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Diffusion Imaging in the Rat Cervical Spinal Cord
Published on: April 7, 2015
Detection of Region-specific Fiber Damage within Injured Spinal Cord Using Advanced Diffusion MRI
Abstract:
This study aimed to evaluate diffusion parameters derived from diffusion tensor imaging (DTI) and spherical mean technique (SMT) for detecting region-specific, fine-grained tissue damage and white matter (WM) tract disruptions following spinal cord injury (SCI). Diffusion MRI data were acquired from the cervical spinal cord of monkeys before and after a unilateral dorsal column lesion at the C5 level, using a 9.4T scanner. Parametric maps derived from DTI and SMT effectively detected regional fiber damage around 16 weeks post-injury. Post-mortem silver staining served as the ground truth for assessing region-specific fiber damage. Diffusion MRI maps aligned well with histological measures and captured the severity of WM damage at the lesioned segment (in an order of dorsal > ventral > lateral WM tracts) and along the dorsal column tract across segments (in an order of lesion center > rostral > caudal). Among the diffusion parameters, fractional anisotropy (FA), axonal volume fraction (V ax ), radial diffusivity (RD), and extra axonal transverse diffusivity (D ex ) showed most significant changes at and around the lesion site where severe tissue damage occurred. FA, V ax , and axial diffusivity (AD) exhibited marked changes in dorsal column proximal to the lesion center, where moderate axonal damage occurred. Additionally, AD and FA showed the greatest sensitivity (true positive rate) and specificity (true negative rate) to mild fiber disruption and demyelination in regions distal to the lesion. Overall, FA provided the highest sensitivity and specificity for detecting fiber degeneration and demyelination, while V ax demonstrated the strongest spatial correlation with histologic markers of regional fiber damage. The combination of DTI and SMT thus offers reliable biomarkers for assessing SCI.
Insights
Diffusion tensor imaging (DTI) and spherical mean technique (SMT) effectively detect spinal cord injury (SCI) damage. Fractional anisotropy (FA) and axonal volume fraction (Vax) show the most significant changes, offering reliable biomarkers for assessing white matter (WM) injury.
Area of Science:
- Neuroimaging
- Spinal Cord Injury Research
- White Matter Integrity
Background:
- Spinal cord injury (SCI) causes complex white matter (WM) damage.
- Accurate detection of region-specific WM damage is crucial for understanding SCI.
- Diffusion MRI techniques like DTI and SMT offer potential for non-invasive assessment.
Purpose of the Study:
- To evaluate diffusion parameters from DTI and SMT for detecting SCI-induced WM damage.
- To assess the ability of these techniques to identify region-specific and fine-grained tissue alterations.
- To correlate diffusion parameters with histological findings for validation.
Main Methods:
- Diffusion MRI data acquired from monkey cervical spinal cords using a 9.4T scanner.
- Acquisition before and after a unilateral dorsal column lesion at C5.
- Analysis using diffusion tensor imaging (DTI) and spherical mean technique (SMT) parametric maps, validated with post-mortem silver staining.
Main Results:
- DTI and SMT maps detected regional fiber damage 16 weeks post-injury, aligning with histological findings.
- WM damage severity followed an order of dorsal > ventral > lateral WM tracts at the lesion site.
- Fractional anisotropy (FA), axonal volume fraction (Vax), radial diffusivity (RD), and extra axonal transverse diffusivity (Dex) showed significant changes at the lesion site; FA, Vax, and axial diffusivity (AD) changed proximally; AD and FA were sensitive to distal mild damage.
Conclusions:
- DTI and SMT provide reliable biomarkers for assessing SCI-induced WM damage.
- FA demonstrated high sensitivity and specificity for detecting fiber degeneration and demyelination.
- Vax showed the strongest spatial correlation with histological markers of regional fiber damage, highlighting its utility in SCI assessment.

