Enriched white matter connectivity networks for accurate identification of MCI patients.
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 has made it possible to understand neurological disorders at a whole-brain connectivity level. Accordingly, we propose an effective 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 count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities(λ(1), λ(2), and 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), these properties can be quantified for each pair of regions with common traversing fibers. For building an MCI classifier, clustering coefficient of each ROI in relation to the remaining ROIs is extracted as feature for classification. These features are then ranked according to their Pearson correlation with respect to the clinical labels, and are further sieved to select the most discriminant subset of features using an SVM-based feature selection algorithm. Finally, support vector machines (SVMs) are trained using the selected subset of features. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy given by our enriched description of WM connections is 88.9%, which is an increase of at least 14.8% from that using simple WM connectivity description with any single physiological parameter. A cross-validation estimation of the generalization performance shows an area of 0.929 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies. Our MCI classification framework, especially the enriched description of WM connections, allows accurate early detection of brain abnormalities, which is of paramount importance for treatment management of potential AD patients.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Chong-Yaw
CY
Wee
Pew-Thian
PT
Yap
Wenbin
W
Li
Kevin
K
Denny
Jeffrey N
JN
Browndyke
Guy G
GG
Potter
Kathleen A
KA
Welsh-Bohmer
Lihong
L
Wang
Dinggang
D
Shen
D000368:Aged; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D002452:Cell Count; D003072:Cognition Disorders / Q000175:diagnosis* / Q000473:pathology; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009412:Nerve Fibers / Q000502:physiology; D009415:Nerve Net / Q000473:pathology*; D009434:Neural Pathways / Q000473:pathology; D009483:Neuropsychological Tests; D017711:Nonlinear Dynamics; D012372:ROC Curve; D015203:Reproducibility of Results