TY - GEN
T1 - A semiparametric PCA approach to fMRI data analysis
AU - Ulfarsson, M. O.
AU - Solo, V.
PY - 2010
Y1 - 2010
N2 - Functional Magnetic Resonance (fMRI) data is most often analyzed using linear regression type methods that consider each voxel separately or by using exploratory methods such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA). In this paper we introduce a model, which we call XnPCA, that combines regression with PCA. Unlike the linear regression methods XnPCA allows for non-stationary noise. Additionally, since XnPCA is based on the maximum likelihood framework the Bayesian information criterion (BIC) can be used for model selection and comparison. We compare XnPCA to a regression model commonly used in fMRI research using real data from a combined visual-motor experiment.
AB - Functional Magnetic Resonance (fMRI) data is most often analyzed using linear regression type methods that consider each voxel separately or by using exploratory methods such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA). In this paper we introduce a model, which we call XnPCA, that combines regression with PCA. Unlike the linear regression methods XnPCA allows for non-stationary noise. Additionally, since XnPCA is based on the maximum likelihood framework the Bayesian information criterion (BIC) can be used for model selection and comparison. We compare XnPCA to a regression model commonly used in fMRI research using real data from a combined visual-motor experiment.
KW - Functional magnetic resonance imaging (fMRI)
KW - Principal Component Analysis (PCA)
KW - Regression
UR - https://www.scopus.com/pages/publications/78049362338
U2 - 10.1109/ICASSP.2010.5495164
DO - 10.1109/ICASSP.2010.5495164
M3 - Conference contribution
SN - 9781424442966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 634
EP - 637
BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
T2 - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Y2 - 14 March 2010 through 19 March 2010
ER -