Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition

Alzheimer's Disease Neuroimaging Initiative (ADNI)

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.

Original languageEnglish (US)
Article number00257
JournalFrontiers in Neuroscience
Volume9
Issue numberJUL
DOIs
StatePublished - 2015

Keywords

  • Alzheimer's disease
  • Classification
  • Connectome
  • Diffusion MRI
  • High-order SVD
  • Mild cognitive impairment

ASJC Scopus subject areas

  • Neuroscience(all)

Fingerprint Dive into the research topics of 'Boosting brain connectome classification accuracy in Alzheimer's disease using higher-order singular value decomposition'. Together they form a unique fingerprint.

  • Cite this