Multi-component relaxation in clinically isolated syndrome: Lesion myelination may predict multiple sclerosis conversion

Abstract

We performed a longitudinal case-control study on patients with clinically isolated syndrome (CIS) with the aid of quantitative whole-brain myelin imaging. The aim was (1) to parse early myelin decay and to break down its distribution pattern, and (2) to identify an imaging biomarker of the conversion into clinically definite Multiple Sclerosis (MS) based on in vivo measurable changes of myelination.Imaging and clinical data were collected immediately after the onset of first neurological symptoms and follow-up explorations were performed after 3, 6, and, 12 months. The multi-component Driven Equilibrium Single Pulse Observation of T1/T2 (mcDESPOT) was applied to obtain the volume fraction of myelin water (MWF) in different white matter (WM) regions at every time-point. This measure was subjected to further voxel-based analysis with the aid of a comparison of the normal distribution of myelination measures with an age and sex matched healthy control group. Both global and focal relative myelination content measures were retrieved.We found that (1) CIS patients at the first clinical episode suggestive of MS can be discriminated from healthy control WM conditions (p < 0.001) and therewith reproduced our earlier findings in late CIS, (2) that deficient myelination in the CIS group increased in T2 lesion depending on the presence of gadolinium enhancement (p < 0.05), and (3) that independently the CIS T2 lesion relative myelin content provided a risk estimate of the conversion to clinically definite MS (Odds Ratio 2.52).We initially hypothesized that normal appearing WM myelin loss may determine the severity of early disease and the subsequent risk of clinically definite MS development. However, in contrast we found that WM lesion myelin loss was pivotal for MS conversion. Regional myelination measures may thus play an important role in future clinical risk stratification.

Psychological effects of breastfeeding on children and mothers

“…Studies employing structural and diffusion-weighted MRI critically complement and extend the above-mentioned findings by directly measuring differences in brain structure. In line with the finding that breastfeeding impacts the timing of myelination, whole brain volume, cortical thickness, and white matter volume have all been found to be increased among children with longer durations of breastfeeding experience [ 33 , 37 – 39 ]. For example, in a cross-sectional design, Deoni et al [ 33 ] investigated white matter maturation from 10 months to 4 years of age and found a positive association between the duration of exclusive breastfeeding and the development of white matter tracts.…”

“…Furthermore, this study reported that breastfeeding was associated with white matter in tracts commonly associated with higher-order cognition and socio-emotional functioning, including the superior longitudinal fasciculus [ 33 ]. Another critical follow-up study from the same group of researchers assessed changes in white matter volume in a longitudinal design [ 39 ]. In this study, breastfed children displayed a prolonged window of white matter development between 16 months and 2 years, resulting in an overall myelin increase detectable by 2 years of age that persisted through childhood.…”

“…In comparison, formula-fed infants displayed a significantly slower rate of white matter development between 1 and 2 years of age, and the overall volume continued to remain below the volume measured for the breastfed infants. Furthermore, Deoni et al [ 39 ] compared the brain development outcomes of infants fed different types of formula. Notably, infants fed with formulas with the highest levels of DHA and ARA showed the white matter development most similar to breastfed infants, albeit on a smaller scale.…”

Predicting conversion from clinically isolated syndrome to multiple sclerosis–An imaging-based machine learning approach

“…Opposed to our study, only basic intensity features (average lesion PD- and T2-intensities) have been employed. Other studies used advanced MR techniques, such as measuring myelin water fraction in white matter (Kitzler et al, 2018) or MR spectroscopy (Ion-Margineanu et al, 2017). While these methods can provide new insight into how MS patients can be assessed with MR imaging, they are not commonly used.…”

“…Machine learning has also been used in MS to distinguish MS patients from healthy controls (Yoo et al, 2018), different disease courses in MS patients (Ion-Margineanu et al, 2017; Zhao et al, 2017) and also to predict conversion in CIS patients. The latter was addressed by (Wottschel et al, 2015), who included clinical and basic lesion features, and by (Kitzler et al, 2018), who analyzed lesion myelination.…”