mechanisms. However, these variations are often confounded by normal aging-related changes, complicating interpretation. To address this challenge, we first defined Alzheimer's continuum cases (Abeta positive (A+), n = 129) and normal elderly (Abeta negative (A-), n = 160) using cerebral spinal fluid amyloid levels, and then applied a novel deep learning approach to resting-state connectivity using functional magnetic resonance imaging (fMRI) of the 289 subjects to disentangle A+-specific dimensions in brain network alterations from those shared with A- individuals. The identified A+-specific dimensions were further refined to predict individual Abeta and p-tau levels separately. We observed that resulting brain signatures, defined from A+-specific dimensions for predicting these two CSF biomarkers, were both attributed to the right superior temporal and anterior cingulate cortices and associated with attention and memory domains. When linking the brain signatures to gene expression data from a public transcriptomic atlas, we found that the brain signatures were associated with molecular pathways involving synaptic dysfunction and disruptions in pathways containing activity of excitatory neurons, astrocytes, and microglia. For A--shared dimensions, the Aβ-linked brain signature involved the left fusiform and right middle cingulate cortices, correlating with the language cognitive measurement and language-related molecular pathways. The p-tau-linked signature predominantly involved the right insula and inferior temporal cortices, correlating with the aging-related molecular pathways. Collectively, our findings provided new insights in understanding of Alzheimer's continuum pathological biomarkers.

Isotope Encoded chemical Imaging Identifies Amyloid Plaque Age Dependent Structural Maturation, Synaptic Loss, and Increased Toxicity.

It is of critical importance to our understanding of Alzheimer's disease (AD) pathology to determine how key pathological factors are interconnected and implicated in nerve cell death, clinical symptoms, and disease progression. The formation of extracellular beta-amyloid (Aβ) plaques is the major pathological hallmark of AD and Aβ has been suggested to be a critical inducer of AD, driving disease pathogenesis. Exactly how Aβ plaque formation begins and how ongoing plaque deposition proceeds and initiates subsequent neurotoxic mechanisms is not well understood. The primary aim of our research is to elucidate the biochemical processes underlying early Aβ plaque formation in brain tissue. We recently introduced a chemical imaging paradigm based on mass spectrometry imaging (MSI) and metabolic isotope labelling to follow stable isotope labelling kinetics (iSILK) in vivo to track the in vivo build-up and deposition of Aβ. Herein, knock-in Aβ mouse models (App NL-F ) that develop Aβ pathology gradually are metabolically labeled with stable isotopes. This chemical imaging approach timestamps amyloid plaques during the period of initial deposition allowing the fate of aggregating Aβ species from before and during the earliest events of plaque pathology through plaque maturation to be tracked. To identify the molecular and cellular response to plaque maturation, we integrated iSILK with single plaque transcriptomics performed on adjacent tissue sections. This enabled changes in gene expression to be tracked as a function of plaque age (as encoded in the Aβ peptide isotopologue pattern) distinct from changes due to the chronological age or pathological severity. This approach identified that plaque age correlates negatively with gene expression patterns associated with synaptic function as early as in 10-month-old animals but persists into 18 months. Finally, we integrated hyperspectral confocal microscopy into our multiomic approach to image amyloid structural isomers, revealing a positive correlation between plaque age and amyloid structural maturity. This analysis identified three categories of plaques, each with a distinct impact on the surrounding microenvironment. Here, we identified that older, more compact plaques were associated with the most significant synapse loss and toxicity. These data show how isotope-encoded MS imaging can be used to delineate Aβ toxicity dynamics in vivo. Moreover, we show for the first time a functional integration of dynamic MSI, structural plaque imaging and whole genome-wide spatial transcriptomics at the single plaque level. This multiomic approach offers an unprecedented combination of temporal and spatial resolution enabling a description of the earliest events of precipitating amyloid pathology and how Aβ modulates synaptotoxic mechanisms.

The Dual Role of Amyloid Beta-Peptide in Oxidative Stress and Inflammation: Unveiling Their Connections in Alzheimer's Disease Etiopathology.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and it is currently the seventh leading cause of death worldwide. It is characterized by the extracellular aggregation of the amyloid β-peptide (Aβ) into oligomers and fibrils that cause synaptotoxicity and neuronal death. Aβ exhibits a dual role in promoting oxidative stress and inflammation. This review aims to unravel the intricate connection between these processes and their contribution to AD progression. The review delves into oxidative stress in AD, focusing on the involvement of metals, mitochondrial dysfunction, and biomolecule oxidation. The distinct yet overlapping concept of nitro-oxidative stress is also discussed, detailing the roles of nitric oxide, mitochondrial perturbations, and their cumulative impact on Aβ production and neurotoxicity. Inflammation is examined through astroglia and microglia function, elucidating their response to Aβ and their contribution to oxidative stress within the AD brain. The blood-brain barrier and oligodendrocytes are also considered in the context of AD pathophysiology. We also review current diagnostic methodologies and emerging therapeutic strategies aimed at mitigating oxidative stress and inflammation, thereby offering potential treatments for halting or slowing AD progression. This comprehensive synthesis underscores the pivotal role of Aβ in bridging oxidative stress and inflammation, advancing our understanding of AD and informing future research and treatment paradigms.

Potential Mechanisms of Tunneling Nanotube Formation and Their Role in Pathology Spread in Alzheimer's Disease and Other Proteinopathies.

Alzheimer's disease (AD) is the most common type of dementia worldwide. The etiopathogenesis of this disease remains unknown. Currently, several hypotheses attempt to explain its cause, with the most well-studied being the cholinergic, beta-amyloid (Aβ), and Tau hypotheses. Lately, there has been increasing interest in the role of immunological factors and other proteins such as alpha-synuclein (α-syn) and transactive response DNA-binding protein of 43 kDa (TDP-43). Recent studies emphasize the role of tunneling nanotubes (TNTs) in the spread of pathological proteins within the brains of AD patients. TNTs are small membrane protrusions composed of F-actin that connect non-adjacent cells. Conditions such as pathogen infections, oxidative stress, inflammation, and misfolded protein accumulation lead to the formation of TNTs. These structures have been shown to transport pathological proteins such as Aβ, Tau, α-syn, and TDP-43 between central nervous system (CNS) cells, as confirmed by in vitro studies. Besides their role in spreading pathology, TNTs may also have protective functions. Neurons burdened with α-syn can transfer protein aggregates to glial cells and receive healthy mitochondria, thereby reducing cellular stress associated with α-syn accumulation. Current AD treatments focus on alleviating symptoms, and clinical trials with Aβ-lowering drugs have proven ineffective. Therefore, intensifying research on TNTs could bring scientists closer to a better understanding of AD and the development of effective therapies.

A Comparison of an Australian Observational Longitudinal Alzheimer's Disease Cohort to Community-Based Australian Data.

Observational Alzheimer's disease (AD) cohorts including the Australian, Biomarkers, Imaging and Lifestyle (AIBL) Study have enhanced our understanding of AD. The generalizability of findings from AIBL to the general population has yet to be studied. We aimed to compare characteristics of people with AD dementia in AIBL to 1) the general population of older Australians using pharmacological treatment for AD dementia, and to 2) the general population of older Australians who self-reported a diagnosis of dementia. Descriptive study comparing people aged 65 years of over (1) in AIBL that had a diagnosis of AD dementia, (2) dispensed with pharmacological treatment for AD in Australia in 2021 linked to the Australian census in 2021 (refer to as PBS/census), (3) self-reported a diagnosis of dementia in the 2021 Australian census (refer to as dementia/census). Baseline characteristics included age, sex, highest education attainment, primary language, and medical co-morbidities. Participants in AIBL were younger, had more years of education, and had a lower culturally and linguistically diverse (CALD) population compared to the PBS/census cohort and dementia/census cohort (mean age±standard deviation - AIBL 79±7 years, PBS/census 81±7, p < 0.001, dementia/census 83±8, p < 0.001; greater than 12 years of education AIBL 40%, PBS/census 35%, p = 0.020, dementia/census 29%, p < 0.001; CALD - AIBL 3%, PBS/census 20%, p < 0.001, dementia/census 22%, p < 0.001). Our findings suggest that care should be taken regarding the generalizability of AIBL in CALD populations and the interpretation of results on the natural history of AD.

Alzheimer's pathology disrupts flexible place cell coding and hippocampal-prefrontal neural dynamics during risky foraging decisions in mice.

This study investigates the neural activity patterns associated with impaired decision-making in Alzheimer's disease (AD) by examining the effects of amyloid pathology on prefrontal-hippocampal circuit dynamics in 5XFAD mice, a model system known for its pronounced early amyloid pathology. Using ecologically relevant "approach food-avoid predator" paradigms, we revealed that 5XFAD mice display impaired decision strategies in risky foraging tasks, characterized by rigid hippocampal place fields and diminished sharp-wave ripple (SWR) frequencies, accompanied by disrupted prefrontal-hippocampal connectivity. These neural deficits align with behavioral inflexibility in dynamic threat scenarios. Our findings indicate that disrupted SWR dynamics and corticolimbic connectivity contribute to decision-making deficits in AD, emphasizing the potential of targeting these specific neural mechanisms to ameliorate cognitive impairments. This research contributes significantly to our understanding of AD's impact on cognition, providing insights that could lead to more targeted therapeutic strategies.

Remember oligodendrocytes: Uncovering their overlooked role in Alzheimer's disease.

Our understanding of Alzheimer's disease (AD) has evolved from focusing solely on neurons to recognizing the role of glia. A recent study in PLOS Biology revealed that oligodendrocytes are an important source of Aβ that impairs neuronal function.

A Bioinformatics-Based Study on Methylation Alterations of the FBLN1 Gene in Hippocampal Tissue of Alzheimer's Disease Model DKO and DTG Mice.

Alzheimer's disease (AD) is characterized by progressive cognitive decline and late-stage neurobehavioral issues marked by amyloid-beta plaques and Tau protein tangles. This study aims to investigate Fibulin-1(FBLN1) gene expression in the hippocampal tissue of Presenilin-1/Presenilin-2 conditional double-knockout (DKO) and double-transgenic (DTG) mice, using single-cell sequencing and experimental methods to verify abnormal methylation status and correlation with AD. Genomic DNA from DKO and DTG mice was used for genotyping. Reduced Representation Bisulfite Sequencing (RRBS) identified 10 genes with abnormal methylation changes, with protein-protein interaction (PPI) analysis highlighting five core genes, including FBLN1. Single-cell sequencing, RT-PCR, and Western blotting (WB) were used to analyze FBLN1 mRNA and protein levels in the hippocampal tissues of early-stage and mid-stage AD DKO, DTG, and CBAC57 mice. RRBS identified 10 genes with abnormal methylation, with PPI highlighting five core genes. Single-cell sequencing showed significant FBLN1 expression in AD groups. RT-PCR and WB indicated elevated FBLN1 mRNA and protein levels in mid-stage AD DKO and DTG mice compared to CBAC57 mice, with no differences in early-stage AD DKO and CBAC57 mice. RRBS revealed hypomethylation of the FBLN1 gene in mid-stage AD DKO mice. Elevated FBLN1 expression in AD models suggests an age-dependent neurodegenerative mechanism independent of amyloid-beta deposition. This study enhances our understanding of AD's epigenetic mechanisms, which will aid targeted diagnosis, treatment, and prognosis.

The informatics of ADNI.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNIestablished a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNIset a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approachnot only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer's disease. Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.

Data sharing as the foundation of discovery: ADNI and breakthroughs in Alzheimer's disease.

The COVID pandemic has shown that when the research community comes together, we can conquer the most complex biomedical challenges. Collaboration and teamwork among federal agencies, private organizations, and researchers have been crucial in the development of vaccines and therapeutics against COVID. Possibly the first example of such cross-functional collaboration is the Alzheimer's Disease Neuroimaging Initiative (ADNI), the largest and longest continually monitored Alzheimer's study. ADNI was designed and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. This article shows how recent successes in the Alzheimer's field are directly a result of ADNI's open and transparent sharing of knowledge, expertise, and resources, which have allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time. ADNI's approach to open-source innovation also served as a model for addressing other complex diseases and led to numerous collaborative research initiatives. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was designed, structured, and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. The recent successes in the Alzheimer's field are directly a result of ADNI's efforts. Open and transparent sharing of knowledge, expertise, and resources allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time.