Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution.
The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.
Gitelman
Darren R
DR
The Northwestern Cognitive Brain Mapping Group, Cognitive Neurology and Alzheimer's Disease Center, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA. d-gitelman@northwestern.edu
Penny
William D
WD
Ashburner
John
J
Friston
Karl J
KJ
D001499:Bayes Theorem; D001931:Brain Mapping; D006439:Hemodynamics; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D011603:Psychophysiology