Discussion

In this chapter, a structural sequence for in vivo mouse brain imaging has been developed. The sequence permits the acquisition of high resolution, isotropic voxels within a feasible in vivo imaging time of 1.5 hours. Scan parameters (TR, TE) were optimised for maximum contrast between the hippocampus and thalamus, as well as the cortex and corpus callosum. The first application of TBM to the rTg4510 mouse model of tauopathy is also presented, where marked morphological changes have been identified in this model. This work presents a platform for my subsequent investigations of longitudinal structural changes in the rTg4510 mouse that is presented in this thesis. Previous in vivo structural characterisation of AD mouse models has largely employed 2D sequences, with a range of spatial resolutions: 94 μm × 140 μm × 400 μm (171); 62.5 μm × 125 μm × 500 μm (172); 78 μm × 78 μm × 500 μm (173); 120 μm × 120 μm × 500 μm (174). In all these studies, the researchers have favoured high in-plane resolution over slice thickness. Analysis of these data sets has largely been restricted to manual segmentation of ROIs, in order to determine volumetric alterations. However, the thickness of the imaging slice may have introduced partial volume artefacts which would have led to inaccuracies in the volumetric results. When optimising the sequence for high resolution in vivo imaging, I selected a 3D sequence in order to minimise this effect.

All of the studies detailed above have employed a SE (T₂-weighted) rather than GE (T2*-weighted) sequence; this is likely to be due to the susceptibility artefacts which are frequently observed in in vivo mouse brain data, and can corrupt GE imaging data. Very few of these papers have reported SNR measurements for their imaging data; however, Natt et al. have previously reported that SNR of 15 – 20 was sufficient to unveil anatomical details in the mouse brain (175). In this work, my sequence returned an SNR of between 8 and 12 which appeared suitable for manual delineation of ROIs as well as voxel-wise analysis techniques.

Previously published work in the rTg4510 mouse has reported structural changes in the hippocampus and cerebral cortex, as well as enlargement of the ventricles using manual segmentation of in vivo MRI data sets (157). Using TBM, we were able to extend these observations and additionally detect local volume reductions in the cortex,

105

hippocampus,caudate putamen andolfactory bulbsas well as ventricular expansion. At this time point, the transgenic brains have undergone gross atrophy which is noticeable by eye in the in vivo structural images. I therefore anticipate that TBM will be most valuable in the detection of subtle structural abnormalities that occur at earlier stages oftauopathy that I investigate in the longitudinal study of the rTg4510 described in Chapter 4.

The hippocampal atrophy observed using TBM was validated by manual delineation of this region, where complete discrimination of the rTg4510 animals and wildtype controls was observed. However, this marked loss in hippocampal volume was not entirely encapsulated in the TBM findings, which only showed a handful of significant voxels in the caudal slices of the hippocampus. According to Lerch et al., image registration works best when the data to be matched is comparable (176); that is, when the phenotype to be explored is reasonably subtle. In this work, the transgenic animals had undergone gross atrophy of the forebrain regions, which may have introduced some difficulties when registering these data sets to the wildtype controls. This may have resulted in an underestimation of the hippocampal atrophy in the TBM findings. The negative correlation that we measured between hippocampal volume and NFT density provides evidence of a direct relationship between NFT pathology and atrophy, whilst highlighting the sensitivity of in vivo MRI to subtle volume changes. This correlation was not present in the cortex, despite higher NFT density. This may be because the hippocampus has a more rapid progression of tangle formation and thus the subsequent neurodegeneration is more advanced at 8–9 months of age.

Structural MRI has already established itself as a reliable biomarker in the clinical diagnosis of Alzheimer's disease (93) , where characteristic grey matter reduction and ventricular enlargement can be readily visualised using standard structural MRI sequences. Structural MRI has found additional clinical relevance in the differential diagnosis of AD over other forms of dementia (e.g. dementia with Lewy bodies) where neuropsychological testing alone may be insufficient (177, 178). Crucially, structural MRI may enable detection of AD prior to the onset of clinical symptoms, making it a valuable biomarker of the disease (98). It is currently employed within the ADNI study – a worldwide collaboration between research institutes, with the aim of understanding the physical brain changes that accompany the descent into dementia (179).

106

Advanced image processing techniques are routinely applied to large clinical AD cohort studies (179), to identify morphological differences in AD subjects. These techniques include voxel-wise analysis methods e.g. TBM, in addition to atlas-based segmentation techniques (180-182). The utility of these techniques has expedited high throughput analysis of large clinical data sets, such as those generated within the ADNI study, enabling automated and unbiased detection of morphometric changes occurring within AD subjects. These methods are being increasingly applied to structural data sets of the AD mouse brain.

Quantification of volumetric alterations in mouse models of AD has traditionally been performed using manual segmentation, which requires a trained user to delineate regions of a target structure in each image slice in order to generate a 3D volume (183, 184). Manual analysis methods suffer a number of disadvantages; they are prone to user-bias, are time-consuming, and require a priori hypotheses to inform the regional analysis. In order to overcome these limitations, the preclinical MRI community are rapidly adopting advanced image processing techniques to analyse mouse brain MRI data. In particular, atlas-based segmentation techniques have established themselves as robust alternatives to manual segmentation for regional analysis of AD mouse brains (157, 185). A number of published pipelines for automatic structural parcellation are now available (186, 187), enabling the automatic delineation of mouse brain regions. The application of these pipelines to mouse brain data sets has been facilitated by increasing availability of in vivo (188) and ex vivo (189-192) mouse brain atlases, to support atlas-based segmentation techniques.

Despite the growth of automated segmentation techniques to characterise structural mouse brain data sets, the translation of voxel-wise analysis methods has been somewhat slower to progress; however, they are gaining popularity due to their ability to detect discrete morphological changes which may be undetected by regional analysis. A number of rodent studies have employed voxel-wise analysis techniques to investigate a range of neurological disorders including: Huntington’s disease (154, 193, 194), AD (155, 195-197), Prader-Willi syndrome (198) and neurodevelopmental abnormalities (199-203). These techniques have demonstrated enhanced sensitivity to morphological disturbances over manual segmentation in mice, making them an attractive alternative for characterising a mouse model (204). However, the vast majority of these studies were performed using ex vivo mouse brain data, which involves perfuse-fixation of the

107

tissues before they are imaged at near-microscopic resolution (205). A contrast agent is frequently employed as a tissue-active stain, to highlight detailed anatomical structures (140). Although this produces high quality imaging data with exquisite soft tissue contrast, it sacrifices the ability to image the same animal longitudinally.

Only a handful of studies have successfully employed voxel-wise analysis techniques to study in vivo AD mouse brains (155, 197). This is surprising, given the significance of longitudinal data in characterising neurodegeneration and its modulation by emerging therapeutics. This may be due to inadequacies in the imaging data, as most in vivo imaging protocols employ multi-slice 2D sequences with high in-plane resolution at the expense of poor section thickness (175). This may render the data unsuitable for TBM analysis, where high resolution isotropic voxels are a requirement. Indeed, previous work by Teipel et al. employed structural MRI in conjunction with voxel-wise analysis techniques to characterise a mouse model of AD (206). In this work, no grey matter alterations were observed, despite histological observations of cerebral amyloidosis at this timepoint (207). Closer inspection of the imaging parameters employed by Teipel et al. revealed that a 2D sequence was employed; with high in-plane resolution (31.25μm) at the expense of poor slice thickness (600μm). This choice of parameters is likely to be non-optimal for voxel-wise analysis methods, and may have prohibited the detection of morphological changes.

In this work, I have optimised the sequence for high resolution in vivo mouse brain imaging, which fulfils the specific requirements for TBM analysis. The application of the optimised sequence to the rTg4510 mouse demonstrated its suitability to detect AD-related changes in this mouse.