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White matter changes caused by mild traumatic brain injury in mice
Lisa M Gazdzinski1, Miranda Mellerup1,2, Tong Wang1,2, John G Sled3,4, Brian J Nieman3,4, and Anne L Wheeler1,2

1Neurosciences and Mental Health, Hospital for Sick Children, Toronto, ON, Canada, 2Physiology, University of Toronto, Toronto, ON, Canada, 3Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada, 4Medical Biophysics, University of Toronto, Toronto, ON, Canada

Synopsis

White matter pathology following mild traumatic brain injury (mTBI) is complex and unlikely to be characterized by a single neuroimaging metric. DTI has been used to probe white matter microstructure, but is known to be non-specific. We show that the orientation dispersion index from the NODDI model may be more sensitive to white matter damage following mTBI in a mouse closed-skull impact model. Combined with quantitative susceptibility mapping, our data suggest minimal overt myelin loss, but progressive white matter injury, which may include myelin disruption, up to 6 weeks post-mTBI.

Introduction

Mild traumatic brain injury (mTBI) can lead to highly variable cognitive and behavioural symptoms, and there is no way of predicting who will experience persistent symptoms. The brain’s white matter is particularly vulnerable to primary and secondary injury processes following mTBI, including axon damage and changes to myelin, but this microstructural damage is not detectable with conventional imaging techniques.1 There is a need to better detect and describe white matter damage following mTBI with neuroimaging in order to better predict outcome and evaluate emerging treatments.

Diffusion tensor imaging (DTI) is the most common technique used to probe white matter microstructure, but the metrics derived from the tensor model (fractional anisotropy (FA), mean, axial, and radial diffusivities (MD, AD, RD)) are inherently non-specific. Recent studies have suggested that more complex geometric models of diffusion, including the Neurite Orientation Dispersion and Density Imaging (NODDI) model2, may be more sensitive and specific to different aspects of white matter pathology in mTBI.3,4 Furthermore, MRI sequences that are specific to other properties of myelin besides diffusion, including quantitative susceptibility mapping (QSM), may help elucidate the role of myelin changes following mTBI.

The objective of this study is to assess white matter changes over time following mTBI using multi-shell diffusion weighted imaging (msDWI) and QSM in a mouse model of mTBI in order to examine the sensitivity and specificity of these techniques to mTBI pathology.

Methods

Mild TBI was induced in 8-week-old male C57BL/6J mice using a closed-skull impact model with the impact centred on the right parietal bone. Sham mice underwent the same surgical procedure without the impact. Beginning 3 days or 6 weeks following the injury, the mice underwent 5 days of behavioural testing to assess memory, anxiety and depression-like behaviours. Following the final behavioural test, the mice were perfusion-fixed for ex vivo MRI (n = 13/group/time point).

Brains were imaged within the skull using a msDWI protocol with FSE readout (30 directions at b = 7300, 20 directions at b = 5070, 15 directions at b = 2080, and 5 b = 0 images; 100 µm isotropic resolution; δ/Δ = 9 ms/17 ms; TR/first TE/echo spacing/ETL = 400 ms/38 ms/8 ms/6; scan time ~ 22 hrs) and with a spoiled gradient echo sequence (56 µm isotropic resolution; TE/TR/alpha=18 ms/100 ms/70°; scan time ~ 11 hrs) from which quantitative susceptibility maps were calculated using the phase images.5

The diffusion data were modelled with the tensor model using FSL (FMRIB, Oxford UK) to compute standard DTI metrics and with the NODDI model to obtain the orientation dispersion index (ODI), and the intracellular, extracellular, and isotropic (CSF) volume fractions2. The diffusion and susceptibility data were analyzed separately. In each case, the reference images were registered together to create a consensus average using an automated linear and non-linear registration algorithm. The transformations from these registrations were then applied to the calculated metrics maps. Statistical comparisons were made between the TBI and sham groups at each white matter voxel and in each structure of a segmented atlas (22 white matter structures per hemisphere)6 using a linear model. Multiple comparisons were corrected for using the false discovery rate (FDR).

Results

Voxel-wise and structure-wise analyses revealed significant differences in FA (TBI < sham, 20% FDR) and ODI (TBI > sham, 1% FDR) between TBI and sham mice in the optic tracts (Figures 1 and 2). These results were driven by differences at the 6-week time point and were greater on the side contralateral to the impact. No other regional differences in DTI or NODDI metrics survived multiple comparison correction, although uncorrected t-statistic maps suggested possible increased RD in the optic tracts (p<0.02) as well and decreased ODI below the impact site at 1 week post-injury. No differences in magnetic susceptibility were observed between TBI and sham mice. Working memory deficits were detected in TBI mice 1 week post-injury (Figure 3; p=0.078).

Conclusion

These results suggest that ODI may be a more sensitive measure of mTBI pathology than FA and RD, thus combining multiple measures may have the potential to characterize mTBI pathology more fully. Our results show that the optic tracts are particularly susceptible to damage in this mouse model of mTBI, as reflected by changes in FA and ODI. Consistent with recent publications4,7, our QSM results suggest that there is minimal overt myelin loss, however myelin structure may be disrupted following mTBI. Our future work will aim to further validate the interpretation of the MRI measures using histopathology and electron microscopy and follow the evolution of mTBI pathology to later time points.

Acknowledgements

No acknowledgement found.

References

  1. Armstrong et al. White matter involvement after TBI: Clues to axon and myelin repair capacity. Exp Neurol 275 Pt 3: 328-333 (2016).
  2. Zhang et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4):1000-16 (2012).
  3. Churchill et al. White matter microstructure in athletes with a history of concussion: comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Human Brain Mapping 38:4201-4211 (2017).
  4. Palacios et al. The evolution of white matter microstructural changes after mild traumatic brain injury: a longitudinal DTI and NODDI study. [preprint] first posted online Jun. 14, 2018; doi: http://dx.doi.org/10.1101/345629.
  5. Li et al. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 55(4):1645-56 (2011).
  6. Dorr et al. High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice. Neuroimage 42(1):60-69 (2008).
  7. Weber et al. Pathological Insights from quantitative susceptibility mapping and diffusion tensor imaging in ice hockey players pre and post-concussion. Frontiers in Neurology 9:1-8 (2018) doi: 10.3389/fneur.2018.00575.

Figures

Average DTI and NODDI metrics maps overlaid with statistics maps indicating voxels that are significantly different in TBI mice vs. shams at 1 and 6 weeks post-injury. The statistics maps are thresholded at uncorrected p<0.01 (FA, ODI,ICVF) or p<0.05 (RD) to easily visualize the spatial distribution of the differences. FA and ODI show differences in the optic tracts that emerge at 6 weeks (yellow arrowheads). RD may also be increased after mTBI in the optic tracts (green arrowheads). The pink arrowhead points to a region of decreased ODI below the impact. Slice locations are shown on the axial image.

Boxplots showing DTI and NODDI metrics, as well as magnetic susceptibility, in the left optic tract (green structure in the accompanying image) at 1 and 6 weeks post-mTBI. FA is decreased and ODI is increased in TBI vs. sham (group effects, * 10% FDR and *** 1% FDR, respectively).

Alternation frequency on the Y-maze working memory task at 3 days and 6 weeks post-mTBI. TBI mice show fewer successful alternations at 3 days (*p=0.078), suggesting impaired working memory or attention in these mice, which resolves by 6 weeks.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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