Peripheral neuropathies are a set of common diseases that affect the peripheral nervous system, causing damage to vital connections between various parts of the body and the brain and spinal cord. Different clinical conditions are known to selectively impact various size nerve fibers, which often makes it difficult to diagnose which peripheral neuropathy a patient might have. The nerve conduction velocity diagnostic test provides clinically useful information in the diagnosis of some peripheral neuropathies. This method is advantageous because it tends to be minimally invasive yet it provides valuable diagnostic information. However, this test does not determine characteristics of peripheral nerve fiber size distributions, and therefore does not show any detailed information regarding the nerve fibers within the nerve trunk. Being able to determine which nerve fibers are contributing to the evoked potential within a nerve trunk could provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system, such as chronic inflammatory demyelinating polyneuropathy or early diabetic peripheral neuropathy. In this study, three rat sciatic nerves are sectioned and stained with hematoxylin and eosin in order to measure the nerve fiber diameters within the nerve trunk. Stained samples are viewed using brightfield microscopy and images are analyzed using ImageJ. Histograms were created to show the frequency of various nerve fiber diameters. The nerve fiber diameters measured during this research are consistent with the range of previously published diameter values and will be used to support continuing research for a novel method to characterize peripheral nerve fiber size distributions using group delay.

Alzheimer’s disease (AD) is a chronic and progressive, irreversible syndrome that deteriorates the cognitive functions. Official death certificates of 2013 reported 84,767 deaths from Alzheimer’s disease, making it the 6th leading cause of death in the United States. The rate of AD is estimated to double by 2050. The neurodegeneration of AD occurs decades before symptoms of dementia are evident. Therefore, having an efficient methodology for the early and proper diagnosis can lead to more effective treatments. Neuroimaging techniques such as magnetic resonance imaging (MRI) can detect changes in the brain of living subjects. Moreover, medical imaging techniques are the best diagnostic tools to determine brain atrophies; however, a significant limitation is the level of training, methodology, and experience of the diagnostician. Thus, Computer aided diagnosis (CAD) systems are part of a promising tool to help improve the diagnostic outcomes. No publications addressing the use of Feedforward Artificial Neural Networks (ANN), and MRI image attributes for the classification of AD were found. Consequently, the focus of this study is to investigate if the use of MRI images, specifically texture and frequency attributes along with a feedforward ANN model, can lead to the classification of individuals with AD. Moreover, this study compared the use of a single view versus a multi-view of MRI images and their performance. The frequency, texture, and MRI views in combination with the feedforward artificial neural network were tested to determine if they were comparable to the clinician’s performance. The clinician’s performances used were 78 percent accuracy, 87 percent sensitivity, 71 percent specificity, and 78 percent precision from a study with 1,073 individuals. The study found that the use of the Discrete Wavelet Transform (DWT) and Fourier Transform (FT) low frequency give comparable results to the clinicians; however, the FT outperformed the clinicians with an accuracy of 85 percent, precision of 87 percent, sensitivity of 90 percent and specificity of 75 percent. In the case of texture, a single texture feature, and the combination of two or more features gave results comparable to the clinicians. However, the Gray level co-occurrence matrix (GLCOM), which is the combination of texture features, was the highest performing texture method with 82 percent accuracy, 86 percent sensitivity, 76 percent specificity, and 86 percent precision. Combination CII (energy and entropy) outperformed all other combinations with 78 percent accuracy, 88 percent sensitivity, 72 percent specificity, and 78 percent precision. Additionally, a combination of views can increase performance for certain texture attributes; however, the axial view outperformed the sagittal and coronal views in the case of frequency attributes. In conclusion, this study found that both texture and frequency characteristics in combinations with a feedforward backpropagation neural network can perform at the level of the clinician and even higher depending on the attribute and the view or combination of views used.

<p>Small nematode model organisms such as <em>Caenorhabditis elegans</em> are widely used in the fields of neurobiology, toxicology, drug discovery, etc. They are advantageous due to their fully characterized genomic and cellular system. Traditional screening methods involve the exposure of animals to chemicals/drugs inside multiwell-plates while its effects on growth, movement and other cellular/sub-cellular processes are monitored by visual inspection. Yet, these methods are time-consuming, low-throughput, expensive, tedious, difficult to control, hard to modulate instantaneously, prone to subjectivity and not suitable for movement-based behavioural assays. Hence, a method to induce and to quantify movement on-demand in a rapid, sensitive, precise and reversible manner would greatly facilitate biological studies. In this thesis, microfluidic engineering approaches have been utilized in nematode-based assays due to their potential to obtain high precision measurements in a low-cost, rapid and automated manner. Movement response of worms to a diverse range of electric signals has been quantitatively characterized. DC and pulse-DC electric fields have been shown to stimulate worms’ swimming towards the negative electrode inside a microchannel (electrotaxis). AC electric fields were used to inhibit movement on-demand. Animals’ movement has been characterized in terms of speed and range of motion, body-bend frequency and turning time. Electrotaxis was shown to be mediated by neuronal activities and correlations between animal’s behaviour and neuronal signalling has also been demonstrated. Using this basic understanding, multiple microfluidic components such as position sensors and electric immobilizers have been developed. Electrotaxis has then been applied as a technique to sort worms in accordance to their size/age and phenotype as well as to perform drug screening at a single-animal level. Integration of the techniques and components developed during this research is expected to have a significant impact on the development of an integrated microfluidic platform for high throughput automated behavioural screening of nematodes with applications in drug discovery, toxicology, neurobiology and genetics.</p> / Doctor of Philosophy (PhD)