title: Joint fMRI brain activation detection and segmentation using level sets.
abstract: This paper proposes a parametric, multivariate method for the joint detection and segmentation of brain activation based on fMRI data. The proposed technique uses region based level sets to separate between the task-related and non-task-related regions and performs, at each iteration of level set evolution, a separate multivariate linear model (MLM) analysis in each of the two regions. Simulations using synthetic data generated based on typical experimental parameters and noise levels showed a false positive rate of 6% and a false negative rate of 2% for the results obtained with the proposed technique. The performance of the level sets method was further investigated by analysing empirical fMRI data from two subjects performing a visual and a motor task. Our results indicate that the proposed technique provides brain activation results comparable to those obtained by a standard univariate approach, with the advantage that it does not require the definition of a significance threshold.
authors.affiliation: Institute for Systems and Robotics - Instituto Superior Técnico, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal. msilveira@isr.ist.utl.pt
authors.forename: Margarida
authors.initials: M
authors.lastname: Silveira
authors.forename: Patricia
authors.initials: P
authors.lastname: Figueiredo
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009043:Motor Activity / Q000502:physiology; D019047:Phantoms, Imaging; D010775:Photic Stimulation; D015203:Reproducibility of Results; D013647:Task Performance and Analysis