Cell Type Differentiation Using Network Clustering Algorithms.

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases.

Multi-modal Imaging-based Pseudotime Analysis of Alzheimer progression.

Alzheimer's disease (AD) is a neurodegenerative disorder that results in progressive cognitive decline but without any clinically validated cures so far. Understanding the progression of AD is critical for early detection and risk assessment for AD in aging individuals, thereby enabling initiation of timely intervention and improved chance of success in AD trials. Recent pseudotime approach turns cross-sectional data into "faux" longitudinal data to understand how a complex process evolves over time.

Astrocyte Reactivity Polygenic Risk Score May Predict Cognitive Decline in Alzheimer's Disease.

Alzheimer's disease (AD) is a polygenic disorder with a prolonged prodromal phase, complicating early diagnosis. Recent research indicates that increased astrocyte reactivity is associated with a higher risk of pathogenic tau accumulation, particularly in amyloid-positive individuals. However, few clinical tools are available to predict which individuals are likely to exhibit elevated astrocyte activation and, consequently, be susceptible to hyperphosphorylated tau-induced neurodegeneration.

Locus coeruleus tau is linked to successive cortical tau accumulation.

Introduction: We investigated the hypothesis that tau burden in the locus coeruleus (LC) correlates with tau accumulation in cortical regions according to the Braak stages and examined whether the relationships differed according to cortical amyloid beta (Aβ) deposition.

Methods: One hundred and seventy well-characterized participants from an ongoing cohort were included. High-resolution T1, tau positron emission tomography (PET), and amyloid PET were obtained.

Traumatic Brain Injury and Alzheimer's Disease Biomarkers: A Systematic Review of Findings from Amyloid and Tau Positron Emission Tomography.

Traumatic brain injury (TBI) has been discussed as a risk factor for Alzheimer's disease (AD) due to its association with AD risk and earlier cognitive symptom onset. However, the mechanisms behind this relationship are unclear. Some studies have suggested TBI may increase pathological protein deposition in an AD-like pattern; others have failed to find such associations.

Tau pathway-based gene analysis on PET identifies CLU and FYN in a Korean cohort.

Introduction: The influence of genetic variation on tau protein aggregation, a key factor in Alzheimer's disease (AD), remains not fully understood. We aimed to identify novel genes associated with brain tau deposition using pathway-based candidate gene association analysis in a Korean cohort.

Methods: We analyzed data for 146 older adults from the well-established Korean AD continuum cohort (Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease; KBASE).

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation.

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs.

Cortico-limbic volume abnormalities in late life depression are distinct from β amyloid and white matter pathologies.

This study was conducted to clarify patterns of cortico-limbic volume abnormalities in late life depression (LLD) relative to non-depressed (ND) adults matched for amyloid β (Aβ) deposition and to evaluate the relationship of volume abnormalities with cognitive performance. Participants included 116 LLD and 226 ND. Classification accuracy of LLD status was estimated using area under the receiver operator characteristic curve.

Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes.

Introduction: The genetic pathways that influence longitudinal heterogeneous changes in Alzheimer's disease (AD) may provide insight into disease mechanisms and potential therapeutic targets.

Methods: Longitudinal endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) representing amyloid, tau, neurodegeneration (A/T/N), and cognition were selected. Genome-wide association analysis was performed using a linear mixed model (LMM) approach, followed by gene and pathway enrichment with significant and functionally relevant SNPs.

LD-informed deep learning for Alzheimer's gene loci detection using WGS data.

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.

Referral Sources Across Racial and Ethnic Groups at Alzheimer's Disease Research Centers.

Background: Despite the need to increase engagement of underrepresented groups (URG) in Alzheimer's disease and related dementias (ADRD) studies, enrollment remains low.

Objective: Compare referral sources across racial and ethnic groups among participants enrolled in ADRC studies.

Methods: Data for this cross-sectional secondary analysis were extracted from the National Alzheimer's Coordinating Center Uniform Data Set.

Plasma miRNAs across the Alzheimer's disease continuum: Relationship to central biomarkers.

Introduction: MicroRNAs (miRNAs) play important roles in gene expression regulation and Alzheimer's disease (AD) pathogenesis.

Methods: We investigated the association between baseline plasma miRNAs and central AD biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 803): amyloid, tau, and neurodegeneration (A/T/N). Differentially expressed miRNAs and their targets were identified, followed by pathway enrichment analysis.

Anxiety in late-life depression is associated with poorer performance across multiple cognitive domains.

Objective: Anxiety is a common comorbid feature of late-life depression (LLD) and is associated with poorer global cognitive functioning independent of depression severity. However, little is known about whether comorbid anxiety is associated with a domain-specific pattern of cognitive dysfunction. We therefore examined group differences (LLD with and without comorbid anxiety) in cognitive functioning performance across multiple domains.