NDLI: Comparing causality measures of fMRI data using PCA, CCA and vector autoregressive modelling

Content Provider	IEEE Xplore Digital Library
Author	Shah, A. Khalid, M.U. Seghouane, A.-K.
Copyright Year	2012
Description	Author affiliation: Canberra Res. Lab., Australian Nat. Univ., Canberra, ACT, Australia (Shah, A.; Khalid, M.U.; Seghouane, A.-K.)
Abstract	Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using two measures; one derived based on univariate autoregressive and autoregressive exogenous (AR/ARX) and other derived based on multivariate vector autoregressive and vector autoregressive exogenous (VAR/VARX) models. The significance and effectiveness of these measures is illustrated on both simulated and real fMRI data sets. It has been revealed that VAR modelling of the regions of interest is robust in inferring true causality compared to principal component analysis (PCA) and canonical correlation analysis (CCA) based causality methods.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	6184
Ending Page	6187
File Size	316630
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6347406
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reactive power Principal component analysis Brain models Time series analysis Vectors Mathematical model effective connectivity Functional MRI PCA CCA VAR/VARX causality
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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