Corticospinal tract abnormalities are associated with weakness in multiple sclerosis.

BACKGROUND AND PURPOSE: The association of MR imaging abnormalities with clinical disability in multiple sclerosis (MS) has been disappointing. This association might be improved by imaging specific functional systems in the central nervous system-for example, the motor system in a patient with weakness. Our aim was to assess the relationship between muscle strength in MS and corticospinal tract (CST) abnormalities detected with multimodality MR imaging of the brain. MATERIALS AND METHODS: In 47 individuals with MS, diffusion tensor imaging (DTI) at 3T was used to reconstruct the intracranial CSTs. Tract profiles depicted the variation in T2 relaxation time, magnetization transfer ratio (MTR), and DTI-derived indices (fractional anisotropy and diffusivity) as a function of normalized position along the tract. Brain parenchymal fraction was calculated as a normalized measure of brain volume. Stepwise linear regression modeling was used to determine the MR imaging indices most closely related to ankle dorsiflexion and hip flexion strength assessed with quantitative dynamometry. RESULTS: Individuals with MS were significantly weak: Average ankle strength fell 1.7 SDs below the age-, handedness-, and sex-corrected healthy mean. Brain parenchymal fraction was not associated with weakness. A parsimonious model that includes MTR in the brain stem and MS clinical subtype explained 30%-45% of the variance in ankle and hip strength. The model was successfully applied to scans and strength data from the same individuals at an earlier time point. CONCLUSION: MR imaging abnormalities specific to the motor tract are associated with clinical dysfunction related to that tract. The relevant abnormalities are found in the brain stem, distant from the periventricular inflammatory lesions that are common in MS. This suggests that neurodegeneration, rather than primary inflammation, at least partially explains the findings.

Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis.

OBJECTIVE: Optical coherence tomography (OCT) noninvasively quantifies retinal nerve fiber layer (RNFL) thickness. Studies show RNFL thinning in multiple sclerosis (MS), and we assessed its association with brain atrophy. METHODS: RNFL thickness was measured in 40 patients with MS and 15 controls. Brain parenchymal fraction (BPF) and partial brain volumes were estimated from cranial MRI scans using SIENA-X. Multiple linear regression modeling assessed the association between OCT and MRI measures of atrophy. RESULTS: Minimum RNFL thickness and subject age together predict 21% (p = 0.005) of the variance in BPF in all patients with MS and 43% (p = 0.003) of the variance in BPF in the subgroup with relapsing remitting MS (RRMS; n = 20). The partial correlation coefficient between BPF and minimum RNFL thickness, controlling for age, is 0.46 (p = 0.003) in all patients with MS and 0.69 (p = 0.001) in patients with RRMS. These associations are driven by CSF volume but not by gray or white matter volume. There is no significant association of these variables among controls. CONCLUSIONS: In multiple sclerosis (MS), retinal nerve fiber layer thickness is associated with brain parenchymal fraction and CSF volume. These data suggest that quantification of axonal thickness in the retina by optical coherence tomography (OCT) provides concurrent information about MRI brain abnormality in MS. OCT should be examined in longitudinal studies to determine if it could be used as an outcome measure in clinical trials of neuroprotective drugs.

Precision and accuracy of regional radioactivity quantitation using the maximum likelihood EM reconstruction algorithm.

The imaging characteristics of maximum likelihood (ML) reconstruction using the EM algorithm for emission tomography have been extensively evaluated. There has been less study of the precision and accuracy of ML estimates of regional radioactivity concentration. The authors developed a realistic brain slice simulation by segmenting a normal subject's MRI scan into gray matter, white matter, and CSF and produced PET sinogram data with a model that included detector resolution and efficiencies, attenuation, scatter, and randoms. Noisy realizations at different count levels were created, and ML and filtered backprojection (FBP) reconstructions were performed. The bias and variability of ROI values were determined. In addition, the effects of ML pixel size, image smoothing and region size reduction were assessed. Hit estimates at 3,000 iterations (0.6 sec per iteration on a parallel computer) for 1-cm(2) gray matter ROIs showed negative biases of 6%+/-2% which can be reduced to 0%+/-3% by removing the outer 1-mm rim of each ROI. FBP applied to the full-size ROIs had 15%+/-4% negative bias with 50% less noise than hit. Shrinking the FBP regions provided partial bias compensation with noise increases to levels similar to ML. Smoothing of ML images produced biases comparable to FBP with slightly less noise. Because of its heavy computational requirements, the ML algorithm will be most useful for applications in which achieving minimum bias is important.