Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition

L. Zhan, Y. Liu, J. Zhou, J. Ye, P. M. Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and Alzheimer's disease (AD), and around 10-15% of people with MCI develop AD each year. More recently, MCI has been further subdivided into early and late stages, and there is interest in identifying sensitive brain imaging biomarkers that help to differentiate stages of MCI. Here, we focused on anatomical brain 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 early versus late MCI.

Original languageEnglish (US)
Title of host publicationProceedings - International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages131-135
Number of pages5
Volume2015-July
ISBN (Print)9781479923748
DOIs
StatePublished - Jul 21 2015
Externally publishedYes
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Fingerprint

Diffusion Magnetic Resonance Imaging
Singular value decomposition
Magnetic resonance imaging
Brain
Neuroimaging
Biomarkers
Alzheimer Disease
Logistics
Feature extraction
Aging of materials
Imaging techniques
Logistic Models
Cognitive Dysfunction

Keywords

  • brain network
  • classification
  • diffusion MRI
  • high order SVD
  • Mild Cognitive Impairment

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Zhan, L., Liu, Y., Zhou, J., Ye, J., & Thompson, P. M. (2015). Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition. In Proceedings - International Symposium on Biomedical Imaging (Vol. 2015-July, pp. 131-135). [7163833] IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7163833

Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition. / Zhan, L.; Liu, Y.; Zhou, J.; Ye, J.; Thompson, P. M.

Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 131-135 7163833.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhan, L, Liu, Y, Zhou, J, Ye, J & Thompson, PM 2015, Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition. in Proceedings - International Symposium on Biomedical Imaging. vol. 2015-July, 7163833, IEEE Computer Society, pp. 131-135, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 4/16/15. https://doi.org/10.1109/ISBI.2015.7163833
Zhan L, Liu Y, Zhou J, Ye J, Thompson PM. Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition. In Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July. IEEE Computer Society. 2015. p. 131-135. 7163833 https://doi.org/10.1109/ISBI.2015.7163833
Zhan, L. ; Liu, Y. ; Zhou, J. ; Ye, J. ; Thompson, P. M. / Boosting classification accuracy of diffusion MRI derived brain networks for the subtypes of mild cognitive impairment using higher order singular value decomposition. Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. pp. 131-135
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