TY - GEN
T1 - Accurate identification of MCI patients via enriched white-matter connectivity network
AU - Wee, Chong Yaw
AU - Yap, Pew Thian
AU - Brownyke, Jeffery N.
AU - Potter, Guy G.
AU - Steffens, David C.
AU - Welsh-Bohmer, Kathleen
AU - Wang, Lihong
AU - Shen, Dinggang
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-642-15948-0_18
DO - 10.1007/978-3-642-15948-0_18
M3 - Conference contribution
AN - SCOPUS:77958040754
SN - 3642159478
SN - 9783642159473
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 147
BT - Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Proceedings
T2 - 1st International Workshop on Machine Learning in Medical Imaging, MLMI 2010, Held in Conjunction with MICCAI 2010
Y2 - 20 September 2010 through 20 September 2010
ER -