.png){kind=icon}
返回
Estimating Effective Connectivity from fMRI Data Using Factor-based Subspace Autoregressive Models
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2014.2365634.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider the problem of identifying large-scale effective connectivity of brain networks from fMRI data. Standard vector autoregressive (VAR) models fail to estimate reliably networks with large number of nodes. We propose a new method based on factor modeling for reliable and efficient high-dimensional VAR analysis of large networks. We develop a subspace VAR (SVAR) model from a factor model (FM), where observations are driven by a lower-dimensional subspace of common latent factors with an AR dynamics. We consider two variants of principal components (PC) methods that provide consistent estimates for the FM hence the implied SVAR model, even of large dimensions. Information criterion is used to select the optimal subspace dimension. We established asymptotic normality and convergence rates for the estimated SVAR coefficients matrix. Evaluation on simulated resting-state fMRI shows that the SVAR models are more robust and produce better connectivity estimates than the classical model for a moderately-large network analysis. Results on real data by varying the subspace dimensions identify strong connections in the default mode network and reveal hierarchical connectivity of resting-state networks with distinct functional relevance.
Keyword:
Brain effective connectivity
factor model
fMRI
vector autoregressive model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
.png){kind=icon}
分享收藏Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging通过功能磁共振成像观察到的大脑活动的自发波动
PREAI
分享收藏Estimating brain functional connectivity with sparse multivariate autoregression用稀疏多元自回归估计脑功能连通性
OAAI
OAAI分享收藏
查看更多内容