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Alzheimer's Disease: Overview

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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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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Classification of Alzheimer's disease using unsupervised diffusion component analysis.

Dominique Duncan¹, Thomas Strohmer

¹Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, United States.

Mathematical Biosciences and Engineering : MBE

|October 25, 2016

Summary

This summary is machine-generated.

This study introduces a new automated method using Unsupervised Diffusion Component Analysis to distinguish early Alzheimer's disease (AD) from healthy brain MRI scans. This novel technique offers a practical tool for early AD identification.

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

Medical Imaging
Neuroscience
Machine Learning

Background:

Alzheimer's disease (AD) diagnosis relies on identifying subtle changes in brain structure.
Early detection of AD is crucial for effective intervention and management.
Standard MRI analysis faces challenges due to variations in scanners and individual brain anatomy.

Purpose of the Study:

To develop an automated method for discriminating early-stage Alzheimer's disease (AD) from healthy individuals using magnetic resonance imaging (MRI).
To introduce and evaluate a novel unsupervised algorithm, Unsupervised Diffusion Component Analysis (UDCA), for AD detection.

Main Methods:

Unsupervised Diffusion Component Analysis (UDCA), an extension of diffusion mapping, was employed to reduce MRI data dimensionality.
The algorithm generates geometric representations of complex MRI data for pattern recognition.
A nonlinear and local network approach was utilized to address scanner calibration and individual brain variations.

Main Results:

The UDCA algorithm successfully reduces data dimensionality and identifies patterns for distinguishing AD from healthy brains.
The method demonstrated robustness against calibration differences across scanners and centers.
It effectively accounts for individual variations in brain size and shape.

Conclusions:

The developed automated and unsupervised algorithm shows potential as a practical tool for clinicians.
This approach facilitates the early identification of Alzheimer's disease patients through MRI analysis.
UDCA offers a novel, non-linear, and local network strategy for improved AD detection.