Alzheimer's disease (AD) is one of the top 10 leading causes of death in the US; it debilitates memory and impairs cognition. The current core clinical criteria for diagnosis of AD are based on functional deficits and cognitive impairments that do not include the advanced imaging techniques or cerebrospinal fluid analysis; the final confirmation of the disease is only possible at the time of autopsy when neurofibrillary tangles and beta-amyloids are present in a brain tissue examination. The distributions of these particular pathogens (neurofibrillary tangles and beta-amyloids) follow a pattern that is useful to identify different stages of AD at the time of the autopsy by looking at the presence of pathogens in the areas of the brain. The pathogens are first seen in entorhinal/perirhinal cortex, and then spread to hippocampus cornu ammonis subfields, followed by association cortex and finally the rest of the brain. This disease progression is standard and described in NIA-RI guidelines. In the last decades, with the introduction of advanced imaging techniques in research settings, many in vivo based research methods have been focusing on the volumetric measurements of the hippocampus and its subfields in MRI images and using them as additional information for early diagnosis of AD. While the hippocampal volume provides excellent diagnostic aid, it doesn't address both the pathogens associated with AD and the progression of the pathogens within the different subregions of the hippocampus. The hippocampus formation is a complex circuit that spans the temporal lobes and found to have distinctive subregions. These subregions are subject to different influence by AD at different stages. Since the disease progression as seen in pathogen distributions follows a pattern, studying the pattern of the regional changes will allow us to predict which stage the disease is at. These pathological shifts in regions of the brain are studied extensively in ex vivo MRI as well as during autopsy but not in in vivo MRI. Considering that the brain areas with neurofibrillary tangles and beta-amyloids show hypointensity (PD-weighted) and hyperintensity voxels (T2-weighted) in the MRI images, we suggest an in vivo study using normalized MRI images taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We analyze the pattern of changes in the hippocampal region using a volume-based method in normalized T1-weighted MRI and an intensity-based analysis in normalized PD-weighted MRI. We use the volume-based method to calculate the changes in the volume of each of the hippocampus subfields. For the intensity-based method, we count the number of hypointensity (intensity value < 125) voxel combinations from the Gray-Level Co-occurrence Matrix (GLCM); a normalized MRI images are used inorder to minimize intensity variation. We then use the data (volume and intensity changes) to construct decision trees which classify the MRI images into three categories: normal control (NC), mild cognitive impairment (MCI) and AD. We have found that the volume-based decision trees detect AD MRI images with an accuracy of 75 % but failed to detect NC and MCI MRI images with the same level of accuracy. Whereas, with the intensity-based decision trees, we were able to classify MRI images into NC, MCI and AD categories each with an equally high level of accuracy (above 86 %). To find out how reliable the intensity-based method is in classifying MRI images, we introduced noises to our images. The addition of noises forced some adjustments in our decision trees. The accuracy of decision tree classification decreased in the presence of noises. However, even in the presence of the additional noises, we noticed that the intensity-based method outperforms volume-based method. The classification of MRI images improves when both measures (intensity-based and volume-based) are used in constructing our decision trees. This study has demonstrated that the inclusion of the intensity measurements of PD-weighted MRI images in AD studies may provide a more accurate way to model the natural progression of AD in vivo and contribute to the early diagnosis of AD. / A Dissertation submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2017. / April 7, 2017. / ADNI, Alzheimer Disease, GLCM, Hippocampus Subfield, Intensity, Multimodel / Includes bibliographical references. / Xiuwen Liu, Professor Directing Dissertation; Samuel Grant, University Representative; Gary Tyson, Committee Member; Piyush Kumar, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_513755 |
| Contributors | Kanel, Prabesh (authoraut), Liu, Xiuwen, 1966- (professor directing dissertation), Grant, Samuel C. (university representative), Tyson, Gary Scott (committee member), Kumar, Piyush (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Computer Science (degree granting departmentdgg) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (108 pages), computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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