Braeckman, Kim

Caeyenberghs, Karen

Descamps, Benedicte

Vanhove, Christian

Undetermined

Ghent University Academic Bibliography

Introduction Traumatic brain injury (TBI) is the leading cause of acquired disability in young adults, often caused by traffic accidents or sport injuries1. Mild TBI (mTBI) is the most common type of TBI. While conventional scans (CT or anatomical MRI) show no evidence of injury due to the diffuse and subtle nature of mTBI, the patients can suffer cognitive defects such as memory problems, attention deficits, and executive control deficits, evens years after their injury. The aim of this study is to investigate whether multishell diffusion MRI analysis can be used to detect microstructural changes in a rat model of mTBI.  Methods Animal model: Nine female Wistar rats weighing 250 ± 19.6 g sustained mTBI utilizing the Marmarou weight drop model2. In brief, in anesthetized rats a steel helmet was fixed on the skull 1/3 before and 2/3 behind bregma. The rat was positioned under a 450 g brass weight on a foam bed. The weight was dropped from a height of 1m guided through a plexiglass column. The foam bed together with the rat was rapidly removed from the column to prevent a second impact.  Imaging and data analysis: MRI data were acquired on a 7T MRI scanner (PharmaScan, Bruker, Ettlingen) before and 1 week after brain injury. T2-weighted images were acquired for anatomical reference. Multishell diffusion data were acquired with multiple directions (b=800, 1500 and 2000 s/mm2; 32, 46 and 64 directions; 5, 5 and 7 b0 images respectively). DWI images were corrected for EPI, motion and eddy current distortions in ExploreDTI version 4.8.6.3. Moreover diffusion kurtosis tensor estimation was performed using weighted linear least squares method4. Maps for the diffusion and kurtosis metrics (FA, MD, AD, RD, MK, AK and RK) were calculated based on the diffusion kurtosis imaging model5 and maps for the white matter metrics (AWF, AxEAD, RadEAD, tortuosity) were calculated based on a white matter diffusion model6. The maps were co-registered in SPM12 with an anatomical template based on the local population, using the anatomical T2-weighted images. The template was constructed by realignment, coregistration and normalisation to the first subject. A volume-of-interest analysis was performed in the hippocampus, cingulum and corpus callosum using Amide toolbox7. The Wilcoxon signed-rank test was performed for each map to investigate changes in white matter between the two time points in SPSS. P < 0.05 was considered significant.  Immunohistochemical analysis: Six rats were sacrificed for histological analysis and perfused with 4% paraformaldehyde. Sections of the brain were stained for the following cellular components: synapses (with anti synaptophysin); myelin (with Lyxol Fast Blue staining); astrocytes (with anti glial fibrillary acidic protein); and neurons (with anti neuronal nuclei, NeuN).   Table 1. Results for the diffusion, kurtosis and white matter metrics before (pre) and after (post) impact in the corpus callosum, cingulum and hippocampus. Abbreviations: AD, axial diffusivity (10-3 mm2/s); AK, axial kurtosis; AWF, axonal water fraction; AxEAD, axial extra-axonal diffusivity (10-3 mm2/s); RadEAD, radial extra-axonal diffusivity (10-3 mm2/s); FA, fractional anisotropy; MD, mean diffusivity (10-4 mm2/s); MK, mean kurtosis; RD, radial diffusivity (10-4 mm2/s). Figure 1. Immunostaining with anti neuronal nuclei (NeuN). Arrowheads indicate injured axons in the cortex.   Results Due to outliers for the radial kurtosis and tortuosity metric, these metrics were removed from final analyses. As can be seen from Table 1, we found increased values of several DTI, DKI and WMTI metrics (AD, AK, AWF, AxEAD, FA, MD, MK) in the three regions of interest in the post-scan compared to the pre-scan. Immunohistological staining for NeuN might suggest that several neurons are undergoing Walerian degeneration.   Discussion and conclusion An increase in AWF could be explained by axonal swelling. Furthermore an increase in AK, MK and FA supports this hypothesis. Since the AWF was increased in all three regions we can conclude that this metric is very sensitive for changes in microstructure due to mTBI, 7 days post injury. Walerian degeneration in the cortex might indicate injured and swollen axons and can be proof of concept that our model induces traumatic brain injury. Further histological analysis is currently on going in order to provide a biological basis to support this hypothesis.   References 1. Thurman. The Epidemiology of Traumatic Brain Injury in Children and Youths: A Review of Research Since 1990. J Child Neurol, 2016, 31(1):20-27. 2. Marmarou, et al. A new model of diffuse brain injury in rats: Part I. J Neuroscience, 1994, 80:291-300. 3. Leemans, et al. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th Annual Meeting of Intl Soc Mag Reson Med, p. 3537, Hawaii, USA, 2009. 4. Veraart, et al. Weighted linear least squares estimation of diffusion MRI parameters: strengths, limitations, and pitfalls. NeuroImage, 2013, 81:335-346. 5. Veraart, et al. More Accurate Estimation of Diffusion Tensor Parameters Using Diffusion Kurtosis Imaging. MRM, 2001, 65:138-145. 6. Fieremans, et al. White matter characterization with diffusional kurtosis imaging, NeuroImage 2011, 58(1):177-188. 7. Loening, et al. AMIDE: A Free Software Tool for Multimodality Medical Image Analysis. Molecular Imaging, 2003, 2(3):131-137.   Metric	Corpus	callosum	 Cingulum	 Hippocampus	Pre	 Post	 Pre	 Post	 Pre	 Post	AD		 1,40	±	0,04	 1,47	±	0,09	 1,32	±	0,04	 1,30	±	0,07	 1,15	±	0,05	 1,19	±	0,46	a	AK	 0,66	±	0,06	 0,78	±	0,04	a	 0,69	±	0,06	 0,75	±	0,02	 0,62	±	0,07	 0,72	±	0,05	a	AWF	 0,30	±	0,02	 0,36	±	0,03	a	 0,31	±	0,02	 0,35	±	0,02	a	 0,24	±	0,	02	 0,27	±	0,01	a	AxEAD		 1,84	±	0,09	 2,05	±	0,16	 1,75	±	0,08	 1,81	±	0,	12	 1,43	±	0,08	 1,54	±	0,69	a	RadEAD		 1,05	±	0,05	 1,01	±	0,09	 1,02	±	0,05	 9,81	±	0,61	 1,03	±	0,07	 1,07	±	0,66	FA	 0,36	±	0,04	 0,45	±	0,04	a	 0,35	±	0,01	 0,39	±	0,03	a	 0,20	±	0,01	 0,22	±	0,03	MD	 9,93	±	0,33	 9,67	±	0,61	 9,50	±	0,31	 9,07	±	0,48	 9,34	±	0,49	 9,51	±	0,35	MK	 0,68	±	0,09	 0,84	±	0,09	b	 0,73	±	0,09	 0,88	±	0,07	 0,54	±	0	08	 0,67	±	0,07	b	RD		 7,91	±	0,33	 7,15	±	0,62	 7,66	±	0,30	 7,09	±	0,41	b	 8,29	±	0,49	 8,32	±	0,39	a. p	=	0,028;	b.	p	=	0,046	

Characterizing microstructural alterationsin a rat model of mild traumatic brain injury : a longitudinal multi-shell diffusion MRI analysis

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Characterizing microstructural alterationsin a rat model of mild traumatic brain injury : a longitudinal multi-shell diffusion MRI analysis

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