title: Detection of neural activity in fMRI using maximum correlation modeling.
abstract: A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.
authors.affiliation: Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
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: D001921:Brain / Q000502:physiology*; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D015203:Reproducibility of Results