The overall goal of this project is to develop a local multivariate analysis software package for fMRI data analysis. It will provide psychologists and neuroscientists a more powerful tool to analyze their fMRI data using advanced multivariate methods. This project will lead to better brain activation maps and thus promote the discovery of currently unknown aspects of brain function. Mass-univariate analysis, such as the general linear model (GLM), is the prevailing fMRI data analysis method. However, it suffers from blurring of edges of activation and potential elimination of the detection of weak activated regions due to routinely applied fixed isotropic spatial Gaussian smoothing. Local multivariate methods such as canonical correlation analysis (CCA) and its variants have been shown to significantly increase the detection power of fMRI activations and improve activation maps. As an advantage, CCA uses adaptive spatial filtering kernels to accurately extract the signal better in a noisy environment. However, there are several drawbacks, particularly low spatial specificity, long computational time, and single-factor experimental design limitation. Furthermore, a parametric estimation method does not exist to determine the family-wise error rate, no extension to group analysis has been investigated, and no studies extending local CCA to nonlinear CCA for fMRI data using kernel methods have been systematically carried out. All these drawbacks prevent local CCA methods from being widely accepted in neuroscience research in fMRI. In this proposal, our goals are to eliminate these drawbacks using novel local multivariate analysis methods (based on CCA) and to develop a software tool to widen its broader application in the neuroscience research community. We expect this software tool to be particularly valuable for neuroscience research where detections of weak activations or spatially localized patterns of activations are desired. As high resolution imaging and computer power advance, we expect an increase in demand for this software tool, thus advancing new discoveries of brain function and more precise spatial localization of activations. As a particular application, we will focus on studying memory actions using a novel event-related recognition paradigm to investigate the effects of familiarity and recollection in subregions of the medial temporal lobes (MTL) for high resolution fMRI. This research will advance our understanding of hippocampal/MTL contributions to memory, which can substantially advance our understanding of the memory deficits associated with a number of debilitating neurological and psychiatric conditions that show abnormalities in these regions, including mild cognitive impairment (MCI), Alzheimer?s disease, schizophrenia, and major depression. More generally, it will provide psychologists and neuroscientists a more powerful tool to analyze their fMRI data using advanced multivariate methods.
The proposed research is highly relevant to public health because advanced mathematical and statistical methods will allow better analysis of high-resolution fMRI data leading to better characterization of cortical function. For this project we choose to focus on studying memory activation in the medial temporal lobes using high-resolution imaging and multivariate analysis which could substantially advance our understanding of the memory deficits associated with a number of debilitating neurological and psychiatric conditions that show abnormalities in these regions, including mild cognitive impairment (MCI), Alzheimer's disease, schizophrenia, and major depression. Our proposed fMRI analysis methods also have great potential for significantly advancing our understanding of other neurological and psychiatric conditions.
|Cordes, Dietmar; Nandy, Rajesh R; Schafer, Scott et al. (2014) Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI. Neuroimage 89:314-30|