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Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ and tau...
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
Alzheimer Disease ll: Pathophysiology01:23

Alzheimer Disease ll: Pathophysiology

Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...

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Related Experiment Video

Updated: Jun 14, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Published on: November 14, 2017

Alzheimer's clinical research data via R packages: The alzverse.

Michael C Donohue1, Kedir Hussen1, Oliver Langford1

  • 1Epstein Family Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|January 29, 2026
PubMed
Summary

New R data packages streamline access to Alzheimer's disease (AD) clinical research data. These tools enhance data sharing, promote reproducibility, and accelerate AD research through collaboration and transparency.

Keywords:
Alzheimer'sRclinical trialopen data

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Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Neuroscience
  • Biomedical Informatics
  • Clinical Research

Background:

  • Sharing clinical research data is crucial for advancing Alzheimer's disease (AD) research.
  • Existing challenges include data accessibility, standardization, documentation, usability, and reproducibility.
  • Overcoming these barriers is essential for accelerating AD research progress.

Purpose of the Study:

  • To develop R data packages for streamlined access to curated Alzheimer's disease clinical study datasets.
  • To create portable, analysis-ready data resources for key AD studies.
  • To facilitate meta-analyses and promote collaboration, transparency, and reproducibility in AD research.

Main Methods:

  • Developed R data packages (A4LEARN, ADNIMERGE2) to provide curated datasets from major AD studies like A4, LEARN, and ADNI.
  • Bundled data, documentation, and reproducible analysis vignettes into portable, analysis-ready formats installable within R.
  • Introduced the alzverse package to apply a common data standard, integrating study-specific packages for meta-analyses.

Main Results:

  • Created R packages (A4LEARN, ADNIMERGE2) offering accessible, curated datasets from significant Alzheimer's disease clinical trials and observational studies.
  • These packages provide analysis-ready data, documentation, and reproducible analysis vignettes.
  • The alzverse package unifies data through common standards, enabling integrated analyses and meta-analyses.

Conclusions:

  • R data packages offer a scalable framework to accelerate Alzheimer's disease clinical research.
  • These tools enhance data sharing, promote transparency, and improve the reproducibility of research findings.
  • By fostering collaboration and providing accessible data resources, these packages significantly contribute to advancing AD research.