A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data

A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data

The Independent Component Analysis - linear non-Gaussian acyclic model (LiNGAM), an algorithm that can be used to estimate the causal relationship among non-Gaussian distributed data, has the potential value to detect the effective connectivity of human brain areas. Under the assumptions that (a): t...

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Bibliographic Details
Main Authors: Lele eXu, Tingting eFan, Xia eWu, KeWei eChen, Xiaojuan eGuo, Jiacai eZhang, Li eYao
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-10-01
Series:Frontiers in Computational Neuroscience
Subjects:
effective connectivity
functional magnetic resonance imaging (fMRI)
Group analysis
causal structure
linear non-Gaussian acyclic model (Lingam)
pooling-LiNGAM (pLiNGAM)
Online Access:http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00125/full

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http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00125/full

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