title: Adaptive analysis of fMRI data.
abstract: This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.
authors.affiliation: Department of Biomedical Engineering, Linköping University, Linköping, Sweden. olafr@imt.liu.se
authors.forename: Ola
authors.initials: O
authors.lastname: Friman
authors.forename: Magnus
authors.initials: M
authors.lastname: Borga
authors.forename: Peter
authors.initials: P
authors.lastname: Lundberg
authors.forename: Hans
authors.initials: H
authors.lastname: Knutsson
mesh_terms: D000465:Algorithms; D001931:Brain Mapping; D002560:Cerebrovascular Circulation / Q000502:physiology; D003627:Data Interpretation, Statistical; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*