Detection of neural activity in functional MRI using canonical correlationanalysis

A novel method for detecting neural activity in functional magnetic resonan ce imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correla tion analysis widely used in fMRI, To detect homogeneous regions of activit y, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such metho ds are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations, in addition, the proposed CCA method also makes it possible to detect activat ed brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of th e hemodynamic response. Excellent performance on real fMRI data is demonstr ated. Magn Reson Med 45:323-330, 2001. (C) 2001 Wiley-Liss, Inc.