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Background Spatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI – including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms – to evaluate their performance in the presence of MS-related pathologies. Methods 3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests. Results All four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms. Conclusions SPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.

Objective To determine whether comorbid diabetes and hypertension are associated with cognition in multiple sclerosis (MS) after accounting for psychiatric comorbidities. Methods Participants completed a structured psychiatric interview, the Hospital Anxiety and Depression Scale (HADS), a comorbidity questionnaire, and cognitive testing including the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT-II), Brief Visuospatial Memory Test-Revised (BVMT-R), and verbal fluency. Test scores were converted to age-, sex- and education-adjusted z-scores. We evaluated associations between diabetes and hypertension and the four cognitive z-scores using a multivariate linear model, adjusting for comorbid depression and anxiety disorders, psychotropic medications, disease-modifying therapies, smoking status and body mass index. Results Of 111 participants, most were women (82.9%) with relapsing remitting MS (83.5%), of mean (SD) age 49.6 (12.7) years. Comorbidity was common; 22.7% participants had hypertension, 10.8% had diabetes, 9.9% had current major depression, and 9.9% had current anxiety disorders. Mean (SD) z-scores were: SDMT −0.66 (1.15), CVLT-II −0.43 (1.32), BVMT-R −0.49 (1.07) and fluency −0.59 (0.86). Diabetes (p = 0.02) and anxiety disorder (p = 0.02) were associated with cognitive function overall. Diabetes was associated with lower BVMT-R (β = −1.18, p = 0.0015) and fluency (β = −0.63, p = 0.037) z-scores. Anxiety was associated with lower SDMT (β = -1.07, p = 0.0074) z-scores. Elevated anxiety symptoms (HADS-A ≥ 11) were associated with lower z-scores on the SDMT and CVLT-II. Conclusion Comorbidities, including diabetes and anxiety, are associated with cognitive dysfunction in MS. Their presence may contribute to the heterogeneous pattern of impairments seen across individuals and they may represent targets for improved management of cognitive symptoms.

Tissue contrast can be enhanced by dividing T1-weighted (T1w) images by T2-weighted (T2w) images to map the so-called T1w/T2w ratio, which has become an increasingly popular technique for quantifying brain tissue changes associated with neurodevelopment, aging, and a variety of neurodegenerative diseases. However, although it is self-evident that T1w/T2w ratios increase with the amount of T2-weighting in the T2w image – which is determined by the echo time (TE), all else being equal – longer TEs also reduce the signal-to-noise ratio (SNR) of the T2w images, and it is not clear how these SNR characteristics affect the reliability of T1w/T2w measurements. Therefore, the current study systematically investigated how different amounts of T2-weighting affected T1w/T2w measurements in order to determine whether there is an optimal amount of T2-weighting. T1w images were acquired from 10 neurologically healthy adults using a 3D turbo field echo (TFE) sequence, and a series of T2-weighted images were extracted from a multi-echo 3D combined gradient- and spin-echo (GRASE) sequence. Analyses of 12 anatomically defined brain regions revealed that both the mean and standard deviation of the T1w/T2w measurements increased exponentially with TE of the T2w images, and that T2w images with TE ≈ 120–160 ms yielded the most consistent/reproducible contrast between white matter ROIs and the whole-brain T1w/T2w signal. Furthermore, comparisons between T1w/T2w measurements and multi-component T2-relaxation myelin water fractions (MWFs) in the same brain regions revealed that T2w images with TE ≥ 160 ms drastically reduced the degree of correlation between T1w/T2w measurements and MWF. Overall, these findings suggest that: 1) there is a substantial trade-off between increased T1w/T2w contrast (based on longer TEs for the T2w images) and the reliability of quantitative T1w/T2w signals; and 2) the optimal TE for T2w GRASE scans is between 120 ms and 160 ms for calculating T1w/T2w ratios.

Background Few studies have evaluated the association between comorbidities associated with increased vascular risk and brain volume changes in multiple sclerosis (MS). To date, findings have not been consistent with respect to which comorbidities are associated with lower brain volumes or whether comorbidities associated with increased vascular risk are associated with greater brain volume loss over time. Objectives We aimed to evaluate the association between the Framingham Risk Score (FRS) which evaluates vascular risk and normalized whole brain volume in MS. Methods We included 98 participants with MS who underwent two brain MRIs two years apart, from which whole brain volumes were calculated. Each participant reported their comorbidities and medications taken. Blood pressure, height and weight were recorded and we calculated the FRS. We tested the association between the FRS at baseline and brain volume at the second time point using quantile regression adjusting for baseline normalized brain volume, age, gender and use of disease-modifying therapy. Results As the FRS increased, brain volume was lower, both at enrollment (β= -0.24; 95%CI: -0.42, -0.04) and at follow-up (-0.27; 95%CI: -0.45, -0.08). After further adjustment for age, gender, and use of disease modifying therapy, higher FRS remained associated with lower brain volume at follow-up at the 90th percentile of brain volume (β= -2.22; 95%CI: -3.40, -1.04) but not at the 10th or 50th percentiles. Conclusion Higher FRS were associated with lower brain volumes in persons with MS at baseline, and with brain volume loss over time. This effect was most pronounced for persons with higher brain volumes at baseline, which suggests that prevention, detection and effective management of comorbidities associated with vascular risk in people with MS is particularly important early in the disease course.

Although event-related fMRI is able to reliably detect brief changes in brain activity and is now widely used throughout systems and cognitive neuroscience, there have been no previous reports of event-related spinal cord fMRI. This is likely attributable to the various technical challenges associated with spinal fMRI (e.g., imaging a suitable length of the cord, reducing image artifacts from the vertebrae and intervertebral discs, and dealing with physiological noise from spinal cord motion). However, with many of these issues now resolved, the largest remaining impediment for event-related spinal fMRI is a deprived understanding of the spinal cord fMRI signal time course. Therefore, in this study, we used a proton density-weighted HASTE sequence, with functional contrast based on signal enhancement by extravascular water protons (SEEP), and a motion-compensating GLM analysis to (i) characterize the SEEP response function in the human cervical spinal cord and (ii) demonstrate the feasibility of event-related spinal fMRI. This was achieved by applying very brief (1 s) epochs of 22°C thermal stimulation to the palm of the hand and measuring the impulse response function. Our results suggest that the spinal cord SEEP response (time to peak ≈8 s; FWHM ≈4 s; and probably lacking pre- and poststimulus undershoots) is slower than previous estimates of SEEP or BOLD responses in the brain, but faster than previously reported spinal cord BOLD responses. Finally, by detecting and mapping consistent signal-intensity changes within and across subjects, and validating these regions with a block-designed experiment, this study represents the first successful demonstration of event-related spinal fMRI.