Comparing causality measures of fMRI data using PCA, CCA and vector autoregressive modelling.
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.
National ICT Australia, Canberra Research Laboratory, The Australian National University, College of Engineering and Computer Science, Canberra, Australia. adnan.shah@nicta.com.au
Adnan
A
Shah
Muhammad Usman
MU
Khalid
Abd-Krim
AK
Seghouane
D001921:Brain / Q000502:physiology; D015984:Causality; D006801:Humans; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D025341:Principal Component Analysis