Functional MRI activity characterization using response time shift estimates from curve evolution.
Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brain's response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.
Desai
Mukund
M
C. S. Draper Laboratory, M53F, 555 Technology Square, Cambridge, MA 02139, USA. mdesai@draper.com
Mangoubi
Rami
R
Shah
Jayant
J
Karl
William
W
Pien
Homer
H
Worth
Andrew
A
Kennedy
David
D
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D005074:Evoked Potentials, Visual; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D011786:Quality Control; D011930:Reaction Time / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes