Whole brain and deep gray matter atrophy detection over 5 years with 3T MRI in multiple sclerosis using a variety of automated segmentation pipelines

Abstract

BackgroundCerebral atrophy is common in multiple sclerosis (MS) and selectively involves gray matter (GM). Several fully automated methods are available to measure whole brain and regional deep GM (DGM) atrophy from MRI.ObjectiveTo assess the sensitivity of fully automated MRI segmentation pipelines in detecting brain atrophy in patients with relapsing-remitting (RR) MS and normal controls (NC) over five years.MethodsConsistent 3D T1-weighted sequences were performed on a 3T GE unit in 16 mildly disabled patients with RRMS and 16 age-matched NC at baseline and five years. All patients received disease-modifying immunotherapy on-study. Images were applied to two pipelines to assess whole brain atrophy [brain parenchymal fraction (BPF) from SPM12; percentage brain volume change (PBVC) from SIENA] and two other pipelines (FSL-FIRST; FreeSurfer) to assess DGM atrophy (thalamus, caudate, globus pallidus, putamen). MRI change was compared by two sample t-tests. Expanded Disability Status Scale (EDSS) and timed 25-foot walk (T25FW) change was compared by repeated measures proportional odds models.ResultsUsing FreeSurfer, the MS group had a ~10-fold acceleration in on-study volume loss than NC in the caudate (mean decrease 0.51 vs. 0.05 ml, p = 0.022). In contrast, caudate atrophy was not detected by FSL-FIRST (mean decrease 0.21 vs. 0.12 ml, p = 0.53). None of the other pipelines showed any difference in volume loss between groups, for whole brain or regional DGM atrophy (all p>0.38). The MS group showed on-study stability on EDSS (p = 0.47) but slight worsening of T25FW (p = 0.054).ConclusionsIn this real-world cohort of mildly disabled treated patients with RRMS, we identified ongoing atrophy of the caudate nucleus over five years, despite the lack of any significant whole brain atrophy, compared to healthy controls. The detectability of caudate atrophy was dependent on the MRI segmentation pipeline employed. These findings underscore the increased sensitivity gained when assessing DGM atrophy in monitoring MS.

Brain atrophy in multiple sclerosis: mechanisms, clinical relevance and treatment options

“…Atrophy in MS is often considered to be the result of extensive axonal transection and demyelination [21][22][23]. The contribution of neuroglia may be less clear; reactive gliosis has the potential to mask considerable tissue loss in WM lesions [24,25].…”

Detection of Cortical and Deep Gray Matter Lesions in Multiple Sclerosis Using DIR and FLAIR at 3T

“…Using the MP‐RAGE images, deep GM (thalamus, caudate, GP, and putamen) brain volumes were measured with the automated FMRIB Software Library (FSL‐FIRST) pipeline (v.6.0, https://fsl.fmrib.ox.ac.uk) using previously described methods 21,22 . Whole brain segmentation was also performed from the MP‐RAGE images to measure intracranial volume (ICV) and brain parenchymal fraction (BPF), a normalized estimate of whole brain atrophy using Statistical Parametric Mapping (v.12, https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) 22 . All raw deep brain volumes were normalized into fractions after dividing them by the ICV 23 .…”

“…21,22 Whole brain segmentation was also performed from the MP-RAGE images to measure intracranial volume (ICV) and brain parenchymal fraction (BPF), a normalized estimate of whole brain atrophy using Statistical Parametric Mapping (v.12, https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ). 22 All raw deep brain volumes were normalized into fractions after dividing them by the ICV. 23 BPF was calculated as the sum of the GM and WM volumes divided by the total ICV (the sum of GM, WM, and CSF volumes).…”

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

“…For example, studies have shown that field strength, [9][10][11] type of pulse sequence, 9,[12][13][14] scanner upgrades, 9 scanner vendor, 9,15 and voxel size 13 may each influence MS-related cerebral lesion and atrophy volumetric measures. Furthermore, the type of postprocessing image segmentation pipeline, 12,13,[16][17][18][19][20] preprocessing image preparation and software version within the same pipeline, 9,21 or workstation operating system 21 may also add variation to the results. Even when using a consistent field strength, scanner vendor, and high-resolution harmonized pulse sequences, there is considerable variability in manual and automated segmentation results 22 partly based on hardware factors such as gradient • C 2020 by the American Society of Neuroimaging nonlinearity.…”