Independent component analysis-based classification of Alzheimer's disease MRI data

Wenlu Yang, Ronald L.M. Lui, Jia Gao, Tony Chan, Shing Tung Yau, Reisa A. Sperling, Xudong Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

There is an unmet medical need to identify neuroimaging biomarkers that allow us to accurately diagnose and monitor Alzheimer's disease (AD) at its very early stages and to assess the response to AD-modifying therapies. To a certain extent, volumetric and functional magnetic resonance imaging (fMRI) studies can detect changes in structure, cerebral blood flow, and blood oxygenation that distinguish AD and mild cognitive impairment (MCI) subjects from healthy control (HC) subjects. However, it has been challenging to use fully automated MRI analytic methods to identify potential AD neuroimaging biomarkers. We have thus proposed a method based on independent component analysis (ICA) for studying potential AD-related MR image features that can be coupled with the use of support vector machine (SVM) for classifying scans into categories of AD, MCI, and HC subjects. The MRI data were selected from the Open Access Series of Imaging Studies (OASIS) and the Alzheimer's Disease Neuroimaging Initiative databases. The experimental results showed that the ICA method coupled with SVM classifier can differentiate AD and MCI patients from HC subjects, although further methodological improvement in the analytic method and inclusion of additional variables may be required for optimal classification.

Original languageEnglish (US)
Pages (from-to)775-783
Number of pages9
JournalJournal of Alzheimer's Disease
Volume24
Issue number4
DOIs
StatePublished - Jan 1 2011

Keywords

  • Alzheimer's disease
  • independent component analysis
  • magnetic resonance imaging
  • mild cognitive impairment
  • neuroimaging biomarker
  • support vector machine

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Psychology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health

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