Two-year study of cervical cord volume and myelin water in primary progressive multiple sclerosis

Primary progressive multiple sclerosis: part of the MS disease spectrum or separate disease entity?

Multiple sclerosis (MS), the most frequent demyelinating disease, is characterized by a variable disease course. The majority of patients starts with relapsing remitting (RR) disease; approximately 50-60% of these patients progress to secondary progressive (SP) disease. Only about 15% of the patients develop a progressive disease course from onset, termed primary progressive multiple sclerosis (PPMS); the underlying pathogenic mechanisms responsible for onset of the disease with either PPMS or relapsing remitting multiple sclerosis (RRMS) are unknown. Patients with PPMS do not show a female predominance and usually have a later onset of disease compared to patients with RRMS. Monozygous twins can be concordant or discordant for disease courses indicating that the disease course is not only genetically determined. Primary progressive multiple sclerosis and secondary progressive multiple sclerosis (SPMS) share many similarities in imaging and pathological findings. Differences observed among the different disease courses are more of a quantitative than qualitative nature suggesting that the different phenotypes are part of a disease spectrum modulated by individual genetic predisposition and environmental influences. In this review, we summarize the knowledge regarding the clinical, epidemiological, imaging, and pathological characteristics of PPMS and compare those characteristics with RRMS and SPMS.

Short-term stability of T1 and T2 relaxation measures in multiple sclerosis normal appearing white matter

The presence of diffuse and widespread abnormalities within the 'normal appearing' white matter (NAWM) of multiple sclerosis (MS) brain has been established. T(1) histogram analysis has revealed increased T(1) (related to water content) in segmented NAWM, while quantitative assessment of T(2) relaxation measures has demonstrated decreased myelin water fraction (MWF, related to myelin content) and increased geometric mean T(2) (GMT(2)) of the intra/extracellular water pool. Previous studies with follow-up periods of 1-5 years have demonstrated longitudinal changes in T(1) histogram metrics over time; however, longitudinal changes in MWF and GMT(2) of segmented NAWM have not been examined. We examined the short-term evolution of MWF, GMT(2) and T(1) in MS NAWM based on monthly scanning over 6 months in 18 relapsing remitting (RR) MS subjects. Histogram metrics demonstrated short-term stability of T(1), MWF and remitting (RR) MS subjects. We observed no change in MWF, GMT(2) or T(1) histogram metrics in NAWM in RRMS over the course of 6 months. Longer follow-up periods may be required to establish demonstrable changes in NAWM based on of MWF, GMT(2) and T(1) metrics.

Quantitative relaxometry of the brain

The exquisite soft tissue contrast provided by magnetic resonance imaging arises principally from differences in the intrinsic relaxation properties, T1 and T2. Although the intricate relationships that link tissue microstructure and the longitudinal and transverse relaxation times remain to be firmly established, quantitative measurement of these parameters, also referred to as quantitative relaxometry, can be informative of disease-related tissue change, developmental plasticity, and other biological processes. Further, relaxometry studies potentially offer a more detailed characterization of tissue, compared with conventional qualitative or weighted imaging approaches.The purposes of this review were to briefly review the biophysical basis of relaxation, focusing specifically on the T1, T2, and T2* relaxation times, and to detail some of the more widely used and clinically feasible techniques for their in vivo measurement. We will focus on neuroimaging applications, although the methods described are equally well suited to cardiac, abdominal, and musculoskeletal imaging. Potential sources of error, and methods for their correction, are also touched on. Finally, the combination of relaxation time data with other complementary quantitative imaging data, including diffusion tensor imaging, is discussed, with the aim of more thoroughly characterizing brain tissue.