This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Independent Component Analysis (ICA) Methods for fMRI. ICA is a data-driven technique useful for analysis of fMRI data sets containing 'activations' which might not be simply predictable from the paradigm timecourse. We developed the first ever ICA method useful for studying data from groups of people, as opposed to single subjects. This was published in 'Human Brain Mapping'. We have applied this group ICA approach to allow analysis of fMRI data reporting on simulated automobile driving, as an example of a rich naturalistic behavior. While conventional fMRI analysis requires a detailed prior model of brain activation, we were able to use group ICA to study brain activity in a group of subjects participating in a simulated driving paradigm (in press 'Human Brain Mapping'. We have developed ICA approaches for analysis of complex fMRI data, as opposed to the magnitude images which most approaches use. Although MRI images are intrinsically complex (i.e., each voxel has bot h amplitude and phase), and although BOLD contrast can induce dynamic change in both amplitude and phase, most fMRI analysis methods begin by computing the magnitude and 'throwing away' the phase information. ICA analysis of the complex MRI images appears to increase the sensitivity of ICA (in press, Magnetic Resonance in Medicine). ICA approaches for fMRI analysis are valuable for revealing brain activity that would not be predicted in advance by a simple model, and for allowing the extension of fMRI methods to rich naturalistic behaviors.
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