publication . Other literature type . Article . 2015

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

Paul Thompson;
Open Access
  • Published: 24 Jul 2015
  • Publisher: Frontiers Media SA
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 n...
free text keywords: Neuroscience, Methods, Alzheimer's disease, mild cognitive impairment, diffusion MRI, connectome, high-order SVD, classification, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Funded by
  • Funder: Canadian Institutes of Health Research (CIHR)
NSF| III-COR-Small: Beyond Feature Selection and Extraction - An Integrated Framework for High-Dimensional Data of Small Labeled Samples
  • Funder: National Science Foundation (NSF)
  • Project Code: 0812551
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
NIH| Computational Methods for Expression Image Analysis
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01LM010730-02
NIH| MRI Biomarker Discovery for Preclinical Alzheimers Disease with Geometry Methods
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R21AG043760-01A1
  • Funder: National Institutes of Health (NIH)
  • Project Code: 3P30AG010129-11S1
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