Diffusion tensor imaging applications in multiple sclerosis patients using 3T magnetic resonance: a preliminary study.

OBJECTIVES: This study evaluated patients with multiple sclerosis using diffusion tensor imaging (DTI) to obtain fractional anisotropy (FA) and mean diffusivity (MD) values. METHODS: We investigated the possible statistically significant variation of MD and FA in different MS patients, compared simultaneously, putting in comparison their normal appearing white matter (NAWM) and white matter affected by disease (plaques), both during activity and in remission, with normal white matter (NWM) of control subjects. RESULTS: Statistical analysis using Levene's test for comparison of variances revealed significant (P < 0.05) differences between FA values of the NWM of the controls and those of NAWM and active or inactive lesions, of the patients in the study. However, the differences between MD values of the NWM of the controls and those of NAWM and active or inactive lesions of the patients in the study were judged not significant (P > 0.05). CONCLUSION: Imaging of MS using MRI techniques is constantly searching for reproducible quantitative parameter. This study shows how these parameters can be identified in the MD and FA values, and thus suggests the implementation of MRI routine protocols for diagnosing MS with the DTI analysis, since it can provide valuable information otherwise unobtainable. KEY POINTS: Magnetic resonance imaging is widely performed in multiple sclerosis (MS) patients Diffusion tensor imaging (DTI) can be implemented using a 3T magnet DTI provides quantitative parameters as mean diffusivity (MD) and fractional anisotropy (FA) MD and, especially, FA can help evaluate the lesion load in MS patients and also assess variation in normal appearing white matter (NAWM) in MS.

Distinct brain volume changes correlating with clinical stage, disease progression rate, mutation size, and age at onset prediction as early biomarkers of brain atrophy in Huntington's disease.

Searching brain and peripheral biomarkers is a requisite to cure Huntington's disease (HD). To search for markers indicating the rate of brain neurodegenerative changes in the various disease stages, we quantified changes in brain atrophy in subjects with HD. We analyzed the cross-sectional and longitudinal rate of brain atrophy, quantitatively measured by fully-automated multiparametric magnetic resonance imaging, as fractional gray matter (GM, determining brain cortex volume), white matter (WM, measuring the volume of axonal fibers), and corresponding cerebral spinal fluid (CSF, a measure of global brain atrophy), in 94 gene-positive subjects with presymptomatic to advanced HD, and age-matched healthy controls. Each of the three brain compartments we studied (WM, GM, and CSF) had a diverse role and their time courses differed in the development of HD. GM volume decreased early in life. Its decrease was associated with decreased serum brain-derived-neurotrophic-factor and started even many years before onset symptoms, then decreased slowly in a nonlinear manner during the various symptomatic HD stages. WM volume loss also began in the presymptomatic stage of HD a few years before manifest symptoms appear, rapidly decreasing near to the zone-of-onset. Finally, the CSF volume increase began many years before age at onset. Its volume measured in presymptomatic subjects contributed to improve the CAG-based model of age at onset prediction. The progressive CSF increase depended on CAG mutation size and continued linearly until the last stages of HD, perhaps representing the best marker of progression rate and severity in HD (R(2)= 0.25, P < 0.0001).

Three-dimensional computed tomography angiography in presurgical planning for treatment of infratentorial dural arteriovenous fistulas.

Dural arteriovenous fistulas (DAVFs) with pure leptomeningeal drainage may be cured by simple interruption of their venous side. This report illustrates the cases of 3 patients undergoing surgery for fistulas classified as Borden Type III, involving the posterior cranial fossa. Preoperatively, the surgical anatomy of these lesions was investigated with 3D reformatting of multislice CT angiography, in addition to conventional angiography. Reformatted images clarified the surgical anatomy of the malformation. Reconstructing both the osseous and the vascular structures and simulating the surgical orientation allowed localization of the dural takeoff point of the DAVF's drainage, showing its relationship with osseous landmarks. Precise localization of the DAVF's drainage may help in choosing the most direct and effective approach to treat the malformation. The reported cases could be treated with a standard retrosigmoid exposure, avoiding the need for more complex cranial base approaches.

BOLD signal and vessel dynamics: a hierarchical cluster analysis.

The aim of the present study was to analyze blood oxygenation level-dependent (BOLD) signal variation during an apnea-based task in order to assess the capability of a functional magnetic resonance imaging (fMRI) procedure to estimate cerebral vascular dynamic effects. We measured BOLD contrast by hierarchical cluster analysis in healthy subjects undergoing an fMRI experiment, in which the task paradigm was one phase of inspirational apnea (IA). By processing the time courses of the fMRI experiment, analysis was performed only on a subclass of all the possible signal patterns; basically, root mean square and absolute variation differences have been calculated. Considering the baseline value obtained by computing the mean value of the initial rest period as reference, particular voxels showed relative important variations during the IA task and during the recovery phase following the IA. We focused our interest on the signal response of voxels that would correspond mainly to white and gray matter regions and that also may be affected by the proximity of large venous vessels. The results are presented as maps of space-temporal distribution of time series variations with two levels of hierarchical clustering among voxels with low to high initial response. Furthermore, we have presented a clustering of the signal response delay, conducting to a partition and identification of specified brain sites.