Discovering structure in the space of activation profiles in fMRI.
We present a method for discovering patterns of activation observed through fMIRI in experiments with multiple stimuli/tasks. We introduce an explicit parameterization for the profiles of activation and represent fMRI time courses as such profiles using linear regression estimates. Working in the space of activation profiles, we design a mixture model that finds the major activation patterns along with their localization maps and derive an algorithm for fitting the model to the fMRI data. The method enables functional group analysis independent of spatial correspondence among subjects. We validate this model in the context of category selectivity in the visual cortex, demonstrating good agreement with prior findings based on hypothesis-driven methods.
Lashkari
Danial
D
Computer Science and Artificial Intelligence Laboratory, MIT, USA.
Vul
Ed
E
Kanwisher
Nancy
N
Golland
Polina
P
D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000033:anatomy & histology / Q000502:physiology*