Name
Title | ||
|---|---|---|
Comparing Causality Measures Of Fmri Data Using Pca, Cca And Vector Autoregressive Modelling |
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EMBC.2012.6347406 | 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
Functional MRI, PCA, CCA, VAR/VARX, causality, effective connectivity | Causality,Data set,Canonical correlation,Artificial intelligence,Computer vision,Autoregressive model,Pattern recognition,Functional magnetic resonance imaging,Multivariate statistics,Univariate,Machine learning,Mathematics,Principal component analysis | Conference |
Volume | ISSN | Citations |
2012 | 1557-170X | 1 |
PageRank | References | Authors |
0.36 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adnan Shah | 1 | 47 | 6.08 |
| Muhammad Usman Khalid | 2 | 1 | 2.05 |
| Abd-Krim Seghouane | 3 | 78 | 12.27 |