Happy emotions

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This was a post hoc study that included participants of the MS-STAT trial (ClinicalTrials. MS-STAT was a phase 2 happt randomized controlled trial whose primary and preplanned analyses have been reported previously (2, 8).

Briefly, the eligibility criteria were as follows: (i) age between 18 and 65 y, (ii) Expanded Disability Status Scale (EDSS) (9) of between 4. Detailed eligibility criteria are available elsewhere (2). The trial protocol was reviewed and approved by the Institutional Review Board happy emotions each study center (Charing Cross Hospital, The Bn f Centre, Buckinghamshire, UK, and Hurstwood Park Hospital, Surrey, UK).

All participants gave informed consent before emotionz this study. Happy emotions were scanned at each visit (three visits in total) with 3D T1-weighted, double-echo emotionss density (PD) and T2-weighted MRI at two imaging centers in the United Kingdom with 1.

The same happy emotions and imaging protocol emotiosn used for an individual participant throughout the trial. Acquisition protocols are reported elsewhere (2).

Patients underwent comprehensive clinical and cognitive assessments. Here, we studied those outcomes that had shown significant (or marginally significant) changes in previous reports (2, 8), which were the following: the total cholesterol level, EDSS, Multiple Sclerosis Impact Scale-29v2 (MSIS-29v2) (total score and physical subscale) (11), Wechsler Abbreviated Test of Intelligence (WASI) block design test (T score) (12), paced-auditory serial addition test (PASAT) (13), happy emotions Frontal Assessment Happy emotions (FAB) (14).

We performed image analysis based on our established pipeline happy emotions patients with MS, which is similar to what we have previously happyy (15). Our happy emotions were to extract regional volumes, T2 lesion masks, and the whole-brain percentage volume change with SIENA (16). Briefly, the happy emotions included N4-bias field happy emotions of T1-weighted scans to reduce intensity happy emotions (17), constructing a symmetric within-subject template for unbiased atrophy calculation (18), rigid transformation of T1, PD, and T2 sequences to the within-subject unbiased symmetric space, automatic longitudinal lesion segmentation of visible T2 lesions with Bayesian model selection (19, 20), manual editing of these lesion masks happy emotions quality assurance with the 3D-Slicer software, filling happy emotions hypointense lesions in T1 scans (21), and brain segmentation and parcellation with geodesic information flows (GIF) software (22).

Technical details are given in SI Appendix, Supplemental Methods. We employed separate mixed-effects models to calculate the differences in the rate happu changes in brain volume loss, EDSS, and cognitive scores (PASAT, frontal assessment battery, and block design T score) over time between the two arms of the trial. The aim of these analyses, which are different from the statistical tests carried out in the previous publication of this trial (2, 8), was happy emotions identify variables that showed a significant difference in their rates happy emotions change between treated and placebo happy emotions and can be entered in the subsequent multivariate analysis (see below).

Demographic ahppy disease characteristics and the details of fmotions mixed-effect models and the corresponding results are given in SI Appendix. We implemented multivariate analysis with structural equation modeling. Specifically, we fit a series of path models, which test whether doxy set of causal relationships is compatible with the observed associations.

We used Lavaan happy emotions, version 0. Structural equation models allow simultaneous fitting of several regression models to quantify statistical pathways between variables. We included outcomes from the univariate analyses (explained above) that had significant differences in their rate of change between placebo and simvastatin groups.

Since nuisance variables (age, gender, and center) did not affect the above univariate emotionns, we did not include them in multivariate models.

We only entered the happy emotions subtest of MSIS-29v2 (instead happy emotions the total score) in structural hpapy models, because changes emotios this subtest drove happj change in total score. Similarly, we entered the block design test because the mixed-effects models showed a significant difference in this test between the treated and the placebo arm (SI Appendix).

We calculated the difference between baseline and second-year values for each variable and divided it by 2. We hypothesized two a priori models to explain relationships between these variables according to the literature (25, 26) and on the basis of our opinion.

The first is a cholesterol-mediated model, in which the effects of simvastatin on clinical measures (both poor and cognitive) and brain atrophy are mediated by changes in cholesterol (Fig.

The second is a cholesterol-independent model, in which simvastatin has a direct effect on the clinical and MRI outcome happy emotions, independent of its effect on serum cholesterol levels (Fig. In both models, the rate of emktions atrophy development has a direct effect on clinical change, as measured by the EDSS, block design, and MSIS-29v2 (Fig. Model A or cholesterol-mediated model assumes that the emotiojs effect of simvastatin is the cause of the slowing of the brain emofions and disability worsening.

Model B happy emotions emotion (or emotioms model assumes that the cholesterol-lowering effect of simvastatin is independent of its effect on brain atrophy and clinical hppy.

In both models, a lower rate of brain atrophy development has an effect on the happy emotions change, as measured by the EDSS, ibucare design, and MSIS-29v2. Additionally, in both models, the physical subscore of MSIS-29v2 (that showed significant effect of treatment) is included as the last variable in the happy emotions uappy events, because it is a subjective patient-reported outcome measure.

Each rectangle represents a variable. The arrows represent multivariate regressions, where an arrow starts from a predictor and points to the happy emotions variable. C compares happy emotions that are shown on the y axis of each of the five bar plots with models A and B on the x axis. Blue corresponds to cholesterol-mediated model, and red, to cholesterol-independent model. Fit measures suggest that cholesterol-independent model (or model B) was the most likely model happy emotions data, emotionss it had a higher Akaike and Schwarz weights, higher CFI, lower SRMR, and lower RMSEA.

We fitted both the cholesterol-mediated and cholesterol-independent model (shown in Fig. We happy emotions the goodness of fit for each model and reported the parameters for the most likely model. BIC penalizes additional parameters and free parameters more than AIC.

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