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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 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...
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...

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Related Experiment Video

Updated: May 26, 2026

Quantitative 3D In Silico Modeling (q3DISM) of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease
09:33

Quantitative 3D In Silico Modeling (q3DISM) of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

Published on: December 26, 2016

Anatomy-Guided Surface Diffusion Model for Alzheimer's Disease Normative Modeling.

Jianwei Zhang1,2, Yonggang Shi1,2,3

  • 1Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Proceedings of Machine Learning Research
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new generative model for brain imaging analysis, improving anatomical alignment for better detection of Alzheimer's disease (AD) and related cognitive impairments.

Keywords:
Alzheimer’s DiseaseCortical SurfaceDiffusion Generative Model

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Fabrication of Amyloid-β-Secreting Alginate Microbeads for Use in Modelling Alzheimer's Disease
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Normative modeling is key for understanding neurodegenerative disease variability, especially in Alzheimer's disease (AD).
  • Cortical normative modeling faces challenges with anatomical structure mismatch due to folding variability.
  • Existing deep generative models for alignment primarily use volume-based data, missing cortical details, while surface-based analysis, though sensitive, also suffers from mismatch.

Purpose of the Study:

  • To propose a novel generative normative modeling framework for surface-based brain imaging data.
  • To address anatomical structure mismatch in cortical normative modeling.
  • To improve the sensitivity of normative analysis in differentiating neurodegenerative disease stages.

Main Methods:

  • Developed a generative normative modeling framework by adapting conditional diffusion models to the spherical domain.
  • Generated normal feature map distributions conditioned on individual anatomical segmentation for enhanced geometrical alignment.
  • Evaluated the model's ability to generate anatomically aligned samples and measure individual differences.

Main Results:

  • The proposed model generates samples with superior anatomical alignment compared to traditional registration methods.
  • Ablation studies and normative assessments confirm the model's effectiveness in measuring individual deviations from the norm.
  • The framework significantly increases sensitivity in distinguishing between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) patients.

Conclusions:

  • The novel spherical generative model effectively overcomes anatomical mismatch in surface-based cortical analysis.
  • This approach enhances the precision of normative modeling for neurodegenerative disease research.
  • The framework shows promise for improved early detection and characterization of Alzheimer's disease and related cognitive decline.