Appendix E1. Supplementary Methods. MR Image Acquisition. MR Image Analysis

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1 RSNA, /radiol Appendix E1 Supplementary Methods MR Image Acquisition By using a 1.5-T system (Avanto, Siemens Medical, Erlangen, Germany) under a program of regular maintenance (no major imager hardware or software upgrades occurred during the study), the following images of the brain were acquired in all subjects: (a) axial dual-echo fast spin-echo (repetition time msec/echo time msec, 2650/28 113; echo train length, five; number of sections, 50; section thickness, 2.5 mm with no gap; matrix size, ; field of view, mm 2 ), (b) sagittal three-dimensional T1-weighted magnetization-prepared rapid acquisition gradient echo (2000/3.93; inversion time, 1100 msec; number of sections, 208; section thickness, 0.9 mm; matrix size, ; field of view, mm 2 ), (c) axial diffusion-weighted pulsed-gradient spin-echo echo-planar sequence (6400/93; number of sections, 40; section thickness, 2.5 mm with no gap; matrix size, ; field of view, mm 2 ), with diffusion-encoding gradients applied in 12 noncollinear directions (b factor, 1000 sec/mm 2 ; number of averages, 8), and (d) postcontrast (0.1 mmol/kg of gadopentetate dimeglumine; acquisition delay, 5 min) sagittal three-dimensional T1-weighted magnetization-prepared rapid acquisition gradient-echo sequence (with the same parameters as the precontrast sequence). All imaging was performed according to published guidelines (39). Diffusion-tensor MR imaging sequences were performed with the patient positioned in the same orientation as that of the dual-echo sequence, with the central section positioned to match exactly the central section of the dual-echo set. MR Image Analysis By using a local thresholding segmentation technique (Jim 5.0, brain T2 hyperintense, T1 hypointense, and gadolinium-enhancing lesion volumes were measured. In patients with CIS, probability maps of T2 hyperintense and T1 hypointense lesions also were produced. For T2 lesions, binarized masks from T2 lesions were obtained, coregistered to the three-dimensional T1-weighted images (by using the rigid transformation calculated between the T2-weighted and the three-dimensional T1-weighted image), normalized to standard space (with the diffeomorphic anatomic registration using exponentiated lie algebra nonlinear transformation calculated for the voxel-based morphometric analysis), and averaged to obtain T2 lesion probability maps. T1-hypointense lesion probability maps were obtained by averaging binarized masks from T1 lesions quantified on the three-dimensional T1-weighted images. Page 1 of 5

2 Global and Regional Volume Analysis After T1 hypointense lesion refilling (40), normalized brain volumes, GM and WM volumes, and longitudinal percentage of brain volume changes were assessed on three-dimensional T1- weighted images by using SIENAx and SIENA software. Voxel-based morphometric analyses were performed by using SPM8. First, threedimensional T1-weighted images were segmented into GM, WM, and cerebrospinal fluid. Then, GM and WM segmented images of all subjects, in the closest possible rigid-body alignment with each other, were used to produce GM and WM templates and to drive the deformation to the templates. At each iteration, the deformations calculated by using the diffeomorphic anatomic registration through exponentiated lie algebra method were applied to GM and WM, with an increasingly good alignment of subject morphology, to produce templates. The spatially normalized images were then intensity modulated to ensure that the overall amount of each tissue class was not altered by the spatial normalization procedure. To better align the final template with the Montreal Neurologic Institute space, an affine registration between the custom GM template and the statistical parametric mapping GM template (in the Montreal Neurologic Institute space) was also calculated and added to the header of each image as a new orientation, to have all images in a standard space. The same transformation was applied to the custom WM template. The images were then smoothed with an 8-mm full width at half maximum Gaussian kernel. Distribution of Regional Damage to WM (Tract-based Spatial Statistics) at Diffusion-Tensor MR Imaging Diffusion-weighted images were first corrected for distortions caused by eddy currents. Then, by using the FMRIB diffusion toolbox (FSL 4.1), the diffusion tensor was estimated in each voxel by using linear regression (41), and mean, axial, and radial diffusivity and fractional anisotropic maps were derived. Tract-based spatial statistics analysis was used to perform a voxel-wise analysis of the whole-brain WM diffusion-tensor MR imaging measures ( Individual fractional anistropic images were nonlinearly registered to the FMRIB58_FA atlas provided in FSL, and were averaged. To optimize intersubject comparability, this step was performed again by using the populationderived atlas as a target. The resulting mean fractional anistropic image was then thinned to create a WM tract skeleton, which was thresholded at a fractional anistropic value greater than 0.2 to include only WM voxels. The fractional anistropic values lying at the center of tracts were projected onto the fractional anistropic skeleton. The individual registration and projection vectors obtained during this process were also applied to the mean, axial, and radial diffusivity data. Tensor-based Morphometric Analysis Tensor-based morphometry, as implemented in SPM8, was used to map changes of regional GM volumes over time within and between the two study groups. A high-dimensional deformation field was used to warp the follow-up halfway images to match the previous halfway images for each subject (42,43). The volume changes were quantified by taking the determinant of the gradient of deformation at a single-voxel level (Jacobian determinant). The following formula was applied to the segmented GM images obtained from the first examinations and the Jacobian determinant maps: (JV 1) GM, where JV is the Jacobian determinant. The resulting product Page 2 of 5

3 images provide estimates of the GM volume changes between the different time points. These images were normalized to the custom template obtained for VBM analysis by using the deformation fields calculated with diffeomorphic anatomic registration through exponentiated lie algebra. Normalized images were smoothed by using an 8-mm isotropic Gaussian kernel, before entering the statistical analysis. Normalized, smoothed maps of GM over time for each subject were then entered into the statistical analysis. To exclude from the statistical analysis pixels assigned by the segmentation to GM with low probability values and pixels with a low intersubject anatomic overlay after normalization, a GM mask was created by averaging GM normalized maps from all subjects. This mask was thresholded at a value of 0.25 and then used as an explicit mask during the statistical analysis. Regional changes in GM volumes during follow-up were assessed by using the general linear model and the theory of Gaussian random fields (44). The results were assessed including age and gender as nuisance covariates, at a threshold P value of.001, uncorrected, with a cluster extent of 50 voxels. Results were also tested with a P value of.05, FWE corrected for multiple comparisons considered indicative of a significant difference. Some methodologic differences must be taken into account to compare voxel-based and tensor-based morphometric findings. For voxel-based morphometric analysis, which in our study is based on a longitudinal comparison of dependent samples using a defined template, we included the normalization factor derived from SIENAx as a nuisance covariate. Tensor-based morphometric analysis with high-resolution deformation fields (45) takes advantage of the nonlinear within-subject registration to capture within-subject volume changes (46). Since a direct comparison of regional volume within the single subject is performed, a correction for head size is not necessary. Results Table E1 and Figure E1 summarize the results of the GM volume assessment at the different study time points. Those clusters with a P value less than.05, FWE corrected, are marked with an asterisk in Table E1. At 3 months, patients showed an increased volume of the middle frontal gyri, inferior temporal gyri, right superior temporal gyrus, left fusiform gyrus, right hippocampus, and right cerebellum (P <.001 uncorrected) compared with those at baseline. At 12 months, patients experienced a decreased volume of the right caudate nucleus and thalamus, the cerebellum and parietal and temporal regions, bilaterally (P <.001 uncorrected) compared with those at 3 months. No area of GM volume increase was detected (P >.001 uncorrected). At 24 months, a further decrease of GM volume was found in CIS patients in several areas of the frontal, temporal, parietal, and occipital lobes, bilaterally, and the left cerebellum (P <.001, uncorrected) compared with those at 12 months. No area of GM volume increase was detected (P >.001, uncorrected). References 39. Miller DH, Barkhof F, Berry I, Kappos L, Scotti G, Thompson AJ. Magnetic resonance imaging in monitoring the treatment of multiple sclerosis: concerted action guidelines. J Neurol Neurosurg Psychiatry 1991;54(8): Page 3 of 5

4 40. Chard DT, Jackson JS, Miller DH, Wheeler-Kingshott CA. Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 2010;32(1): Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994;103(3): Ashburner J, Andersson JL, Friston KJ. Image registration using a symmetric prior--in three dimensions. Hum Brain Mapp 2000;9(4): Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM. Computerassisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol 2003;2(2): Friston KJ, Holmes AP, Poline JB, et al. Analysis of fmri time-series revisited. Neuroimage 1995;2(1): Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000;11(6 Pt 1): Leow AD, Klunder AD, Jack CR Jr, et al. Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage 2006;31(2): Table E1. GM Modifications by Using Tensor-based Morphometry Time Points, Measures, and Anatomic Regions Month 3 vs baseline GM increase Cluster Extent Montreal Neurologic Institute Coordinates x y z t Values Right middle frontal gyrus Left middle frontal gyrus Right superior temporal gyrus Right inferior temporal gyrus Left inferior temporal gyrus Left fusiform gyrus Right parahippocampal gyrus Right cerebellum crus Right cerebellum 4, GM decrease Month 12 vs month 3 GM increase GM decrease Right caudate nucleus Right thalamus Left postcentral gyrus Right middle temporal gyrus Left inferior temporal gyrus Right supramarginal gyrus Left cerebellum Right cerebellar vermis P Value FWE Corrected Page 4 of 5

5 Right cerebellum crus Left cerebellum crus Month 24 vs month 12 GM increase GM decrease Right middle frontal gyrus Left middle frontal gyrus Right inferior frontal gyrus Right postcentral gyrus Left postcentral gyrus Left precentral gyrus Left inferior temporal gyrus Left superior parietal lobule Right angular gyrus Right fusiform gyrus Right hippocampus Right supramarginal gyrus Left middle occipital gyrus *.01 Left cerebellum crus Note. General linear model includes age and sex as nuisance covariates (P <.001, uncorrected). * P <.05, FWE corrected at a cluster level. Page 5 of 5