Longitudinal changes in diffusion tensor-based quantitative MRI in multiple sclerosis.

OBJECTIVE: To estimate longitudinal changes in a quantitative whole-brain and tract-specific MRI study of multiple sclerosis (MS), with the intent of assessing the feasibility of this approach in clinical trials. METHODS: A total of 78 individuals with MS underwent a median of 3 scans over 2 years. Diffusion tensor imaging indices, magnetization transfer ratio, and T2 relaxation time were analyzed in supratentorial brain, corpus callosum, optic radiations, and corticospinal tracts by atlas-based tractography. Linear mixed-effect models estimated annualized rates of change for each index, and sample size estimates for potential clinical trials were determined. RESULTS: There were significant changes over time in fractional anisotropy and perpendicular diffusivity in the supratentorial brain and corpus callosum, mean diffusivity in the supratentorial brain, and magnetization transfer ratio in all areas studied. Changes were most rapid in the corpus callosum, where fractional anisotropy decreased 1.7% per year, perpendicular diffusivity increased 1.2% per year, and magnetization transfer ratio decreased 0.9% per year. The T2 relaxation time changed more rapidly than diffusion tensor imaging indices and magnetization transfer ratio but had higher within-participant variability. Magnetization transfer ratio in the corpus callosum and supratentorial brain declined at an accelerated rate in progressive MS relative to relapsing-remitting MS. Power analysis yielded reasonable sample sizes (on the order of 40 participants per arm or fewer) for 1- or 2-year trials. CONCLUSIONS: Longitudinal changes in whole-brain and tract-specific diffusion tensor imaging indices and magnetization transfer ratio can be reliably quantified, suggesting that small clinical trials using these outcome measures are feasible.

MRI of the corpus callosum in multiple sclerosis: association with disability.

Inflammatory demyelination and axon damage in the corpus callosum are prominent features of multiple sclerosis (MS) and may partially account for impaired performance on complex tasks. The objective of this article was to characterize quantitative callosal MRI abnormalities and their association with disability. In 69 participants with MS and 29 healthy volunteers, lesional and extralesional callosal MRI indices were estimated via diffusion tensor tractography. expanded disability status scale (EDSS) and MS functional composite (MSFC) scores were recorded in 53 of the participants with MS. All tested callosal MRI indices were diffusely abnormal in MS. EDSS score was correlated only with age (r = 0.51). Scores on the overall MSFC and its paced serial auditory addition test (PASAT) and 9-hole peg test components were correlated with callosal fractional anisotropy (r = 0.27, 0.35, and 0.31, respectively) and perpendicular diffusivity (r = -0.29, -0.30, and -0.31) but not with overall callosal volume or callosal lesion volume; the PASAT score was more weakly correlated with callosal magnetization-transfer ratio (r = 0.21). Anterior callosal abnormalities were associated with impaired PASAT performance and posterior abnormalities with slow performance on the 9-hole peg test. In conclusion, abnormalities in the corpus callosum can be assessed with quantitative MRI and are associated with cognitive and complex upper-extremity dysfunction in MS.

Intelligence and medial temporal lobe function in older adults: a functional MR imaging-based investigation.

BACKGROUND AND PURPOSE: The influence of general intelligence and formal education on functional MR imaging (fMRI) activation has not been thoroughly studied in older adults. Although these factors could be controlled for through study design, this approach makes sample selection more difficult and reduces power. This study was undertaken to examine our hypothesis that intelligence and education would impact medial temporal lobe (MTL) fMRI responses to an episodic memory task in healthy elderly subjects. MATERIALS AND METHODS: Thirty-six women and 38 men, 50-83 years of age (mean, 63.4 +/- 7.9 years), completed an auditory paired-associates paradigm in a 1.5T magnet. The amplitude and volume of fMRI activation for both the right and left MTLs and MTL subregions were correlated with the intelligence quotients (IQs) and educational levels by using Pearson correlation coefficient tests and regression analyses. RESULTS: The participants' mean estimated full scale IQ and verbal IQ scores were 110.4 +/- 7.6 (range, 92-123) and 108.9 +/- 8.7 (range, 88-123), respectively. The years of education showed a mean of 16.1 +/- 3.2 years (range, 8-25 years). The paradigm produced significant activation in the MTL and subregions. However, the volume and amplitude of activation were unrelated to either IQ or years of schooling in men and/or women. CONCLUSIONS: We found no evidence of an effect of IQ or education on either the volume or amplitude of fMRI activation, suggesting that these factors do not necessarily need to be incorporated into study design or considered when evaluating other group relationships with fMRI.

Monte carlo conditional inference for log-linear and logistic models: a survey of current methodology.

Nuisance parameters are parameters that are not of immediate interest to the experimenter. For log-linear and logistic models the null distribution of (most) statistics of interest depends on such parameters. Traditionally, nuisance parameters were eliminated by performing inference with respect to the chi-squared limiting distribution of common test statistics. An alternative solution is to eliminate the nuisance parameters by conditioning on their minimal sufficient statistics. The support of the resulting conditional distribution is often intractable, making null probability calculations challenging. An often feasible way to avoid complete enumeration of this support is to approximate conditional probabilities using Monte Carlo methods. In this article we survey recent developments in Monte Carlo conditional analysis for log-linear and logistic models focusing on the algorithms proposed by Booth and Butler, Diaconis and Sturmfels, Smith et al., and Mehta et al. We illustrate these algorithms with simple motivating examples.