Accurate identification of MCI patients via enriched white-matter connectivity network

Chong Yaw Wee, Pew Thian Yap, Jeffery N. Brownyke, Guy G. Potter, David C. Steffens, Kathleen Welsh-Bohmer, Lihong Wang, Dinggang Shen

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

1 Citation (Scopus)

Abstract

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques have made understanding neurological disorders at a whole brain connectivity level possible. Accordingly, we propose a network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber penetration count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ 1, λ 2, λ 3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), the average statistics of each ROI in relation to the remaining ROIs are extracted as features for classification. These features are then sieved to select the most discriminant subset of features for building an MCI classifier via support vector machines (SVMs). Cross-validation results indicate better diagnostic power of the proposed enriched WM connection description than simple description with any single physiological parameter.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings
Pages140-147
Number of pages8
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010 - Beijing, China
Duration: 2010 Sep 202010 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6357 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010
CountryChina
CityBeijing
Period10/9/2010/9/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wee, C. Y., Yap, P. T., Brownyke, J. N., Potter, G. G., Steffens, D. C., Welsh-Bohmer, K., Wang, L., & Shen, D. (2010). Accurate identification of MCI patients via enriched white-matter connectivity network. In Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings (pp. 140-147). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6357 LNCS). https://doi.org/10.1007/978-3-642-15948-0_18