title: On rotational invariance in adaptive spatial filtering of fMRI data.
abstract: Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.
authors.affiliation: Medical Informatics, Department of Biomedical Engineering and Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden. joary@imt.liu.se
authors.forename: Joakim
authors.initials: J
authors.lastname: Rydell
authors.forename: Hans
authors.initials: H
authors.lastname: Knutsson
authors.forename: Magnus
authors.initials: M
authors.lastname: Borga
mesh_terms: D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D005374:Filtration / Q000706:statistics & numerical data*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008432:Mathematical Computing; D010100:Oxygen / Q000097:blood; D019047:Phantoms, Imaging; D013223:Statistics as Topic
chemical_list: D010100:Oxygen