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Alzheimer's Disease: Overview01:26

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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.
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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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Related Experiment Video

Updated: May 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Multimodal Classification of Alzheimer's Disease Using Longitudinal Data Analysis and Hypergraph Regularized

Shuaiqun Wang1, Huan Zhang1, Wei Kong1

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

Bioengineering (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for Alzheimer's disease (AD) classification using longitudinal neuroimaging data and hypergraphs. The approach improves diagnostic accuracy by analyzing changes over time, aiding in early detection and biomarker identification.

Keywords:
Alzheimer’s diseasehypergraph learninglongitudinal datamulti-task learningmultimodal classification

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Biomedical Data Analysis

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder impacting memory and cognition.
  • Magnetic Resonance Imaging (MRI) is crucial for AD diagnosis, but current methods often overlook longitudinal data.
  • Analyzing temporal changes in neuroimaging is vital for accurate AD monitoring and diagnosis.

Purpose of the Study:

  • To develop a multi-task feature selection algorithm for Alzheimer's disease classification using longitudinal imaging and hypergraphs (THM2TFS).
  • To improve the accuracy of Alzheimer's disease diagnosis by leveraging temporal dependencies in neuroimaging data.
  • To identify key biomarkers associated with Alzheimer's disease progression.

Main Methods:

  • A multi-task learning framework was established, treating feature selection at each time point as a separate task.
  • Group sparse regularization, incorporating hypergraph-induced and fused sparse Laplacian regularization, was used to model subject relationships and temporal changes.
  • Multi-kernel Support Vector Machines (SVM) integrated selected features for final classification.
  • Functional MRI and structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) across four time points were utilized.

Main Results:

  • The THM2TFS method achieved high classification accuracies: 96.75% (AD vs. NC), 93.45% (MCI vs. NC), and 83.78% (AD vs. MCI).
  • The algorithm effectively captured relevant information from longitudinal imaging data.
  • The proposed method demonstrated improved classification accuracy compared to existing approaches.

Conclusions:

  • The THM2TFS algorithm offers a robust approach for Alzheimer's disease classification using longitudinal neuroimaging data.
  • The method enhances diagnostic accuracy and aids in identifying critical biomarkers for Alzheimer's disease.
  • This work highlights the importance of incorporating temporal dynamics in neuroimaging analysis for neurodegenerative disease research.