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Home arrow Computer Science arrow Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016

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Introduction

Diffusion MRI (dMRI) allows us to non-invasively study microstructural changes caused by neuropathology. Among these pathologies, gaining understanding of Alzheimer’s disease (AD) is of particular importance, affecting over one in nine people age 65 and above in theU.S. alone [1]. Traditionally, dMRI studies have used Diffusion Tensor Imaging (DTI) [2] to model the grey and white matter structure abnormalities in AD patients. Only recently, more complex white matter models like Neurite Orientation Dispersion and Density Imaging (NODDI) [3] have been explored to classify AD, and have shown greater discriminative power than DTI [4]. This reinforces the importance of exploring white matter models that provide more detailed microstructural information than DTI.

In human studies, it is hard to relate dMRI derived metrics to corresponding microstructural changes for lack of histological validation. As a solution, animal models provide a way to gain understanding on the underlying pathophysiology of AD by allowing dMRI in addition to histological measurements. Mouse models of human tauopathy (rTg4510) have been previously studied at various time points using DTI [5, 6], and at a single time point comparing DTI with NODDI metrics [7]. In this latter study, NODDI derived metrics once again appeared more discriminative than those derived from DTI. Further efforts focusing on multishell dMRI analysis of transgenic Alzheimer rats (TgF344-AD) have shown that dMRI measurements at higher gradient strengths aid the classification of AD-like pathology [8]. However, only anisotropy measures of DTI and hybrid diffusion imaging (HYDI) [9] were explored.

In this study, we compare the evolution of dMRI-derived markers from different white matter models as progressive neurodegeneration occurs in transgenic Alzheimer rats (TgF344-AD). In particular, we study the patterns of alteration across three time points in the hippocampus, cingulate cortex and corpus callosum— areas known to be affected in AD. The two grey matter areas were previously shown to manifest age-dependent cerebral amyloidosis that precedes tauopathy, gliosis and apoptotic loss of neurons [10], making these cortical regions extremely relevant for understanding the underlying mechanisms in AD. We compare biomarkers derived from DTI, NODDI and Mean Apparent Propagator (MAP)-MRI [11] using multishell data. To the best of our knowledge, this is the first study that investigates multi-shell biomarkers at different time points in AD animal models.

The paper is structured as follows: we first describe the diffusion MRI data and the metrics we derive in Sect. 2. We provide the results in Sect. 3 and discuss them in Sect. 4. We finally provide our conclusions in Sect. 5.

 
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