title: Neuronal event detection in fMRI time series using iterative deconvolution techniques.
abstract: An iterative estimation algorithm for deconvolution of neuronal activity from Blood Oxygen Level Dependent (BOLD) time series data is presented. The algorithm requires knowledge of the hemodynamic impulse response function but does not require knowledge of the stimulation function. The method uses majorization-minimization of a cost function to find an optimal solution to the inverse problem. The cost function includes penalties for the l(1) norm, total variation and negativity. The algorithm is able to identify the occurrence of neuronal activity bursts from BOLD time series accurately. The accuracy of the algorithm was tested in simulations and experimental fMRI data using blocked and event-related designs. The simulations revealed that the algorithm is most sensitive to contrast-to-noise ratio levels and to errors in the assumed hemodynamic model and least sensitive to autocorrelation in the noise. Within normal fMRI conditions, the method is effective for event detection.
authors.affiliation: Functional MRI laboratory, University of Michigan, MI 48109-2108, USA. hernan@umich.edu
authors.forename: Luis
authors.initials: L
authors.lastname: Hernandez-Garcia
authors.forename: Magnus O
authors.initials: MO
authors.lastname: Ulfarsson
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009474:Neurons / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*