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Thesis Format

Monograph

Degree

Master of Science

Program

Neuroscience

Supervisor

Morton, J. Bruce

Abstract

Neuromelanin (NM) is an insoluble dark pigment molecule that is found in the substantia nigra of the human brain. Due to its paramagnetic nature, NM can be imaged using MRI in the form of neuromelanin sensitive contrast. This method, known as Neuromelanin Sensitive Magnetic Resonance Imaging (NM-MRI) allows non-invasive imaging of the human substantia nigra through its by-product, NM. NM-MRI research has been mostly done using lower field strength (3 or 1.5 Tesla) MRI scans. The advent of high field strength imaging, e.g., 7 Tesla (7T) provides the opportunity to study neuromelanin production sites with higher spatial resolution and enhanced detail. Since NM-MRI research has not been conducted with high field strength imaging platforms, it is unknown whether the techniques used for quantifying NM at a lower field strength reliably extend to a high field strength platform. In the absence of this information, it is impossible to establish whether these two sequences generate the same estimates of NM. Thus, before it is possible to harness the advantages of high field strength imaging, it is critical to investigate the convergence of NM-MRI signal between 3T and 7T NM-MRI. The current study employs a within-subjects design to answer this question. Neuromelanin sensitive images were obtained from 28 healthy adult participants at both 3T and 7T. NM images were segmented both manually and with the help of a standard atlas. NM in the substantia nigra was quantified in the form of Contrast to Noise Ratio (CNR). Spearman’s rank order correlations assessed statistical dependence between the ranking of participant CNR values at 3T and 7T. We found that CNR values at 3T predicted those at 7T when standard deviation (as opposed to the mean) of the background region was used for defining noise. In addition, CNR values didn’t increase with an increase in field strength. In fact, CNR values at 7T were lower as compared to 3T. This effect was mainly due to a disproportionate increase in noise at 7T. An increased susceptibility noise is a common trade-off for better contrast associated with high field strength imaging. We discuss our findings and comment on the utility of employing high field strength NM- MRI.

Summary for Lay Audience

Dopamine is a neurotransmitter that involved in learning, reward processing, and motivation. A by-product of normal dopamine metabolism in humans, neuromelanin has recently been used to gain a deeper understanding of the human dopamine system. Because of its magnetic nature, neuromelanin gives a unique signal when imaged using a family of magnetic resonance imaging (MRI) sequences. These sequences generate neuromelanin sensitive contrast but has only been investigated in low field strength MRI. The advent of high field strength MRI has advanced the visualization of the brain because of its superior spatial resolution. Leveraging the benefits of high field strength imaging bears the promise of furthering our current understanding of neuromelanin and the dopamine system. However, before these benefits can be harnessed for neuromelanin research, it is critical to test if the image processing techniques and statistical analyses used for quantifying neuromelanin signal at low field strength provide reliable estimates of neuromelanin quantification at a high field strength imaging platform. Thus, the current research aims to investigate if estimates of neuromelanin converge between 3 Tesla (low field strength ) and 7 Tesla (high field strength) MRI scans. For this purpose, we scanned participants using both 3T and 7T MRI scans to obtain neuromelanin sensitive images. Images were analyzed using different techniques to assess NM-MRI signal strength in neuromelanin rich areas, as compared to areas with no neuromelanin. In this paper, we discuss our findings and comment on the utility of employing high field strength imaging for studying neuromelanin signal.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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