Magnetic source magnetic resonance imaging (msMRI) is an fMRI technique that has been under development for direct detection of neuronal magnetic fields to map brain activity and has been shown to be experimentally detectable using conventional means, but there is debate on the detection of the msMRI signal since it can be only a 0.2% change. Detection of its temporal characteristics has yet to be reported and may strengthen the case for msMRI detection. The temporal characteristics of the detected msMRI signal were examined in this work, but it was found that the sensitivity of conventional analysis techniques are low within the context of msMRI, preventing consistent msMRI signal detection and analysis of its temporal characteristics. Examination of blood oxygen level dependent (BOLD) contrast contamination and application of mean-shift clustering (MSC) to fMRI analysis were performed to look into the possibility of improving the low sensitivity. fMRI analysis is commonly performed with cross correlation analysis (CCA) and techniques based on the General Linear Model (GLM), but both CCA and GLM techniques typically perform calculations on a per-voxel basis and do not consider relationships neighboring voxels may have. MSC is a technique to consider for this purpose and shows improved activation detection for both simulated and real BOLD fMRI data. To consider the issue of BOLD contamination, the hemodynamic response over time was examined using repeated median nerve stimulation. On average, the results show the BOLD signal is not detectable after the second fMRI run. The results are consistent with previous hemodynamic habituation effect studies with other types of stimulation, but they do not completely agree with findings of evoked potential studies. Overall, this work shows that the low detection sensitivity may be able to be addressed with the purpose of furthering msMRI research.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-5323 |
| Date | 01 July 2014 |
| Creators | Ai, Leo |
| Contributors | Xiong, Jinhu |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | dissertation |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright 2014 Leo Ai |
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