Resting-state multi-spectrum functional connectivity networks for identification of MCI patients.
In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered (0.025 ≤ ƒ ≤ 0.100 Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients.
Image Display, Enhancement, and Analysis Laboratory, Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Chong-Yaw
CY
Wee
Pew-Thian
PT
Yap
Kevin
K
Denny
Jeffrey N
JN
Browndyke
Guy G
GG
Potter
Kathleen A
KA
Welsh-Bohmer
Lihong
L
Wang
Dinggang
D
Shen
D000368:Aged; D000375:Aging / Q000378:metabolism / Q000473:pathology; D001481:Basal Metabolism; D001921:Brain / Q000378:metabolism / Q000473:pathology* / Q000503:physiopathology; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D015999:Multivariate Analysis; D009415:Nerve Net / Q000378:metabolism / Q000473:pathology* / Q000503:physiopathology; D059906:Neuroimaging