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 A...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Fla.), 2012-02, Vol.59 (3), p.2045-2056
Hauptverfasser:	Wee, Chong-Yaw, Yap, Pew-Thian, Zhang, Daoqiang, Denny, Kevin, Browndyke, Jeffrey N., Potter, Guy G., Welsh-Bohmer, Kathleen A., Wang, Lihong, Shen, Dinggang
Format:	Artikel
Sprache:	eng
Schlagworte:	Aged Algorithms Alzheimer's disease Alzheimer's disease (AD) Artificial Intelligence Brain Brain - pathology Brain network analysis multiple-kernel Support Vector Machines (SVMs) Classification Cluster Analysis Cognitive ability Cognitive Dysfunction - diagnosis Cognitive Dysfunction - pathology Cohort Studies Data Interpretation, Statistical Diffusion Tensor Imaging Diffusion tensor imaging (DTI) Educational Status Female Humans Image Processing, Computer-Assisted Linear Models Magnetic Resonance Imaging Male Medical imaging Memory Middle Aged Mild cognitive impairment (MCI) Multimodality representation Nerve Net - pathology Neural Pathways - pathology Neuropsychological Tests NMR Nonlinear Dynamics Nuclear magnetic resonance Reproducibility of Results Resting-state functional magnetic resonance imaging (rs-fMRI) ROC Curve Support Vector Machine
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