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Multi-contrast Magnetic Resonance Imaging of Myelin and Iron in the Brain

dc.contributor.advisorBoretius, Susann Prof. Dr.
dc.contributor.authorDadarwal, Rakshit
dc.date.accessioned2022-09-27T14:11:12Z
dc.date.available2022-12-13T00:50:09Z
dc.date.issued2022-09-27
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?ediss-11858/14262
dc.identifier.urihttp://dx.doi.org/10.53846/goediss-9436
dc.language.isoengde
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.ddc570de
dc.titleMulti-contrast Magnetic Resonance Imaging of Myelin and Iron in the Brainde
dc.typedoctoralThesisde
dc.contributor.refereeBrück, Wolfgang Prof. Dr.
dc.date.examination2021-12-14de
dc.description.abstractengThe non-invasive characterization of brain myelin has essential applications in basic neuroscience and the clinical management of white matter brain diseases and other inflammatory and neurodegenerative diseases. Magnetic resonance imaging (MRI) provides a variety of image contrasts that change in relation to myelin alterations. However, it is not clear how these potential MRI biomarkers of myelin reflect the underlying tissue structure at the meso- and microscale. Due to physical and technical limitations, the spatial resolution of MRI is poor in comparison to the size of tissue constituents. Thus, the observed effects in each image voxel are the sum of a set of tissue substructures. As a result, numerous biophysical mechanisms contribute to the MRI signal in an image voxel. This thesis evaluates currently used MRI contrast mechanisms regarding their ability to serve as in vivo biomarkers for myelin- and age-related changes in the brain. To this end, mouse mutants of myelin genes and non-human primates at different ages have been examined using these MRI techniques, including diffusion-based MRI (dMRI), Quantitative Susceptibility Mapping (QSM), and MR techniques utilizing the transfer of magnetization. dMRI has the ability to link the MRI measurements at a millimeter scale to tissue microstructures on a scale much smaller than the nominal image resolution. The reason for this is that the measured MR signal originates from the random motion of water molecules in the brain tissue at the cellular level. The difficulty lies in the disentanglement of the contributors to this MR signal, as the signal is eventually averaged over an image voxel of several micrometers. A variety of mathematical models has been proposed to decipher the tissue properties from the obtained dMRI signal. However, it is not clear how effectively these methods capture myelin and axon alterations. In my first study (Chapter 1), I explored four different approaches of signal modeling to fit the dMRI signal obtained from mouse mutants with varying levels of myelin and axonal abnormalities. I discovered that most quantitative estimates of dMRI are sensitive to myelin and axon alterations but with poor specificity when compared to the tissue characteristics revealed by electron microscopy. A still relatively new MRI technique that is sensitive to myelin and iron in the brain tissue is QSM. QSM has been applied in patients with demyelinating and neurodegenerative diseases as well as in human studies on healthy aging. Non-human primates (NHP), our closest relatives in the animal kingdom, are of particular value for studying age-related alterations in the brain as we share many similarities in neuroanatomy and cognitive abilities. In NHP, QSM has, however, not yet been established. In my second study (Chapter 2), I established QSM and the mapping of its counterpart, the effective transverse relaxation rate (R2*) in the macaque monkey brain. I then compared the results of QSM and the values of R2* to those in the human brain. I observed comparable QSM and R2* results in subcortical regions of the human and monkey brains, except for the red nucleus, where humans showed higher magnetic susceptibility compared to macaques. Significant differences in QSM were also observed in white matter structures, such as in the body of the corpus callosum and the anterior commissure. Here, monkeys showed a lower susceptibility than humans. In addition, I discovered that the reference brain region chosen to normalize the QSM values could significantly affect comparative QSM studies in humans and NHPs. Another intriguing observation was that the human cerebrospinal fluid had a paramagnetic QSM contrast, whereas the monkey cerebrospinal fluid was diamagnetic, best explained by the significantly higher iron content in humans. Quantitative MRI and, in particular, region-of-interest analyses using techniques such as the one mentioned above significantly benefit (or even require) species-specific anatomical MRI atlases. The macaque brain atlases that are currently publicly available are from rhesus macaques, which have a different brain morphology than cynomolgus macaques, the NHP species most used in biomedical research and preclinical studies. Moreover, the majority of rhesus macaque atlases rely on T1-weighted images only. These atlases provide an excellent gray-to-white matter contrast but lack, for instance, contrast from subcortical structures. In my third study (Chapter 3), I developed the Deutsches Primatenzentrum cynomolgus macaque (DPZCYNO) template. This dedicated high-resolution (0.25 mm) atlas was designed with a stereotaxic orientation for single-subject MR image standardization and anatomical structural localization. This stereotaxic orientation is particularly necessary for invasive studies, which, for instance, require a precise targeting of brain regions by the inserted electrodes. In addition, to overcome the limitations of a single MRI contrast, I created MRI templates from multiple anatomical (T1-weighted, T2-weighted, Magnetization Transfer weighted, and Multi-echo gradient-recalled echo) and parametric (QSM, R2*, magnetization transfer saturation, and apparent T1 relaxation time) contrasts. These multi-contrast MRI templates will aid in anatomical structure delineation and parcellation by improving contrast from all tissue types. Acquiring data from many MRI sequences is not always feasible in non-human primates. Constraints imposed by anesthesia and animal health limit the available measurement time. Similar limitations exist for data acquisition in human patients. As shown in this thesis, a single MRI contrast is, however, insufficient to generate satisfactory contrasts from all types of brain tissue. In my fourth study (Chapter 4), I developed a strategy for obtaining a good cortical, subcortical, and white matter contrast by using only two (T1-weighted and QSM) MRI contrasts. The developed weighted linear fusion of QSM and T1-weighted images (TQ-SILiCON) significantly improved the visualization and segmentation of gray-to-white matter in the macaque brain. I also demonstrated that the TQ-SILiCON approach works equally well for humans and improves human brain tissue segmentation. Furthermore, TQ-SILiCON required data sets could be obtained using clinically available MRI systems and in a reasonably short measurement time. In my fifth and final study (Chapter 5), I employed the developed methods and pipelines to explore healthy brain aging in two NHP species, the cynomolgus macaque and the marmoset monkey. These species are increasingly utilized in preclinical studies to test novel treatment approaches to slow down aging and age-related decline of cognitive abilities. However, there is still insufficient knowledge about how well non-human primate models resemble the process of aging in humans. Utilizing the multi-contrast approach, I discovered an age-related increase of QSM and R2* in subcortical structures in both macaque and marmoset. This is most likely due to an accumulation of iron, as preliminary histological analyses of the species studied by us suggested this, and it is very much in line with previous reports on humans. Future studies involving advanced histological analyses will be performed to validate the proposed multi-contrast approach.de
dc.contributor.coRefereeNave, Klaus-Armin Prof. Dr.
dc.subject.engBrainde
dc.subject.engMRIde
dc.subject.engMyelinde
dc.subject.engIronde
dc.subject.engHumande
dc.subject.engMacaquede
dc.subject.engMarmosetde
dc.subject.engQSMde
dc.subject.engDiffusionde
dc.identifier.urnurn:nbn:de:gbv:7-ediss-14262-2
dc.affiliation.instituteGöttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB)de
dc.subject.gokfullBiologie (PPN619462639)de
dc.description.embargoed2022-12-13de
dc.identifier.ppn181774111X
dc.identifier.orcid0000-0003-3091-2580de
dc.notes.confirmationsentConfirmation sent 2022-09-27T14:15:01de


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