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Thesis (Ph. D. in Biomedical Engineering)--Vanderbilt University, Dec. 2006. / Title from title screen. Includes bibliographical references.

abstract: The rapid development in acquiring multimodal neuroimaging data provides opportunities to systematically characterize human brain structures and functions. For example, in the brain magnetic resonance imaging (MRI), a typical non-invasive imaging technique, different acquisition sequences (modalities) lead to the different descriptions of brain functional activities, or anatomical biomarkers. Nowadays, in addition to the traditional voxel-level analysis of images, there is a trend to process and investigate the cross-modality relationship in a high dimensional level of images, e.g. surfaces and networks. In this study, I aim to achieve multimodal brain image fusion by referring to some intrinsic properties of data, e.g. geometry of embedding structures where the commonly used image features reside. Since the image features investigated in this study share an identical embedding space, i.e. either defined on a brain surface or brain atlas, where a graph structure is easy to define, it is straightforward to consider the mathematically meaningful properties of the shared structures from the geometry perspective. I first introduce the background of multimodal fusion of brain image data and insights of geometric properties playing a potential role to link different modalities. Then, several proposed computational frameworks either using the solid and efficient geometric algorithms or current geometric deep learning models are be fully discussed. I show how these designed frameworks deal with distinct geometric properties respectively, and their applications in the real healthcare scenarios, e.g. to enhanced detections of fetal brain diseases or abnormal brain development. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2020

This dissertation presents a knowledge-guided expert system that is capable of applying routinesfor multispectral analysis, (un)supervised clustering, and basic image processing to automatically detect and segment brain tissue abnormalities, and then label glioblastoma-multiforme brain tumors in magnetic resonance volumes of the human brain. The magnetic resonance images used here consist of three feature images (T1-weighted, proton density, T2-weighted) and the system is designed to be independent of a particular scanning protocol. Separate, but contiguous 2D slices in the transaxial plane form a brain volume. This allows complete tumor volumes to be measured and if repeat scans are taken over time, the system may be used to monitor tumor response to past treatments and aid in the planning of future treatment. Furthermore, once processing begins, the system is completely unsupervised, thus avoiding the problems of human variability found in supervised segmentation efforts. Each slice is initially segmented by an unsupervised fuzzy c-means algorithm. The segmented image, along with its respective cluster centers, is then analyzed by a rule-based expert system which iteratively locates tissues of interest based on the hierarchy of cluster centers in feature space. Model-based recognition techniques analyze tissues of interest by searching for expected characteristics and comparing those found with previously defined qualitative models. Normal/abnormal classification is performed through a default reasoning method: if a significant model deviation is found, the slice is considered abnormal. Otherwise, the slice is considered normal. Tumor segmentation in abnormal slices begins with multispectral histogram analysis and thresholding to separate suspected tumor from the rest of the intra-cranial region. The tumor is then refined with a variant of seed growing, followed by spatial component analysis and a final thresholding step to remove non-tumor pixels. The knowledge used in this system was extracted from general principles of magnetic resonance imaging, the distributions of individual voxels and cluster centers in feature space, and anatomical information. Knowledge is used both for single slice processing and information propagation between slices. A standard rule-based expert system shell (CLIPS) was modified to include the multispectral analysis, clustering, and image processing tools. A total of sixty-three volume data sets from eight patients and seventeen volunteers (four with and thirteen without gadolinium enhancement) were acquired from a single magnetic resonance imaging system with slightly varying scanning protocols were available for processing. All volumes were processed for normal/abnormal classification. Tumor segmentation was performed on the abnormal slices and the results were compared with a radiologist-labeled ��ground truth' tumor volume and tumor segmentations created by applying supervised k-nearest neighbors, a partially supervised variant of the fuzzy c-means clustering algorithm, and a commercially available seed growing package. The results of the developed automatic system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

This doctoral thesis details a series of projects at the border of chemistry and neuroscience leading to the development of a novel family of probes which chemically target specific cells and molecules in the brain. Chapter 1 concisely introduces the history, development and applications of probes for monitoring brain activity and highlights synthetic voltage sensitive dyes as probes which have not yet reached their full potential, partly due to the lack of targeting strategies in brain tissue. Chapter 2 details the development of a new class of polymer-supported probes for ligand-directed delivery of fluorescent voltage sensitive dyes to monoaminergic neurons in live brain tissue. The polysaccharide dextran equipped with dichloropane as a ligand and either an electrochromic or PeT-based voltage sensor selectively targets dopaminergic and noradrenergic axons in mouse brain slice preparations. The new probes enabled voltage imaging in a defined neuronal population without the use of genetic manipulation. All following chapters describe modification of one of the components of the targeting platform developed in Chapter 2 aiming to optimize its performance or broaden its application potential. Chapter 3 extends the developed polymer platform to the targeting of a different molecular target – the AMPA-type glutamate receptor – via a ligand-directed covalent labeling strategy. Chapter 4 examines PEG as an alternative polymer carrier and shows that while dextran is more universal as a carrier, PEG provides superior targeting selectivity with negatively charged PeT-based voltage sensors. A series of targetable probes with improved voltage sensitivity based on the PEG platform is introduced here as well. Chapter 5 describes the synthesis of targetable probes carrying voltage sensors for imaging modalities other than visible light fluorescence, specifically for short wave infrared (SWIR) fluorescence and photoacoustic (PA) imaging. Chapter 6 shows the first steps towards adapting the delivery platform to the development of dual-ligand drugs for cell-selective pharmacology in the brain.

NR 20140805

Sodium (23Na) plays a critical role in all organisms – it is crucial in cellular homeostasis, pH regulation and action potential propagation in muscle and neuronal fibres. Healthy cells have a low intracellular 23Na and high extracellular concentration, with the sodium-potassium pump maintaining this sodium gradient. In the human brain approximately 50% of its total energy consumption is occupied by maintenance of this gradient, demonstrating the pump’s importance in health. A failure of the sodium-potassium pump leads to cellular apoptosis and ultimately necrosis, with potentially disastrous results for neurological function. Magnetic resonance imaging (MRI) of 23Na is of great interest because of the ubiquity of sodium in cellular processes. However, it is hampered by many technical challenges. Among these are a low gyromagnetic ratio, short T2∗ relaxation times, and low concentrations all of which lead to long acquisitions in order to account for the poor inherent signal. In addition, 23Na MRI requires specialized hardware, non-standard pulse sequences and reconstruction methods in order to create images. These have all contributed to render clinical applications for 23Na MRI virtually non-existent, despite research indicating sodium’s role in various neurological disorders, including multiple sclerosis, Alzheimer’s, stroke, cancer, and traumatic brain injury. This work is motivated by a desire to use 23Na MRI in clinical settings. To that end, hardware and software methods were initially developed to process sodium images. In order to quantify the imaging system the point-spread function (PSF) and the related modulation transfer function (MTF) were calculated with the aid of a 3D-printed resolution phantom with different 23Na concentrations in gelatin. Two pulse sequences, density-adapted projection reconstruction (DA-3DPR) and Fermat looped orthogonally encoded trajectories (FLORET), with similar acquisition times were tested. Reconstructions were performed with the non-uniform fast Fourier transform. Results indicated a full-width, half-maximum (FWHM) value of 1.8 for DA-3DPR and 2.3 for FLORET. In a follow-up study, simulation experiments were added to various sodium phantom concentrations in 3% agar. The simulations indicated high potential variability in the MTF calculations depending on the methodology, while the phantom experiments found a FWHMs of 2.0 (DA-3DPR), and 2.5 (FLORET). Diffusion tensor imaging (DTI) is an MRI technique with wide adoption for the assessment of a variety of neurological disorders. Combining DTI with 23Na MRI could provide novel insight into brain pathology; however, a study with a healthy population is warranted before examinations with other populations. Fifteen subjects were scanned with DTI and sodium MRI, and the latter was used to derive voxel-wise tissue sodium concentration (TSC). Regional grey and white matter (WM) TSC was analyzed and compared to fractional anisotropy (FA) and cerebrospinal fluid (CSF) proximity. Results indicated that WM voxels proximal to CSF regions (i.e. corpus callosum) could have lower than expected FA values and higher measured TSC, with an inverse correlation between TSC and distance to CSF. This is likely the result of the broad PSF of 23Na MRI, as regions distal to CSF did not exhibit this phenomenon. This potentially represents a confounding effect when interpreting sodium concentrations, especially in regions proximal to the high 23Na content in CSF. / Thesis / Doctor of Philosophy (PhD)

published_or_final_version / Diagnostic Radiology / Doctoral / Doctor of Philosophy

Functional brain changes occur rapidly by alterations in synaptic activity, or more slowly, typified by changes in synaptic density and functional neurochemistry. Functional MRI has focused more on the prior than the latter, even though slow brain changes are important for normal brain function and for many brain disorders. With this in mind, I have adapted and optimized MRI-based tools in mice designed to measure ‘slow functional’ changes in the brain - slow changes in linked to synaptic density or slow changes in functional neurochemistry. First, I developed and optimized a series of tools that can map cerebral blood volume (CBV) across the cortical mantle and within cortical layers. I show that this reflects the known functional architecture of the mouse brain and use a whisker-cutting paradigm to show that this approach is sensitive to slow changes in synaptic density. Second, I demonstrate the utility of this approach for mapping slow changes in the brain associated with disease, by pinpointing changes in synaptic density in a novel mouse model of Alzheimer’s disease. Third, I implemented and optimized in mice an MR spectroscopy technique designed to measure changes in two neurotransmitters, GABA, and glutamate. I then demonstrate the translational capabilities of this approach by identifying glutamate abnormalities in the brains of patients in the prodromal stages of schizophrenia.