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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Classification of Alzheimer's Disease Stages: An Approach Using PCA-Based Algorithm.

Fayyaz Ahmad1, Waqar Mahmood Dar1

  • 11 Department of Statistics, University of Gujrat, Gujrat, Pakistan.

American Journal of Alzheimer'S Disease and Other Dementias
|July 31, 2018
PubMed
Summary

Early Alzheimer's disease (AD) detection aids management through a novel classification approach. This method utilizes principal component analysis (PCA) on functional MRI data to effectively identify AD stages.

Keywords:
ADNIMRIPCASPMfunctional magnetic resonance imaging

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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
  • Medical Imaging
  • Machine Learning

Background:

  • Early diagnosis of Alzheimer's disease (AD) is crucial for effective patient management and treatment.
  • Principal Component Analysis (PCA) is a recognized algorithm for feature extraction and dimensionality reduction, notably in face recognition.
  • The hippocampus is a brain region significantly impacted by Alzheimer's disease.

Purpose of the Study:

  • To propose and evaluate a novel classification approach for Alzheimer's disease (AD) stages.
  • To adapt a principal component analysis (PCA)-based algorithm for the classification of AD using neuroimaging data.
  • To effectively classify different stages of AD, including mild cognitive impairment and advanced stages.

Main Methods:

  • Feature extraction was performed on 100 images from 10 children using PCA, involving covariance matrix construction and eigenvalue/eigenvector calculation.
  • Functional magnetic resonance imaging (fMRI) and magnetic resonance imaging (MRI) data were sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
  • Clusters of voxels from the hippocampus region were selected for mild cognitive impairment, AD stage 1, stage 2, and stage 3, utilizing eigenvectors derived from fMRI data.

Main Results:

  • The PCA-based algorithm demonstrated effectiveness in classifying voxels corresponding to different stages of Alzheimer's disease.
  • Eigenvectors associated with maximum eigenvalues of fMRI data proved instrumental in the classification process.
  • The study successfully applied a face-recognition-inspired algorithm to neuroimaging data for AD staging.

Conclusions:

  • The proposed PCA-based algorithm offers a viable method for the classification of Alzheimer's disease stages using fMRI data.
  • The approach highlights the potential of leveraging established algorithms from other domains, like face recognition, for neurodegenerative disease research.
  • Accurate classification of AD stages is essential for timely intervention and personalized treatment strategies.