Medicine and Health Sciences Commons^™

Interactive Visualization Of Multimodal Brain Connectivity: Applications In Clinical And Cognitive Neuroscience, Saeed Mahdizadeh Bakhshmand

Electronic Thesis and Dissertation Repository

Magnetic resonance imaging (MRI) has become a readily available prognostic and diagnostic method, providing invaluable information for the clinical treatment of neurological diseases. Multimodal neuroimaging allows integration of complementary data from various aspects such as functional and anatomical properties; thus, it has the potential to overcome the limitations of each individual modality. Specifically, functional and diffusion MRI are two non-invasive neuroimaging techniques customized to capture brain activity and microstructural properties, respectively. Data from these two modalities is inherently complex, and interactive visualization can assist with data comprehension.

The current thesis presents the design, development, and validation of visualization and computation …

Effect Of Acute Administration Of Angiopoietin-1 In Experimental Traumatic Spinal Cord Injury: Magnetic Resonance Imaging And Neurobehavioral Studies, Chirag B. Patel

Dissertations & Theses (Open Access)

Spinal cord injury (SCI) is a devastating condition that affects people in the prime of their lives. A myriad of vascular events occur after SCI, each of which contributes to the evolving pathology. The primary trauma causes mechanical damage to blood vessels, resulting in hemorrhage. The blood-spinal cord barrier (BSCB), a neurovascular unit that limits passage of most agents from systemic circulation to the central nervous system, breaks down, resulting in inflammation, scar formation, and other sequelae. Protracted BSCB disruption may exacerbate cellular injury and hinder neurobehavioral recovery in SCI.

In these studies, angiopoietin-1 (Ang1), an agent known to reduce …

Prediction Of Brain Tumor Progression Using A Machine Learning Technique, Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. Mckenzie, Jihong Wang, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.)

Electrical & Computer Engineering Faculty Publications

A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to …