FUNCTIONAL MRI DATA AQUISITION AND ANALYSIS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The overall goals of this resource, with respect to fMRI, have been and remain to enhance the specificity and sensitivity of human functional MRI through improvements in data acquisition and analysis. In the previous grant period, the aims of this TRD were focused on the development of perfusion-based fMRI acquisition methods and Independent Component Analysis (ICA) based methods for fMRI analysis. As part of the first aim, we developed a novel method for performing fMRI using cerebral blood volume (CBV) dependent contrast, dubbed "Vascular Space Occupancy (VASO)" fMRI. This approach, which does not require the use of a contrast agent, was shown to have increased spatial specificity compared to Blood Oxygenation Level Dependent (BOLD) fMRI. For the second aim, we developed new Independent Component Analysis (ICA) approaches to fMRI data analysis, and demonstrated that ICA can find brain activation overlooked by standard approaches and can yield robust results even when the timing of brain activation does not precisely match that anticipated by the investigator. In this renewal application, this TRD will focus on fMRI acquisition and analysis developments that will address several issues confronting our collaborators (see Table 1, next page). For instance, our pediatric collaborators studying a variety of developmental disorders such as ADHD, autism, reading disability, and trauma-based functional deficits have to deal with reduced compliance and would like to scan faster. A similar situation is true for patients with dementia and psychosis. In addition, the effects studied by these investigators and by researchers studying memory function and attention often consist of very small signal modulations on top of more robust signal activations (visual, motor). Some investigators would like higher spatial resolution to better study small cortical areas. All of these problems can be reduced by going to higher magnetic field strength (7.0T), where increased signal to noise is available. Another issue confronting our collaborators is the limitation of conventional fMRI data analysis to pre-conceived hemodynamic responses, which may miss important underlying brain activities. We have addressed this by going to the "data-driven" methodology of independent component analysis, which has revealed several additional activation components. However, this has led to fundamental questions about the meaning and origin of these "extra" activation components not present in standard fMRI data analyses. This therefore requires assessment of the specificity of such ICA of fMRI results. Finally, many of our collaborators are pursuing fMRI studies in children and in patients with neuro-degenerative disease. These data can be problematic to analyze, as such research participants may show poor compliance with experimental paradigms. The ultimate example of this may be the coma patients of Dr. Christensen. ICA allows studies to be performed with paradigms that reduce demands on compliance, including such so-called rich naturalistic behaviors as playing a video game or watching a movie or, in the case of coma, just listening to a relative talking. Our overall goals in the coming period are therefore to enhance fMRI sensitivity, to address experimental questions related to fMRI-ICA specificity, and to develop approaches to functional brain mapping for basic and clinical research which reduce demands on participant compliance. The specific aims are: AIM 1. Optimize fMRI data acquisition at 7.0 Tesla. We will optimize fMRI acquisitions at 7.0 T with respect to parallel imaging acceleration factor, shimming, TE and TR choice, and slice number, depending on the individual needs for our neuroscience collaborators. AIM 2. Characterize the independent components of fMRI data. To characterize the independent components of fMRI data, we will acquire additional image data, including BOLD fMRI acquisitions at higher temporal and spatial resolution, fMRI acquisitions using different contrasts, namely VASO and arterial spin labeling (ASL), and structural imaging including MP-RAGE and MR angiography. We will use data acquired at three field strengths (1.5, 3.0, and 7.0 Tesla), to assess the independent components found in BOLD fMRI data. These data will be analyzed using multiple approaches including feature-based joint ICA. AIM 3. Develop ICA methods for fMRI data from rich naturalistic behaviors. We will develop advanced ICA methods for analysis of fMRI data from persons engaged in rich naturalistic behaviors, and work with our collaborators to apply these approaches to their research aims. This will be done for data from both individuals and groups. These approaches will ultimately be combined with the DTI efforts in TRD 3 for connectivity-function assessment.