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

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.

• Publication year

  2015

• Venue

  Frontiers in Neuroscience

• Publication date

  2015-07-24

• Fields of study

  Medicine, Computer Science, Psychology

• Identifiers
  DOI 10.3389/fnins.2015.00257PMID 26257601PMCID 4513242
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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