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A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the(More)
This paper presents a novel learning algorithm that nds the linear combination of one set of multi-dimensional variates that is the best predictor, and at the same time nds the linear combination of another set which is the most predictable. This relation is known as the canonical correlation and has the property of being invariant with respect to aane(More)
This paper presents a novel algorithm for analysis of stochastic processes. The algorithm can be used to nd the required solutions in the cases of principal component analysis PCA, partial least squares PLS, canonical correlation analysis CCA or multiple linear regression MLR. The algorithm is iterative and sequential in its structure and uses on-line(More)
This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a(More)
This paper presents a method for automatic segmenta-tion of bone from volumetric computed tomography (CT) data. Due to osteoporosis, which degenerates the bone density and hence decreases the intensity of the bone in the CT dataset, it is not possible to use conventional thresh-olding techniques to handle the segmentation. Furthermore we want to use prior(More)
A computationally efficient data-driven method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The proposed method is more robust and much more computationally efficient(More)