TITLE:
IMPROVING FMRI ANALYSIS AND MR RECONSTRUCTION WITH THE
INCORPORATION OF MR RELAXIVITES AND CORRELATION EFFECT
EXAMINATION
ABSTRACT:
Functional magnetic resonance imaging (fMRI) and functional connectivity MRI
(fcMRI) use the physical principles of nuclear MR to provide high resolution representations of
brain activity and connectivity. As the fMRI and fcMRI signals are detected from the excited
hydrogen atoms in a magnetic field, the acquired data is determined by the underlying physical
processes, such as the MR relaxivities. The Fourier encoded frequency space measurements are
reconstructed into brain images, then spatiotemporal processing operations are applied before
computing the brain activation and connectivity statistics. This dissertation seeks to utilize the
MR relaxivities on different stages of fMRI pipeline, and aims to observe the statistical
implications of the spatiotemporal processing operators on the fMRI and fcMRI data. We first
develop a new statistical complex-valued nonlinear fMRI activation model that incorporates the
MR relaxivities of gray matter into the brain activation statistics by utilizing the physical MR
magnetization equation and the first scans of the fMRI data. We provide both theoretical and
experimental comparison between the proposed model with the conventional linear magnitudeonly and complex-valued fMRI activation models. Our statistical analysis results show that the
new model provides better accuracy and stability in computing brain activation statistics while
theoretically eliminating false positives in non-gray matter areas. We then develop a linear
Fourier reconstruction operator that incorporates the MR relaxivities into the image
reconstruction process to account for their effects. The utilization of a linear system makes it
achievable to theoretically compute the statistical implications of the use of the proposed
operator. By focusing on longitudinal relaxation time, T1, to include into the image
reconstruction, we show that the application of the proposed Fourier reconstruction operator
provides better image contrast in the reconstructed images by recovering the information of the
tissue characteristics that exist prior to T1 equilibrium. We finally examine the effects of time
series preprocessing on computed functional correlations through the use of linear operators and
provide ways of accounting for such effects in computing functional activity and connectivity
statistics. Using both theoretical and experimentally acquired functional connectivity data, we
examine the correlations induced by commonly used spatial and temporal processing operations.
Furthermore, we provide the expansion of the statistical fcMRI and fMRI models to incorporate
the quantified processing induced correlations in computing brain activity and connectivity
statistics.