Paper Info
Title
Connectivity analysis of human functional MRI data: from linear to nonlinear and static to dynamic
Abstract
In this paper, we describe approaches for analyzing functional MRI data to assess brain connectivity. Using phase-space embedding, bivariate embedding dimensions and delta-epsilon methods are introduced to characterize nonlinear connectivity in fMRI data. The nonlinear approaches were applied to resting state data and continuous task data and their results were compared with those obtained from the conventional approach of linear correlation. The nonlinear methods captured couplings not revealed by linear correlation and was found to be more selective in identifying true connectivity. In addition to the nonlinear methods, the concept of Granger causality was applied to infer directional information transfer among the connected brain regions. Finally, we demonstrate the utility of moving window connectivity analysis in understanding temporally evolving neural processes such as motor learning.
Year
DOI
Venue
2006
10.1007/11812715_3
MIAR
Keywords
Field
DocType
human functional mri data,connectivity analysis,nonlinear connectivity,nonlinear approach,resting state data,continuous task data,fmri data,nonlinear method,brain connectivity,linear correlation,true connectivity,functional mri data,information transfer,resting state,nonlinear dynamics,phase space,motor learning,granger causality
Nonlinear system,Embedding,Information transfer,Motor learning,Functional magnetic resonance imaging,Pattern recognition,Resting state fMRI,Granger causality,Artificial intelligence,Bivariate analysis,Mathematics,Machine learning
Conference
Volume
ISSN
ISBN
4091
0302-9743
3-540-37220-2
Citations 
PageRank 
References 
3
0.50
6
Authors
4
Name
Order
Citations
PageRank
Gopikrishna Deshpande119515.65
Stephen LaConte226528.11
Scott Peltier3787.35
Xiaoping Hu4945.37