Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI

UMR-678, INSERM-UPMC, Pitié-Salpêtrière Hospital, Paris, France.
NeuroImage (Impact Factor: 6.36). 04/2011; 55(3):1024-33. DOI: 10.1016/j.neuroimage.2010.11.089
Source: PubMed

ABSTRACT Characterizing demyelination/degeneration of spinal pathways in traumatic spinal cord injured (SCI) patients is crucial for assessing the prognosis of functional rehabilitation. Novel techniques based on diffusion-weighted (DW) magnetic resonance imaging (MRI) and magnetization transfer (MT) imaging provide sensitive and specific markers of white matter pathology. In this paper we combined for the first time high angular resolution diffusion-weighted imaging (HARDI), MT imaging and atrophy measurements to evaluate the cervical spinal cord of fourteen SCI patients and age-matched controls. We used high in-plane resolution to delineate dorsal and ventrolateral pathways. Significant differences were detected between patients and controls in the normal-appearing white matter for fractional anisotropy (FA, p<0.0001), axial diffusivity (p<0.05), radial diffusivity (p<0.05), generalized fractional anisotropy (GFA, p<0.0001), magnetization transfer ratio (MTR, p<0.0001) and cord area (p<0.05). No significant difference was detected in mean diffusivity (p=0.41), T1-weighted (p=0.76) and T2-weighted (p=0.09) signals. MRI metrics were remarkably well correlated with clinical disability (Pearson's correlations, FA: p<0.01, GFA: p<0.01, radial diffusivity: p=0.01, MTR: p=0.04 and atrophy: p<0.01). Stepwise linear regressions showed that measures of MTR in the dorsal spinal cord predicted the sensory disability whereas measures of MTR in the ventro-lateral spinal cord predicted the motor disability (ASIA score). However, diffusion metrics were not specific to the sensorimotor scores. Due to the specificity of axial and radial diffusivity and MT measurements, results suggest the detection of demyelination and degeneration in SCI patients. Combining HARDI with MT imaging is a promising approach to gain specificity in characterizing spinal cord pathways in traumatic injury.

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Available from: Pierre-François Pradat, Apr 23, 2014
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    • "Magnetic resonance imaging (MRI) of the spinal cord has tremendous potential for improving diagnosis/prognosis in traumatic, inflammatory and other causes of diseases, as well as for evaluating the effect of new drugs. In particular, multi-parametric MRI, combining several quantitative techniques (e.g., diffusion-weighted imaging, magnetization transfer, functional MRI) provides a variety of biomarkers sensitive to white matter damage and neuronal function in the spinal cord (Wheeler-Kingshott et al., 2014; Cohen-Adad et al., 2011a; Stroman et al., 2014). However, spinal cord MRI faces two major challenges: data acquisition and data processing. "
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    ABSTRACT: The field of spinal cord MRI is lacking a common template, as existing for the brain, which would allow extraction of multi-parametric data (diffusion-weighted, magnetization transfer, etc.) without user bias, thereby facilitating group analysis and multi-center studies. This paper describes a framework to produce an unbiased average anatomical template of the human spinal cord. The template was created by co-registering T2-weighted images (N=16 healthy volunteers) using a series of pre-processing steps followed by non-linear registration. A white and gray matter probabilistic template was then merged to the average anatomical template, yielding the MNI-Poly-AMU template, which currently covers vertebral levels C1 to T6. New subjects can be registered to the template using a dedicated image processing pipeline. Validation was conducted on 16 additional subjects by comparing an automatic template-based segmentation and manual segmentation, yielding a median Dice coefficient of 0.89. The registration pipeline is rapid (~15min), automatic after one C2/C3 landmark manual identification, and robust, thereby reducing subjective variability and bias associated with manual segmentation. The template can notably be used for measurements of spinal cord cross-sectional area, voxel-based morphometry, identification of anatomical features (e.g., vertebral levels, white and gray matter location) and unbiased extraction of multi-parametric data.
    NeuroImage 09/2014; 102. DOI:10.1016/j.neuroimage.2014.08.057 · 6.36 Impact Factor
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    • "Magnetic resonance imaging (MRI) of the spinal cord has tremendous potential for improving diagnosis/prognosis in neurodegenerative diseases and trauma as well as for developing new drugs. In particular, multiparametric MRI, which combines several semiquantitative techniques (e.g., diffusion-weighted imaging, magnetization transfer, and functional MRI), provides a variety of biomarkers sensitive to white matter integrity and neuronal function [1, 2]. However, spinal cord MRI in research and clinics is underutilized, a direct consequence of the difficulties related to the numerous artifacts and low signal sensitivity in the spine region. "
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    ABSTRACT: Context. MRI of the spinal cord provides a variety of biomarkers sensitive to white matter integrity and neuronal function. Current processing methods are based on manual labeling of vertebral levels, which is time consuming and prone to user bias. Although several methods for automatic labeling have been published; they are not robust towards image contrast or towards susceptibility-related artifacts. Methods. Intervertebral disks are detected from the 3D analysis of the intensity profile along the spine. The robustness of the disk detection is improved by using a template of vertebral distance, which was generated from a training dataset. The developed method has been validated using T1- and T2-weighted contrasts in ten healthy subjects and one patient with spinal cord injury. Results. Accuracy of vertebral labeling was 100%. Mean absolute error was 2.1 ± 1.7 mm for T2-weighted images and 2.3 ± 1.6 mm for T1-weighted images. The vertebrae of the spinal cord injured patient were correctly labeled, despite the presence of artifacts caused by metallic implants. Discussion. We proposed a template-based method for robust labeling of vertebral levels along the whole spinal cord for T1- and T2-weighted contrasts. The method is freely available as part of the spinal cord toolbox.
    International Journal of Biomedical Imaging 07/2014; 2014(1):719520. DOI:10.1155/2014/719520
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    • "For an exhaustive description of the MRI acquisition parameters, the reader is referred to [25]. "
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    ABSTRACT: To evaluate multimodal MRI of the spinal cord in predicting disease progression and one-year clinical status in amyotrophic lateral sclerosis (ALS) patients. After a first MRI (MRI1), 29 ALS patients were clinically followed during 12 months; 14/29 patients underwent a second MRI (MRI2) at 11±3 months. Cross-sectional area (CSA) that has been shown to be a marker of lower motor neuron degeneration was measured in cervical and upper thoracic spinal cord from T2-weighted images. Fractional anisotropy (FA), axial/radial/mean diffusivities (λ⊥, λ//, MD) and magnetization transfer ratio (MTR) were measured within the lateral corticospinal tract in the cervical region. Imaging metrics were compared with clinical scales: Revised ALS Functional Rating Scale (ALSFRS-R) and manual muscle testing (MMT) score. At MRI1, CSA correlated significantly (P<0.05) with MMT and arm ALSFRS-R scores. FA correlated significantly with leg ALFSRS-R scores. One year after MRI1, CSA predicted (P<0.01) arm ALSFSR-R subscore and FA predicted (P<0.01) leg ALSFRS-R subscore. From MRI1 to MRI2, significant changes (P<0.01) were detected for CSA and MTR. CSA rate of change (i.e. atrophy) highly correlated (P<0.01) with arm ALSFRS-R and arm MMT subscores rate of change. Atrophy and DTI metrics predicted ALS disease progression. Cord atrophy was a better biomarker of disease progression than diffusion and MTR. Our study suggests that multimodal MRI could provide surrogate markers of ALS that may help monitoring the effect of disease-modifying drugs.
    PLoS ONE 04/2014; 9(4):e95516. DOI:10.1371/journal.pone.0095516 · 3.23 Impact Factor
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