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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 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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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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Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Related Experiment Video

Updated: Jun 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Multitask Sparse Canonical Correlation Analysis and Regression with Parameter Decomposition based on Deep Subspace

Wei Kong1, Pengfei Su1, Yufang Xu2

  • 1College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, P.R. China.

Current Alzheimer Research
|June 9, 2026
PubMed
Summary

This study introduces DSR-PDMTSCCAR, a novel framework for multimodal imaging genomics in Alzheimer's disease (AD). It enhances the understanding of AD pathophysiology by capturing complex nonlinear relationships in brain imaging and genetic data.

Keywords:
Alzheimer's DiseaseDSR-PDMTSCCAR.brain imaging modalitiesgenotypepositron emission tomographysingle nucleotide polymorphismstructural MRI

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Last Updated: Jun 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data
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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

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:

  • Neuroimaging
  • Genomics
  • Computational Biology

Background:

  • Multimodal imaging genomics offers a comprehensive view of brain pathophysiology, surpassing single-modality analyses.
  • Traditional methods struggle with complex, nonlinear relationships in heterogeneous neuroimaging and genetic data for diseases like Alzheimer's disease (AD).

Purpose of the Study:

  • To introduce a novel framework, DSR-PDMTSCCAR, for advanced multimodal imaging genomics analysis in AD.
  • To overcome the limitations of linear models in capturing complex, nonlinear associations within imaging-genetics data.

Main Methods:

  • Developed DSR-PDMTSCCAR, integrating deep subspace reconstruction for nonlinear mapping and parameter decomposition.
  • Extracted modality-consistent and modality-specific features from structural MRI (sMRI) and positron emission tomography (PET) data.
  • Incorporated multitask sparse canonical correlation analysis (MTSCCA) to model heterogeneous multimodal associations.

Main Results:

  • DSR-PDMTSCCAR demonstrated superior performance over SCCA, PLS, and DCCA on simulated and ADNI cohort data.
  • Identified key AD biomarkers, including hippocampal and amygdalar alterations in sMRI/PET.
  • Detected APOE-related genetic variants associated with AD pathology, confirming biomarker relevance.

Conclusions:

  • The DSR-PDMTSCCAR framework provides robust multimodal imaging-genetics analysis for enhanced AD insights.
  • Achieved a maximum canonical correlation coefficient (CCC) of 0.1759, outperforming MTSCCA (0.1525).
  • Significantly improved PET-SNP correlation (0.1896 vs. 0.0864 for MTSCCA), advancing precision medicine in AD.