fMRI Detection by Clustering Model Fitting Parameters

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DESCRIPTION (provided by applicant): The project proposed herein will develop a flexible analytical framework for functional magnetic resonance imaging (fMRI) data that will identify multi-voxel regions of activation through statistical evaluation of hemodynamic response model fits to the observed blood-oxygenation level-dependent effect responses. The proposed framework will allow the incorporation of multiple hemodynamic response models that may be used to evaluate fMRI responses both on a single-trial and averaged (blocked) basis. In addition to an expected improvement in the identification of "true" fMRI activation, this framework is expected to be capable of reducing the background noise level in summary images by breaking from the tradition of analyzing each individual voxel for its statistical significance and subsequently grouping "significant" voxels into regions of activation. Finally, this framework will also allow us to identify regions that are co-activated, or at least exhibit similar temporal structure in response to the stimulation, independent of the amplitude of the individual responses, thus contributing a new tool to studies of functional connectivity. Aim: Develop a novel framework for fMRI data analysis that uses a priori knowledge of both the experimental paradigm and hemodynamic response to detect activation on a regional basis. Achievement of the specific aim of this proposal will provide a powerful tool that may greatly enhance the future results obtained by the functional MRI community. Development of an analytical procedure that focuses on (potentially disjoint) multi-voxel regions of interest as the fundamental activation unit to be detected will increase the probability of a detected voxel being a true detection, enhancing the usefulness of fMRI in furthering our understanding of both normal and disordered cortical activity.