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Synthetic control methods for n-of-1 and parallel-group trials in Alzheimer's disease: A proof-of-concept study using the I-CONECT
INTRODUCTION: With the advent of Alzheimer's disease (AD)-modifying and symptomatic treatments of demonstrated efficacy, enrolling participants as concurrent placebo controls in trials can become increasingly difficult. Synthetic controls have been proposed as a viable alternative to concurrent control groups, but their feasibility and reliability remain untested in AD studies.
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Regularization by neural style transfer for MRI field-transfer reconstruction with limited data
Recent advances in MRI reconstruction have demonstrated remarkable success through deep learning-based models. However, most existing methods rely heavily on large-scale, task-specific datasets, making reconstruction in data-limited settings a critical yet underexplored challenge. While regularization by denoising (RED) leverages denoisers as priors for reconstruction, we propose Regularization by Neural Style Transfer (RNST), a novel framework that integrates a neural style transfer (NST)...
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Mitochondrial DNA affects tau phosphorylation in aging and Alzheimer's disease
INTRODUCTION: Impaired mitochondrial function is seen in Alzheimer's disease (AD), but its role is unclear. Mitochondrial DNA (mtDNA) supports bioenergetic metabolism, but it is uncertain how it might influence AD neuropathology.
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MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers
Nontargeted peak detection in LC-MS-based metabolomics must become robust and benchmarked. We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. Peaks are then grouped for adduct and in-source fragment detection, and compounds are annotated by...
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Genetics of <em>PLCG2</em> expression and splicing relative to Alzheimer's disease risk
CONCLUSIONS: We report that two AD genetic risk factors, rs12445675 and rs1071644, affect AD risk by impacting the LNC-PLCG2 to PLCG2 ratio and PLCG2 exon 28 splicing, respectively.
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Structural Similarity Networks Reveal Brain Vulnerability in Dementia
INTRODUCTION: Alzheimer's disease (AD) is characterised by inter-individual heterogeneity in brain degeneration, limiting diagnostic and prognostic precision. We present a novel framework integrating Morphometric Inverse Divergence (MIND) networks with hierarchical Bayesian large-scale population modelling to identify individual-level neuroanatomical deviations.
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Downward bias in the association between APOE and Alzheimer's Disease using prevalent and by-proxy disease sampling in the All of Us Research Program
CONCLUSIONS: Our study highlights how genetic associations with ADRD can be sensitive to how cases are defined in biobanks like All of Us, with effect sizes downwardly biased when using prevalent or by-proxy cases compared to incident cases.
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Effects of the SGLT2 inhibitor dapagliflozin in early Alzheimer's disease: A randomized controlled trial
INTRODUCTION: Due to its metabolic effects, dapagliflozin, a sodium-glucose transporter 2 (SGLT2) inhibitor, holds potential as an Alzheimer's disease (AD) therapeutic.
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NIAGADS: A data repository for Alzheimer's disease and related dementia genomics
The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is the National Institute on Aging-designated national data repository for human genetics research on Alzheimer's disease and related dementias (ADRD). NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis, including whole genome and exome sequence data from the Alzheimer's Disease Sequencing Project and other...
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Return of research results across the Alzheimer's Disease Research Centers network
INTRODUCTION: The Consortium for Clarity in Alzheimer's Disease and Related Dementias Through Imaging (CLARiTI) Return of Results Core aims to develop tools and a framework for disclosing individual results at Alzheimer's Disease Research Centers (ADRCs). An understanding of current disclosure practices is necessary to generate this protocol.
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The dynamics of cognitive decline toward Alzheimer's disease progression: Results from ADSP-PHC's harmonized cognitive composites
INTRODUCTION: Accurately assessing the temporal order of cognitive decline across multiple domains is critical in Alzheimer's disease (AD). Existing literature presents controversial conclusions likely due to the use of a single cohort and different analytical strategies.
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Comparing clinical features of behavioral variant frontotemporal dementia and Alzheimer's disease using network analysis
INTRODUCTION: Clinical characterization of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) is challenging due to overlapping neuropsychiatric symptoms and cognitive profiles between the two conditions.
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Vascular risk factors mediate the relationship between education and white matter hyperintensities
INTRODUCTION: Education can protect against cognitive decline and dementia through cognitive reserve and reduced vascular risk. This study examined whether vascular risk mediates the relationship between education and white matter hyperintensity (WMH) burden.
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Integrative multi-omics approaches identify molecular pathways and improve Alzheimer's Disease risk prediction
Alzheimer's Disease (AD) is the most prevalent condition that impacts the aging population, with no effective treatment or singular underlying causal factor identified. As a complex disease, characterizing the genetic risk of developing AD has proven to be difficult; polygenic scores (PGS) exclusively use common variants which fail to fully capture disease heterogeneity. This study used univariate and multivariate approaches to characterize AD risk. Genome-, transcriptome-, and proteome-wide...
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MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification
Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in medicine, widely used to detect and assess various health conditions. Different MRI sequences, such as T1-weighted, T2-weighted, and FLAIR, serve distinct roles by highlighting different tissue characteristics and contrasts. However, distinguishing them based solely on the description file is currently impossible due to confusing or incorrect annotations. Additionally, there is a notable lack of effective tools to differentiate...