Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification.

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第一作者： Chong-Yaw,Wee

第一单位： Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

作者： Chong-Yaw,Wee ^[1] ; Sen,Yang ^[2] ; Pew-Thian,Yap ^[1] ; Dinggang,Shen ^[3] ; Alzheimer’s Disease Neuroimaging Initiative ^[4]

作者单位： Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. ^[1] Department of Computer Science and Engineering, Arizona State University, Tempe, AZ, USA. ^[2] Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. dgshen@med.unc.edu. ^[3] Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea. dgshen@med.unc.edu. ^[4]

关键词 Mild Cognitive Impairment (MCI)Resting-state functional MRI (R-fMRI)Sliding window correlation Sparse temporal networks Temporal dynamics Temporal smoothness