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Clinical and Animal Research on Alzheiner’s Disease by Using Quantitative Whole-brain Structural MRI and DTI Analysis

Author: ZuoZuoZuo
Tutor: ZhuWenZhen
School: Huazhong University of Science and Technology
Course: Medical Imaging and Nuclear Medicine
Keywords: CNS MR-Diffusion Brain Animal models DementiaAlzheimer’s disease mild cognitive impairment diffusion tensor imagingatlas magnetic resonance imaging principal component analysis lineardiscriminant analysis Alzheimer’s disease Huntington’s disease primary progressiveaphasia spinocerebellar ataxia
CLC: R445.2
Type: PhD thesis
Year: 2013
Downloads: 6
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Part I In vivo Quantitative whole-brain Diffusion Tensor Imaging analysis of APP/PS1transgenic mice using Voxel-based and Atlas-based methodsPurpose Diffusion tensor imaging (DTI) has been applied to characterize the pathological features of Alzheimer’s disease (AD) in a mouse model, although little is known about the structural specificity. Voxel-based analysis (VBA) and atlas-based analysis (ABA) are good complementary tools for whole-brain DTI analysis. The purpose of this study is to identify the spatial localization of disease-related pathology of AD mouse model.Materials and Methods VBA and ABA quantification were used for the whole-brain DTI analysis of nine APP/PS1mice and wild-type (WT) controls. Multiple scalar measurements, including fractional anisotropy (FA), trace, axial diffusivity (DA), and radial diffusivity (DR), were investigated to capture the various types of pathology. The accuracy of the image transformation applied for VBA and ABA was evaluated by comparing manual and atlas-based structure delineation using kappa statistics. Following the MR examination the brains of the animals were analyzed for microscopy.Results Extensive anatomical alterations in APP/PS1mice, including in both gray matter areas (neocortex, hippocampus, caudate putamen, thalamus, hypothalamus, claustrum, amygdala, and piriform cortex) and white matter areas (corpus callosum/external capsule, cingulum, septum, internal capsule, fimbria, and optic tract) have been identified by an increase in FA or DA, or both, compared to WT (p<0.05, corrected). The average kappa value between manual and atlas-based structure delineation was approximately0.8, and there was no significant difference between APP/PS1and WT mice (p>0.05). The histopathological changes in the gray matter areas were confirmed by microscopy studies. DTI did, however, demonstrate significant changes in white matter areas, where the difference was not apparent by qualitative observation of a single-slice histological specimen.Conclusion This study demonstrated the structure specificity of pathological changes in APP/PS1mouse model, and also showed the feasibility of applying whole-brain analysis methods for the investigation of AD mouse model. Part II The Pattern of White Matter changes among Alzheimer’s Disease, Mild Cognitive Impairment and Healthy peoplePurpose Increasing evidence has demonstrated that white matter(WM) changes among Alzheimer’s disease (AD), mild cognitive impairment (MCI) and healthy people are signficantly different. However, the pattern of WM changes are still under debate. The purpose of this study is to identify the spatial pattern of WM alterations among AD, MCI and healthy people, and to find reliable biomarkers for the early diagnosis and monitoring of the disease.Materials and Methods Twenty-one patients diagnosed as probable AD according to NINCDS-ADRDA(M/F=11/10, mean age66.8yrs.),8patients diagnosed as MCI according to Petersen’s criteria (M/F=3/5, mean age64.4yrs.) and15healgy people (M/F=8/7, mean age65.3yrs.) were enrolled in this study. All subjects underwent diffusion tensor imaging (DTI) on a3.0T MR system with TR/TE of10000/83ms, FA of90°, matrix of256×256, FOV of240mm×240mm, Phase FOV of1, slice thickness of3.0mm with no space, NEX of1, total slice of42, b value of1000s/mm2along30directions. All the raw data was processed by using DTI studio software to get the fractional anisotropy (FA) images. Then atlas-based analysis (ABA) were used for whole-brain DTI anlysis including58deep gray matter (GM) and deep WM structures. The differences of FA value among AD, MCI and healthy people were compared by using ANOVA, with a post-hoc analysis. The correlation between FA value and MMSE scores were further investigated in the regions where significant differences were found. Results Compared with healthy controls, AD patients demonstrated wide-spread FA decrease in deep GM and deep WM structures (p<0.05, FDR corrected). Among all the structures, the FA value of the splenium of corpus callosum (SCC) and the thalamus were signficantly different between the MCI group and the healthy group (p<0.05, FDR corrected), but not between the AD group and the MCI group (p>0.05); the FA value of the cingulum and the superior longitudinal fasciculus (SLF) were significantly different between the AD group and the MCI group (p<0.05, FDR corrected), but not between the MCI group and the healthy group (p>0.05). The mean FA value of the cingulum and the SLF were positively correlated with MMSE scores, with the highest correlation coefficient in the right cingulum (r=0.606, p=0.001). No significant correlation was found between the FA value of SCC and MMSE score (p>0.05), or the thalamus and MMSE score (p>0.05).Conclusion The spatial pattern of WM alterations among AD, MCI and healthy people are significantly different. The microstructure changes in the SCC and the thalamus are early events, but have no significant correaltion with the cognition impairment. The WM disruption in the cingulum and the SLF are in correlation with cognition decline, suggesting that FA values in these areas could be used as a sentive biomarker for monitoring disease progression. Part Ⅲ Gap between an Atlas and a Target Image Analysis (GAIA):Use of a Degree of Local Atlas-Image Segmentation Disagreement to Capture the Features of Anatomic Brain MRIPurpose To develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gap between an Atlas and a target Image Analysis (GAIA), in which a degree of local atlas-image segmentation disagreement was used to capture the anatomical features of target images.Materials and Methods As a proof-of-concept, the GAIA was applied to a training dataset for pattern recognition of the neuroanatomical features of Alzheimer’s disease, Huntington’s disease, spinocerebellar ataxia type6, and four subtypes of primary progressive aphasia. These feature vectors were applied to the test dataset to evaluate the accuracy of the pattern recognition.Results The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified.Conclusion The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which will enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

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CLC: > Medicine, health > Clinical > Diagnostics > Diagnostic Imaging > Magnetic resonance imaging
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