Changes in Apparent Fiber Density and Track-Weighted Imaging Metrics in White Matter following Experimental Traumatic Brain Injury.
Traumatic brain injury (TBI) has been assessed with diffusion tensor imaging (DTI), a commonly used magnetic resonance imaging (MRI) marker for white matter integrity. However, given that the DTI model only fits a single fiber orientation, results can become confounded in regions of "crossing" white matter fibers. In contrast, constrained spherical deconvolution estimates a fiber orientation distribution directly from high angular resolution diffusion-weighted images. Consequently, constrained spherical deconvolution-based measures, such as apparent fiber density (AFD) and track-weighted imaging (TWI) metrics (including tract density imaging, average pathlength mapping, and mean curvature), may be more sensitive than DTI metrics to white matter injury post-TBI. As such, this study administered the lateral fluid percussion injury (FPI) model of TBI, assessed for changes in AFD and TWI metrics, and compared these results to the DTI metrics, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Rats received either an FPI (n = 11) or sham injury (n = 9) and after a recovery period of 12 weeks underwent MRI. AFD was calculated as described previously and statistical testing was performed using connectivity-based fixel enhancement. TWI and DTI metrics were assessed using voxel-wise nonparametric permutation testing. We found that rats given an FPI had significantly reduced AFD, tract density, average pathlength, and mean curvature when compared to sham-injured rats and significant changes in DTI metrics, including reduced FA and increased MD, RD, and AD. However, the latter DTI metrics identified fewer voxels affected by TBI. Additionally, analysis of AFD with connectivity-based fixel enhancement was the only method that identified damage within the corticospinal tract of rats given an FPI. These results support the use of constrained spherical deconvolution, in conjunction with DTI metrics, to better assess disease progression and treatment post-TBI.
1 Anatomy and Neuroscience, The University of Melbourne , Parkville, VIC, Australia .|2 The Florey Institute of Neuroscience and Mental Health , Parkville, VIC, Australia .
David K
DK
Wright
2 The Florey Institute of Neuroscience and Mental Health , Parkville, VIC, Australia .|3 Department of Electrical & Electronic Engineering, The University of Melbourne , Parkville, VIC, Australia.
Leigh A
LA
Johnston
4 Applied MRI Research, National Institute of Radiological Sciences , QST, Chiba, Japan .
Jeff
J
Kershaw
1 Anatomy and Neuroscience, The University of Melbourne , Parkville, VIC, Australia .
Roger
R
Ordidge
5 Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne , Parkville, VIC, Australia .
Terence J
TJ
O'Brien
5 Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne , Parkville, VIC, Australia .
Sandy R
SR
Shultz
D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging*; D000070642:Brain Injuries, Traumatic / Q000000981:diagnostic imaging*; D056324:Diffusion Tensor Imaging / Q000379:methods; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009412:Nerve Fibers; D059906:Neuroimaging / Q000379:methods*; D051381:Rats; D020318:Rats, Long-Evans; D066127:White Matter / Q000000981:diagnostic imaging*
DTI; MRI; axonal injury; biomarkers