Identification of MCI individuals using structural and functional connectivity networks.
Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.
Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, NC, USA.
Chong-Yaw
CY
Wee
Pew-Thian
PT
Yap
Daoqiang
D
Zhang
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; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D016000:Cluster Analysis; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000473:pathology*; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging; D004522:Educational Status; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000473:pathology*; D009434:Neural Pathways / Q000473:pathology*; D009483:Neuropsychological Tests; D017711:Nonlinear Dynamics; D012372:ROC Curve; D015203:Reproducibility of Results; D060388:Support Vector Machine