Spatiotemporal mapping of brain atrophy in mouse models of Huntington's disease using longitudinal in vivo magnetic resonance imaging

Abstract

Mouse models of Huntington's disease (HD), that recapitulate some of the phenotypic features of human HD, play a crucial role in investigating disease mechanisms and testing potential therapeutic approaches. Longitudinal studies of these models can yield valuable insights into the temporal course of disease progression and the effect of drug treatments on the progressive phenotypes. Atrophy of the brain, particularly the striatum, is a characteristic phenotype of human HD, is known to begin long before the onset of motor symptoms, and correlates strongly with clinical features. Elucidating the spatial and temporal patterns of atrophy in HD mouse models is important to characterize the phenotypes of these models, as well as evaluate the effects of neuroprotective treatments at specific time frames during disease progression. In this study, three dimensional in vivo magnetic resonance imaging (MRI) and automated longitudinal deformation-based morphological analysis was used to elucidate the spatial and temporal patterns of brain atrophy in the R6/2 and N171-82Q mouse models of HD. Using an established MRI-based brain atlas and mixed-effects modeling of deformation-based metrics, we report the rates of progression and region-specificity of brain atrophy in the two models. Further, the longitudinal analysis approach was used to evaluate the effects of sertraline and coenzyme Q10 (CoQ10) treatments on progressive atrophy in the N171-82Q model. Sertraline treatment resulted in significant slowing of atrophy, especially in the striatum and frontal cortex regions, while no significant effects of CoQ10 treatment were observed. Progressive cortical and striatal atrophy in the N171-82Q mice showed significant positive correlations with measured functional deficits. The findings of this report can be used for future testing and comparison of potential therapeutics in mouse models of HD.

Correlations of Behavioral Deficits with Brain Pathology Assessed through Longitudinal MRI and Histopathology in the R6/2 Mouse Model of HD

“…In contrast, as previously reported [26], cortical and whole brain volume changes correlated with changes in motor performance. Longitudinal changes in cortical volume precede structural differences in striatum in R6/2, consistent with previous reports [25], [26]. These early cortical changes can potentially be explained by neuronal aggregates that appear first and accumulate faster in cortical regions as compared to the striatum of R6/2 mice [32], [46].…”

“…These mice develop age-related deficits in motor coordination, locomotor activity, anxiolytic behavior and impaired cognition [17], [18], [19], [20]. A progressive regional brain atrophy is evident and can be assessed non-invasively using magnetic resonance imaging (MRI) [21], [22], [23], [24], [25], [26]. Although individual features of the R6/2 mouse model have been extensively characterized, a longitudinal assessment of the emergence of behavioral dysfunction with concomitant measurement of brain atrophy by MRI, along with their correlation with cellular and molecular changes, is essential to establish how these features are mechanistically connected.…”

In vivo high-resolution diffusion tensor imaging of the mouse brain

“…MR-based mouse brain atlases are useful in analyzing MRI data acquired from mouse brains, for example, automated structural segmentation and volume measurements (Ali et al, 2005; Badea et al, 2007; Bock et al, 2006; Zhang et al, 2010). They can also be used as templates to perform voxel-based analysis to examine changes in structural morphology and tissue properties (Aggarwal et al, 2012; Lau et al, 2008; Lerch et al, 2008; Sawiak et al, 2009; Tyszka et al, 2006). Many of these MR-based atlases, especially DTI based mouse brain atlases, were previously generated from post-mortem brain specimens in order to achieve high spatial resolution and image quality.…”

Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning

“…The present study focused on inferring maps of key cellular structures in the mouse brain from MRI signals. Previous works on this problem include: new MRI contrasts that capture specific aspects of cellular structures of interest [21][22][23][24] ; carefully constructed tissue models for MR signals [25][26][27][28] ; statistical methods to extract relevant information from multi-contrast MRI 8 ; and techniques to register histology and MRI data [29][30][31] to produce ground truth for validation [32][33][34] . Here, we built on these efforts by demonstrating that deep learning networks trained by co-registered histological and MRI data can improve our ability to detect target cellular structures.…”