Diffusion magnetic resonance imaging (dMRI) allows us to noninvasively investigate the microstructural properties of brain tissue, and reconstruct the axonal pathways that connect distant brain regions. This enables us to infer the biological processes that give rise to thought and consciousness. However, despite significant advances in both imaging technology and computing power, our ability to estimate connectivity in a single subject using dMRI data remains quite limited. Barriers to accurate single subject estimates include poor accuracy and reproducibility of both fiber tracking and diffusion modeling results, and a difficulty in reproducing the methods of other researchers in this field. As a result, studies using different dMRI methods have drawn conflicting conclusions about the same biological systems. To overcome these barriers, I first present a technique to estimate the noise in dMRI data and show that this measure is a strong indicator of the reproducibility of dMRI measurements. Software engineering principles, such as modularization and thorough testing, were implemented and made publicly available in an open source library called Dipy. By providing a single platform where tools and methods from different developers can be implemented using shared constructs and made publicly available to users, Dipy aims to help the community more easily reproduce the findings of other researchers. In the last section of this work, I use these modeling and fiber tracking tools to reconstruct whole brain networks for individual subjects in a large population. The white matter tissue properties projected onto these networks show that regional differences in white matter integrity are strongly associated with body mass index in young, healthy individuals. This association helps explain the reduced cognitive ability in individuals with higher BMI. This study demonstrates the power of using single subject connectivity networks when studying the human brain and its role in health outcomes. In order to fully unlock the potential of dMRI imaging, methods development needs to continue to focus on improving the reproducibility and accuracy of dMRI studies.
During the last few decades, researchers have struggled to find reliable biomarkers for multiple sclerosis (MS) that could aid in diagnosis, measurement of disease progression, evaluation of treatments in clinical trials, and prediction of treatment effect. Traditional metrics, such as brain and lesion volumes, are poor contenders since they do not reliably reflect clinical metrics. Until recently, spinal cord metrics were also poor contenders, due to the quality limitations of spinal cord imaging. However, with recent technological advances, we are now able to acquire better quality spinal cord images and capture these metrics more accurately. This thesis investigates the potential of using spinal cord images clinically in MS through four different studies. The first study investigates different spinal cord metrics and shows how spinal cord PSIR gradient independently predicts EDSS in RRMS patients. The second study demonstrates two different methods for how spinal cord gray matter can be automatically segmented so that metrics can be easily obtained in a clinical setting. The third study is an investigation of how spinal cord metrics change longitudinally. The fourth study is a voxel-wise analysis of spinal cord metrics that shows local patterns of intensity, gradient, and deformation.
Neurosurgery and Disconnection Syndrome research have a symbiotic relationship. The human brain is a staggeringly complex system, unique to each individual. Even at birth there is already incredible diversity to this network, upon which we add a lifetime of experiences, influencing our brain structure and function by the way we use it. One of the best ways to study such a variable and complex system is to see what happens when it is perturbed. Neurosurgical intervention presents a rare opportunity to interact with the human brain in a controlled environment and see what happens when transient or permanent interference occurs. In return, the lessons learned about the relationship between brain structure and function can guide surgical intervention to minimize the risk of surgical injury causing permanent functional deficits. The risk a person is willing to take on to a functional system is a very personal decision; to some people, motor or language function may be what makes life worth living and others are willing to risk deficits to treat a pathology more aggressively. Understanding what damage patterns result in deficits is key to empowering the patient to make these decisions.
The brain's white matter connections can be modeled with Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) Fiber Tracking (also called tractography), a process by which water diffusion is used to deduce pathways of axon bundles. Neurosurgical applications present particular engineering challenges due to a variety of factors influenced by both the pathology and intervention. This thesis details several tools developed to address these challenges including methods to quality-control tractography streamline datasets, a processing pipeline to model disconnections caused by surgical intervention, a method to translate tractography information to a format tractable for integration with radiation therapy planning, and a pipeline relating electrode stimulation to white matter connectivity. All of the code is open-source so that researchers can use these tools to conduct their own studies.
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