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首页> Journal of neuroscience methods> Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

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作者： Jianwen,Xie ^[1] ; Pamela K,Douglas ^[2] ; Ying Nian,Wu ^[1] ; Arthur L,Brody ^[2] ; Ariana E,Anderson ^[3]

作者单位： Department of Statistics, University of California, Los Angeles, United States. ^[1] Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States. ^[2] Department of Statistics, University of California, Los Angeles, United States; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States. Electronic address: arianaanderson@mednet.ucla.edu. ^[3]

关键词 Artifacts Classification FMRI ICA Image processing Independent component analysis K-SVD L1 Regularized Learning Machine learning NMF Negative BOLD signal Non-negative matrix factorization Pattern recognition Random forests Sparsity Support vector machines

主题词算法(Algorithms);听觉(Auditory Perception);脑(Brain);脑图(Brain Mapping);脑血管循环(Cerebrovascular Circulation);人类(Humans);磁共振成像(Magnetic Resonance Imaging);运动知觉(Motion Perception);神经通路(Neural Pathways);神经心理学测验(Neuropsychological Tests);氧(Oxygen);休息(Rest)

DOI 10.1016/j.jneumeth.2017.03.008

PMID 28322859

发布时间 2022-12-06

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Journal of neuroscience methods

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