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

Xie Jianwen; Douglas Pamela K.; Wu Ying Nian; Brody Arthur L.; Anderson Ariana E.

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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

机译：解码功能性大脑网络的编码：非负矩阵分解（NMF），独立分量分析（ICA）和稀疏编码算法的FMRI分类比较

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Background: Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks.

机译：背景：FMRI中的脑网络通常使用空间独立分量分析（ICA）来识别，但其他数学约束提供了用于生成脑网络的替代生物合理的框架。非负矩阵分子（NMF）将通过强制阳性抑制负粗体信号。空间稀疏编码算法（L1正常化学习和K-SVD）将施加本地专业化和多任务处理的呼吁，其中单个voxel中的总观察到的活动来自受限制的可能脑网络。

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来源
《Journal of Neuroscience Methods》 |2017年第2017期|共14页
作者
Xie Jianwen; Douglas Pamela K.; Wu Ying Nian; Brody Arthur L.; Anderson Ariana E.;
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作者单位

Univ Calif Los Angeles Dept Stat Los Angeles CA USA;

Univ Calif Los Angeles Dept Psychiat &

Biobehav Sci 760 Westwood Plaza Los Angeles CA 90095 USA;

Univ Calif Los Angeles Dept Stat Los Angeles CA USA;

Univ Calif Los Angeles Dept Psychiat &

Biobehav Sci 760 Westwood Plaza Los Angeles CA 90095 USA;

Univ Calif Los Angeles Dept Stat Los Angeles CA USA;

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原文格式 PDF
正文语种 eng
中图分类人体生理学;
关键词
FMRI; Classification; ICA; NMF; K-SVD; L1 Regularized Learning; Independent component analysis;

机译：FMRI;分类;ICA;NMF;K-SVD;L1正规化学习;独立分量分析;

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1. 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 [J] . Xie Jianwen, Douglas Pamela K., Wu Ying Nian, Journal of Neuroscience Methods . 2017,第期

机译：解码功能性大脑网络的编码：非负矩阵分解（NMF），独立分量分析（ICA）和稀疏编码算法的FMRI分类比较
2. Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks [J] . Xuan Li, John Q. Gan, Haixian Wang NeuroImage . 2018,第期

机译：集体稀疏对称非负矩阵分解，用于识别休息状态脑功能网络中的重叠社区
3. Regional patterns of amyloid-beta accumulation in Alzheimer's disease: Comparison between autoencoder and the non-negative matrix factorization (NMF) [J] . Myungwon Choi, Byeongchang Jeong, Hyunchul Youn, IBRO Reports . 2019,第4期

机译：阿尔茨海默氏病中淀粉样蛋白β积累的区域模式：自动化器与非负矩阵分子（NMF）之间的比较
4. Independent component analysis, sparse component analysis and non-negative matrix factorization [C] . Amari S. Software in Air Traffic Control Systems - The Future . 1992

机译：独立成分分析，稀疏成分分析和非负矩阵分解
5. Standard and Inception-based Encoder-Decoder Neural Networks for Predicting the Solution Convergence of Design Optimization Algorithms [D] . O'Neill, Nathanial James 2019

机译：基于标准和基于初始的编解码器神经网络，用于预测设计优化算法的解决方案收敛
6. 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 [O] . Jianwen Xie, Pamela K. Douglas, Ying Nian Wu, -1

机译：解码功能性大脑网络的编码：非负矩阵分解（NMF）独立成分分析（ICA）和稀疏编码算法的fMRI分类比较
7. 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 [O] . Xie, Jianwen, Douglas, Pamela K., Wu, Ying Nian, 2016

机译：解码功能性脑网络的编码：fmRI 非负矩阵分解（NmF）的分类比较，独立分量分析（ICa）和稀疏编码算法

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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