Although about 30% of patients with mild traumatic brain injury (mTBI) suffer prolonged symptoms after injury1, conventional anatomic magnetic resonance imaging (MRI) has not proven useful in diagnosing or predicting outcomes after mTBI. In this work we evaluated a novel technique, diffusion compartment imaging (DCI), with a mouse model of mTBI that enables study of mTBI under controlled conditions. We compared DCI and diffusion tensor imaging (DTI) changes to histopathological observations in two injury conditions (with and without persistent functional deficits). Our results suggest that, unlike DTI, DCI detects specific evidence of traumatic axonal injury. Moreover, DCI detects changes only in mice with persistent functional deficits.
Mice underwent mTBI using the weight-drop model7 of mTBI and two injury conditions: 6 mice with TD mTBI and 6 mice with PD mTBI. In-vivo MRI was performed before and in the acute phase (<24h) after injury using a Bruker BioSpec 70/30 system. We acquired a T2 RARE anatomical scan (resolution= $$$0.08\times0.08\times0.5\mathrm{mm}^3$$$, TE/TR=9ms/3s, RARE factor=8, 8 averages, <10min) and a multi-shell DW-MRI scan (5 shells at $$$b=250,500,750,1000,2000\mathrm{s}/\mathrm{mm}^2$$$, multi-shot 2D EPI, 4 shots, resolution=$$$0.16\times0.16\times0.5\mathrm{mm}^3$$$, TE/TR=36ms/2500ms, 6 repetitions, <1h20). After acquisition the mice were sacrificed and the brains collected for histopathology. Immunostaining was performed for APP.
For each mice, pre- and post-injury DW scans were aligned to a common reference system defined by the pre-injury T2 scan. A template was created by manually segmenting the corpus callosum (CC) in one mouse. The CC was then automatically segmented in all scans by using non-rigid registration of the template to each mouse, and the mean of DTI and DCI parameters assessed in the corresponding region. DCI was achieved using the DIAMOND model.10 DIAMOND is a hybrid biophysical and statistical approach that characterizes each diffusive environment in each voxel using a continuous statistical distribution of diffusion tensors. It enables the assessment of compartment-specific diffusion characteristics (compartment FA, RD and MD, ie cFA,cRD,cMD) and of the intra-compartment microstructural heterogeneity (compartment heterogeneity index, cHEI). We considered a DIAMOND model with 1) one anisotropic compartment; 2) a free diffusion compartment ($$$D_\mathrm{free}=3\times10^{-3}\mathrm{mm}^2/\mathrm{s}$$$); and 3) an isotropic restricted diffusion compartment ($$$D_\mathrm{{isoR}}=1\times10^{-3}\mathrm{mm}^2/\mathrm{s}$$$). The fractions associated with the compartments were not constrained to sum to 1. Statistical significance testing was achieved using the Kruskal-Wallis test, correcting for multiple comparison to account for the number of tested parameters.
1.Bigler, E.D., Neuropsychology and clinical neuroscience of persistent post-concussive syndrome. J Int Neuropsychol Soc, 2008. 14(1): p. 1-22.
2.Basser, P.J., J. Mattiello, and D. LeBihan, MR diffusion tensor spectroscopy and imaging. Biophys J, 1994. 66(1): p. 259-67.
3.Hulkower, M.B., D.B. Poliak, S.B. Rosenbaum, et al., A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol, 2013. 34(11): p. 2064-74.
4.Jones, D.K., T.R. Knosche, and R. Turner, White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. Neuroimage, 2013. 73: p. 239-54.
5.Stanisz, G.J., A. Szafer, G.A. Wright, et al., An analytical model of restricted diffusion in bovine optic nerve. Magn Reson Med, 1997. 37(1): p. 103-11.
6.Latour, L.L., K. Svoboda, P.P. Mitra, et al., Time-dependent diffusion of water in a biological model system. Proc Natl Acad Sci U S A, 1994. 91(4): p. 1229-33.
7.Mannix, R., J. Berglass, J. Berkner, et al., Chronic gliosis and behavioral deficits in mice following repetitive mild traumatic brain injury. Journal of neurosurgery, 2014: p. 1-9.
8.Kondo, A., K. Shahpasand, R. Mannix, et al., Antibody against early driver of neurodegeneration cis P-tau blocks brain injury and tauopathy. Nature, 2015. 523(7561): p. 431-6.
9.Salvador, R., A. Pena, D.K. Menon, et al., Formal characterization and extension of the linearized diffusion tensor model. Hum Brain Mapp, 2005. 24(2): p. 144-55.
10.Scherrer, B., A. Schwartzman, M. Taquet, et al., Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND). Magn Reson Med, 2016. 76(3): p. 963-977.
11.Johnson, V.E., W. Stewart, and D.H. Smith, Axonal pathology in traumatic brain injury. Exp Neurol, 2013. 246: p. 35-43.
12.Bazarian, J.J., T. Zhu, B. Blyth, et al., Subject-specific changes in brain white matter on diffusion tensor imaging after sports-related concussion. Magn Reson Imaging, 2012. 30(2): p. 171-80.
13.Unterberg, A.W., J. Stover, B. Kress, et al., Edema and brain trauma. Neuroscience, 2004. 129(4): p. 1021-9.