Novel local PCA-based method for detecting activation signals in fMRI

A novel Local Principal Component Analysis (LPCA) technique is presented in this paper for activation detection in functional Magnetic Resonance Imaging (fMRI) without explicit knowledge about the shape of the activation signal. The proposed LPCA method is very different from the traditional PCA methods for fMRI signal detection in principle. At first, our LPCA algorithm does not require any orthogonality assumption between the activation signal and other signal components, while the traditional PCA methods are based on this assumption. In addition, our LPCA algorithm applies PCA to the temporal sequence of each individual voxel instead of applying PCA to the whole data set. In our algorithm, we first apply a linear regression procedure to alleviate the common baseline drift artifact. Then the baseline-corrected temporal signals are partitioned into active and inactive segments according to the paradigm used for the fMRI data acquisition. Several most dominant principal components are computed from all these segments for each voxel by the PCA. By projecting the segments of each voxel onto a linear subspace formed by the corresponding dominant principal components, two separate clusters are formed from the active and inactive segments. An activation measure is defined based on the degree of separation between these two clusters in the projection space. Experimental results of applying our LPCA algorithm to detect fMRI activation signals on various data sets are given. From our experiments, the LPCA algorithm in general provides 4 - 6 times signal-to-noise ratio (SNR) improvement over the standard t-test method.