首页> 美国卫生研究院文献>other >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

【2h】

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）独立成分分析（ICA）和稀疏编码算法的fMRI分类比较

代理获取

本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。

页面导航

摘要
著录项
相似文献
相关主题

摘要

BackgroundBrain 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：背景技术fMRI中的脑神经网络通常使用空间独立成分分析（ICA）进行识别，而其他数学约束条件为生成脑网络提供了生物学上可行的替代框架。非负矩阵分解（NMF）将通过增强正性来抑制负的BOLD信号。空间稀疏编码算法（L1正则化学习和K-SVD）将强加局部专业化，并阻止多任务处理，因为单个体素中观察到的总活动源自有限数量的可能的大脑网络。

著录项

期刊名称 other
作者
Jianwen Xie; Pamela K. Douglas; Ying Nian Wu; Arthur L. Brody; Ariana E. Anderson;
展开▼
作者单位

展开▼
年(卷),期 -1(282),-1
年度 -1
页码 81–94
总页数 36
原文格式 PDF
正文语种
中图分类
关键词

相似文献

外文文献
中文文献
专利

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. Comparison between brain sub-networks decomposed by auto encoder (AE) and graph auto encoder (GAE) with non-negative weight constraints and sparse encoding [J] . Pilsub Lee, Myungwon Choi, Daegyeom Kim, IBRO Reports . 2019,第4期

机译：具有非负权重约束和稀疏编码的自动编码器（AE）和图自动编码器（GAE）分解的脑子网络之间的比较
3. 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
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. Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization [O] . Qingyao Wu, Zhenyu Wang, Chunshan Li, 2015

机译：通过正则化非负矩阵分解来预测稀疏标签PPI网络中的蛋白质功能特性
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

摘要

著录项

相似文献

相关主题

期刊订阅