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Magnetic Resonance Imaging and Histology Parameters

Non-invasive neuro-imaging-based technologies such as Positron Emission Tomography (PET) scan and Magnetic Resonance Imaging (MRI), if adequately validated, hold the most promise for adoption in both diagnosis and clinical follow-up of disease progression in AD (Dickerson and Sperling, 2005). Using neuro-imaging, differences in brain anatomy, chemistry, and physiology can be detected via the measured MRI parameters. Additionally, longitudinal MRI studies enable the assessment of neuro-anatomical changes as the animal ages. MRI technology is highly advanced with different scanning technologies resulting in different measures. Diffusion Tensor Imaging (DTI) has been shown to characterize AD progression in white matter (Alexander et al., 2007; Klohs et al., 2013), and DTI quantifies the diffusivity of water molecules in the brain microstructure, which is hypothesized to follow a Gaussian distribution. Diffusion Kurtosis Imaging (DKI) aims at simultaneously quantifying both the Gaussian (diffusion tensor) and non-Gaussian (diffusion kurtosis) behavior of water. Several studies have reported the superiority of DKI over DTI in detecting AD pathology in both white and gray matter (Hui et al., 2008; Cheung et al., 2009; Veraart et al., 2011).

The degree of neuronal myelination was determined by immunohistochem- ical visualization of myelin basic protein (MBP), the major protein component of myelin sheaths. In addition, Glial Fibrillary Acidic Protein (GFAP) and Ionizing Calcium-Binding Adaptor molecule 1 (IBA-1) were used as markers for astrocytes and microglia, respectively. Finally, 4G8 labeling was performed to detect amyloid-beta in the brains of APP/PS1 transgenic mice.

In this chapter, we apply the methodology presented in Chapter 4 to evaluate the potential of MRI parameters as biomarkers for histology features in different regions of the brain. Although, the experimental setting we discuss in this chapter is not the same as the clinical trial setting discussed in previous

TABLE 17.1

Summary of the data: Number of animals per age and genotype.

Age

2

4

6

8

10

Transgenic

10

10

9

9

3

Wildtype

2

2

2

2

2

chapters, we propose to use the surrogacy framework for the evaluation of MRI parameters as biomarkers for specific histology features, using a similar approach within the normal-normal surrogacy setting. After a description of the case study in Section 17.2, we present an elaborate discussion of the similarity to the surrogacy setting in Section 17.3. A two-stage modeling approach is presented in Section 17.4. Sections 17.5 and 17.6 are devoted to the application of the proposed methodology in the case study, while software issues are discussed in Sections 17.7 and 17.8.

 
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