Multiple sclerosis (MS) is characterized by progressive gray matter (GM) atrophy that strongly correlates with clinical disability. However, whether localized GM atrophy correlates with specific disabilities in patients with MS remains unknown.
To understand the association between localized GM atrophy and clinical disability in a biology-driven analysis of MS.
Design, Setting, and Participants
In this cross-sectional study, magnetic resonance images were acquired from 133 women with relapsing-remitting MS and analyzed using voxel-based morphometry and volumetry. A regression analysis was used to determine whether voxelwise GM atrophy was associated with specific clinical deficits. Data were collected from June 28, 2007, to January 9, 2014.
Main Outcomes and Measures
Voxelwise correlation of GM change with clinical outcome measures (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite scores).
Among the 133 female patients (mean [SD] age, 37.4 [7.5] years), worse performance on the Multiple Sclerosis Functional Composite correlated with voxelwise GM volume loss in the middle cingulate cortex (P < .001) and a cluster in the precentral gyrus bilaterally (P = .004). In addition, worse performance on the Paced Auditory Serial Addition Test correlated with volume loss in the auditory and premotor cortices (P < .001), whereas worse performance on the 9-Hole Peg Test correlated with GM volume loss in Brodmann area 44 (Broca area; P = .02). Finally, voxelwise GM loss in the right paracentral lobulus correlated with bowel and bladder disability (P = .03). Thus, deficits in specific clinical test results were directly associated with localized GM loss in clinically eloquent locations.
Conclusions and Relevance
These biology-driven data indicate that specific disabilities in MS are associated with voxelwise GM loss in distinct locations. This approach may be used to develop disability-specific biomarkers for use in future clinical trials of neuroprotective treatments in MS.
Progressive brain atrophy is a well-known feature of multiple sclerosis (MS) and is considered an indicator of irreversible tissue damage, primarily in gray matter (GM) but also in white matter (WM).1-3 Gray matter atrophy occurs in the earliest stages of MS,4-7 develops faster than WM atrophy,8,9 and is more strongly correlated with physical disability and cognitive impairment than T1-weighted gadolinium-enhancing lesions or T2-weighted lesions.1,5,6,10-13 Although MS is characterized by WM lesions, GM atrophy is considered one of the most relevant markers of permanent disability in MS.14-16
Cognitive disability is of particular interest because cognitive impairment occurs in approximately 40% to 70% of patients with MS and affects them personally, socially, and professionally.17-19 Gray matter atrophy has been associated with cognitive disability in MS20-22; specifically, cognitive impairment in relapsing-remitting MS (RRMS) was better explained by brain atrophy than by T2-weighted lesion volume.23 This explanation appears to be true for RRMS and benign MS, suggesting a silent progression of cognitive impairment independent of standard measures of the clinical course of MS.24 In particular, cortical atrophy has been suggested as one of the major underlying causes of cognitive impairment.10,22,25 Consistent with this suggestion, several pathologic26,27 and magnetic resonance imaging (MRI)4,6,28 studies have shown that the cerebral cortex is affected in MS.
Voxel-based morphometry (VBM) is a well-established and well-validated image analysis technique29-31 and provides an unbiased and comprehensive assessment of anatomical differences throughout the brain with high local specificity. If brain atrophy is not homogenous in MS, then regional measures may provide more sensitive indices of change and localize that atrophy. Indeed, regional measures of brain atrophy may better define the nature of the pathologic mechanism underlying MS disability. Voxel-based morphometry has been used extensively in the analysis of brain atrophy in MS.32-37 However, few studies have evaluated the association between voxelwise GM atrophy and disability,38,39 despite extensive reporting of the association of whole-GM atrophy or hypothesis-driven substructure atrophy with clinical disability.3,9,40,41 The voxelwise approach has the advantage of being purely biology driven and capable of identifying new brain regions not previously considered in MS.
Disease-specific brain atlases store multimodal imaging data from a specific subpopulation.42-44 In this cross-sectional study, we created disability-specific brain atlases representing regions of GM loss associated with deficits in specific clinical test results. The clinical test data and MR images were collected from 158 women with RRMS at their baseline pretreatment evaluation as part of a recently completed, multicenter estriol treatment trial.45 We hypothesized that worse performance on specific clinical tests would be associated with worse focal GM atrophy not necessarily limited to a previously described neuroanatomical structure.
Box Section Ref ID
Question Is there an association between specific clinical deficits in multiple sclerosis and localized gray matter atrophy?
Findings Instead of choosing regions of interest a priori, a biology-driven approach revealed that poor performance on specific clinical tests correlated with localized gray matter atrophy in clinically eloquent regions.
Meaning Specific disabilities are associated with gray matter loss in distinct locations.
All study patients were enrolled in the phase 2 clinical trial of combination treatment with glatiramer acetate (Copaxone) and estriol (clinicaltrials.gov trial identifier NCT00451204)46; enrollment criteria have been described previously.45 Briefly, women aged 18 to 50 years with the diagnosis of RRMS and active relapsing disease within the previous 24 months were eligible for inclusion. Patients were untreated with a 3-month washout of all other disease-modifying treatments or had been treated for less than 2 months with glatiramer acetate. Patients were enrolled at 16 sites throughout the United States. The present ad hoc study was approved by the institutional review board of the University of California, Los Angeles. All patients provided written informed consent.
Data were collected from June 28, 2007, to January 9, 2014. All enrolled patients underwent a neurologic examination and cognitive and behavioral testing consisting of the Expanded Disability Status Scale (EDSS) (range, 0 to 5.5, with higher scores indicating greater disability)47 and the Multiple Sclerosis Functional Composite (MSFC) (range, −1.96 to 1.33, with higher scores indicating better performance and less disability),48 which included the 9-Hole Peg Test (9-HPT) (range, 13.4-54.4, with longer times indicating worse performance), the Timed 25-Foot Walk (T25-FW) (range, 2.7-10.4, with higher scores indicating worse performance), and the Paced Auditory Serial Addition Test at 3 seconds (PASAT3) (range, 26-60, with higher scores indicating better performance) and 2 seconds (PASAT2) (range, 18-60, with higher scores indicating better performance). We collected high-resolution T1-weighted and fluid-attenuated inversion recovery (FLAIR) MR images from all patients. Only the baseline clinical tests and MRIs at enrollment (month 0) were used for this analysis. The MR images needed to pass stringent quality control to ensure sufficient image contrast to produce high-quality tissue segmentations. Of the original 158 patients from 16 sites, a total of 133 patients from 13 sites met this quality control criterion for VBM analysis. An overview of included and excluded patients is given in Table 1. We found no significant differences in any outcome measures between patients included vs excluded from this analysis.
Image Acquisition and Processing
We acquired the MRI data using T1-weighted sequences with and without gadolinium-based contrast agent and a FLAIR sequence to detect WM lesions. Brain images were processed in MATLAB software (version R2011a; MathWorks) and examined using statistical parametric mapping (SPM8; Wellcome Department of Cognitive Neurology) and the VBM8 toolbox extension as described previously.37,49 In brief, we manually delineated and subsequently inpainted WM lesions in the native T1-weighted scans to prevent a possible confound of WM lesions on tissue segmentation.37,50,51 The inpainted images were classified as GM, WM, or cerebrospinal fluid and registered to MNI (Montreal Neurological Institute) space using linear and nonlinear transformations. The tissue segmentation algorithm accounted for partial volume effects52 and was based on adaptive maximum a posteriori estimations53; a spatially adaptive, nonlocal means denoising filter54; and a hidden Markov random field model.55 These methods made the tissue classification independent of the tissue probability maps and thus additionally minimized the influence of misclassifications, lesions, and altered geometry.37,51
We used the resulting GM segments to calculate the global and local GM volumes for the statistical analyses. Whole-brain GM volumes were computed as the sum of all voxelwise volumes within a segment. The GM segments were spatially normalized to the DARTEL template56 supplied with the VBM8 toolbox and modulated for nonlinear components of the transformation. This process resulted in voxelwise comparability between the patients while correcting for differences in whole-brain size. Spatially normalized GM segments were smoothed with a gaussian kernel (8-mm full width half maximum) and a mean template was created from the normalized brain images of all patients.
We summarized patient characteristics using descriptive statistics. We compared included and excluded patients using the χ2 test for categorical variables and unpaired 2-tailed t test for continuous variables (or Wilcoxon rank sum test when needed). Overall, 133 patients had EDSS scores and 129 patients had PASAT2 scores. The 122 patients who were right-handed were included in the 9-HPT analysis to avoid the handedness confound.
We assessed associations of voxelwise GM volumes with the MSFC, PASAT3, PASAT2, 9-HPT, or T25-FW scores using voxelwise regression analyses within the general linear model. Associations between voxelwise GM volumes and EDSS scores were assessed by evaluating group differences between patients above and below the median disability score (EDSS score, 2.0) using unpaired 2-tailed t tests within the general linear model. We assessed the associations between voxelwise GM volumes and the individual EDSS functional system subscores by evaluating group differences between patients with and without disability using unpaired 2-tailed t tests within the general linear model. All analyses were adjusted for age, disease duration, number of relapses within the previous 12 months, scanner, and MRI WM measures (volume of FLAIR lesions, volume of enhancing lesions, and number of enhancing lesions). The VBM results were corrected for voxelwise multiple comparisons by controlling the familywise error on the cluster level57 at P = .05, using a cluster-forming threshold of P = .001. We applied a correction for nonstationarity to ensure valid results.58 For visualization, the corrected results were subsequently overlaid on the mean template and maximum-intensity projections were generated. In addition, we brought significance clusters to the MNI single-subject space to enable comparisons with the underlying anatomy using the Anatomy SPM toolbox.59
We assessed the correlations between whole-GM volumes and the MSFC, PASAT3, PASAT2, 9-HPT, T25-FW, and EDSS scores and EDSS functional system subscores using the Spearman rank correlation. Analyses used SAS/STST software (SAS Institute, Inc). A heat map and correlation clustering were created using the gplots package in R software (http://www.R-project.org). Owing to the relatively small sample size in this exploratory study, the α level was only adjusted for multiple comparisons in the voxelwise analyses, not in the whole-GM analyses.
Composite Measures, MSFC, and EDSS
The final study sample included 133 women with RRMS (mean [SD] age, 37.4 [7.5] years). We observed a significant direct correlation between the MSFC scores and voxelwise GM volume. Because lower MSFC scores indicate worse performance, this result indicated that worse MSFC performance correlated with localized GM loss. A significance cluster in the middle cingulate cortex reached into Brodmann area (BA) 660 and medial superior parietal area 5M (P < .001).61 The second significance cluster with a maximum in the right middle frontal gyrus extended posteriorly to the precentral gyrus without reaching BA 6 and 4 (P = .004).60 Contralaterally, a third significance cluster extended from the Rolandic operculum over the precentral gyrus without reaching BA 6 and 460 and terminating in BA 44 (P = .009).62 The maximum intensity projections and renderings are depicted in Figure 1. In contrast to the MSFC findings, we observed no significant correlations between voxelwise GM volumes and EDSS scores.
Individual Clinical Tests
We observed a significant direct correlation between PASAT2 scores and voxelwise GM volume. Because lower PASAT2 scores indicated worse performance, this result indicated that worse PASAT2 performance correlated with localized GM loss. A significance cluster was located in the left primary auditory cortex Te1.063 and extended to the left superior temporal gyrus, the left posterior insula (Ig1),64 and the left inferior parietal lobe (P < .001). A significance cluster was also located in the right primary auditory cortex Te1.0 and extended onto the right posterior insula (Ig1) and the right inferior parietal lobe (P = .008). The significance cluster in the left precentral gyrus was located in BA 6 (P = .04).60 Maximum intensity projections, a coronal section, and a rendering for visualization are depicted in Figure 2A.
We observed a significant inverse correlation between 9-HPT time and voxelwise GM volume. Because longer 9-HPT times indicated worse performance, this result indicated that worse performance correlated with localized GM loss. The cluster was located in the left inferior frontal gyrus, specifically BA 44 (P = .02).62 The cluster extended slightly in the occipital direction into the inferior frontal junction. Maximum intensity projections and a coronal section for visualization are depicted in Figure 2B.
Patients with bowel and bladder disability (EDSS functional system subscore >0) had significantly less GM within the right paracentral lobulus (P = .03) and the left anterior-basal insula (P = .04). Maximum intensity projections and coronal sections for visualization are depicted in Figure 2C.
We observed no significant correlations between voxelwise GM volumes and the PASAT3 and T25-FW findings. Furthermore, we observed no significant correlations between voxelwise GM volumes and any of the other EDSS functional system scores.
We calculated whole-GM volumes for all of the patients and performed regression analyses with clinical measures (Table 2). We found statistically significant correlations between whole-GM volume and the MSFC, 9-HPT, PASAT2, PASAT3, and T25-FW scores. We did not observe statistical significance in the correlation between whole-GM volume and EDSS; however, the bowel and bladder and the cerebellar functional system subscores were significantly correlated with whole-GM volumes.
Heat Map and Correlation Clustering
Correlation clustering is a way of visualizing a large, complex data set in a manageable fashion that highlights elements that correlate with each other (Figure 3). We observed close clustering of whole-GM volumes with PASAT2, PASAT3, and MSFC scores. Strong negative correlations between the MSFC and the T25-FW and 9-HPT scores were as expected because increased times to complete the T25-FW and 9-HPT indicate worse disability and contribute to a lower MSFC score.
We have observed localized GM loss associated with deficits in specific clinical test results occurring in clinically eloquent regions—areas consistent with functions associated with their respective clinical measures. This observation of discrete GM losses that differ from one clinical disability to another is consistent with our overall hypothesis that, as disabilities differ, patterns of localized GM atrophy differ accordingly.
Auditory function is a critical component of the PASAT2; therefore, the strong correlation between PASAT2 performance and GM volume in the primary auditory cortex bilaterally is of particular interest. Furthermore, we found a strong correlation between PASAT2 performance and GM volume in the left precentral gyrus (BA 6). Neuroimaging studies have demonstrated that BA 6 is involved in nonmotor mental operation tasks, with one report65 using positron emission tomography to visualize activity in BA 6 during numerical, verbal, and spatial tasks and the other66 using functional MRI to visualize activity in BA 6 during spatial and verbal mental operations. With that said, our findings are not consistent with those of a previous report demonstrating significance clusters for correlations between GM volume and PASAT2 scores in the orbitofrontal cortex.39 The difference in our observations may be owing to differences in patient populations and/or analysis methods. We had an all-female population with RRMS and a mean disease duration of 3 years, whereas the previous study had a mixed-sex population that included healthy participants and patients with MS and a mean disease duration of 7 years. Further, we used high- instead of low-dimensional nonlinear registration and lesion in-painting, as recommended.37,51
Unexpectedly, the PASAT3 did not show a correlation with localized GM atrophy. We hypothesize that this finding is owing to the smaller continuum of disability in the PASAT3 than in the PASAT2. The range of PASAT3 scores was small, with 73 of 133 of the patients showing no functionally significant disability (PASAT3 score 55-60), thus limiting the domain of the regression analysis.
The 9-HPT involves very precise hand and arm movements; although left BA 44 (ie, part of the Broca area) is primarily associated with the production of speech (verbal communication), it is also involved in specific, coherent activation of the motor system, particularly arm and hand motions (theoretically gestural communication).67-69 Our findings emerged from the patient MRI scans without a priori hypotheses or preconceptions about affected areas, which makes them even more compelling. In essence, these biology-driven rather than hypothesis-driven results reveal a new area of GM loss associated with a common disability in MS, namely, performance on the 9-HPT.
Patients with MS and EDSS bladder and bowel functional system subscores greater than 0 showed a significance cluster of GM loss in the right paracentral lobule. The cluster occupies a region of the cerebral cortex that receives somatosensory information to the genital area and has been shown to be associated with defecation and micturition.70-72
The MSFC is a combination of tests that involve multiple separate neurologic systems, including the sensory, motor, coordination, attention, and cognition systems. Despite the variety of systems involved, a number of large clusters had voxelwise GM volume correlated with the MSFC score. The MSFC clusters had some overlap with those observed in the PASAT2 and 9-HPT results, although this overlap was not complete. This result was a surprise because we expected that composite measures of distinct clinical disabilities would not correlate well with voxelwise GM loss. On the other hand, we did not find any correlation between the EDSS scores and localized GM loss, consistent with previous reports.38,73 We hypothesize that this finding may be owing to the fact that the EDSS includes more subscales, many of which are minimally affected in many patients, resulting in greater heterogeneity of disabilities. Further, and perhaps more importantly, the MSFC subtests (and transitively the MSFC) are objective measures with substantial dynamic ranges, whereas the EDSS and its functional system subscores are subjective assessments and have a limited dynamic range. Our data suggest that objective clinical outcome measures are better suited for this kind of analysis, as are continuous (rather than dichotomous or discrete) measures.
We observed correlations between whole-GM volume and the MSFC and its component tests, consistent with previous reports.12,13,40 We did not observe a statistically significant correlation between whole-GM volumes and the EDSS scores, although we found an inverse correlation between whole-GM volumes and the EDSS bowel and bladder functional system subscore and between GM volumes and the EDSS cerebellar functional system subscore (less GM volume is correlated with more disability). Although several studies have reported that GM atrophy is associated with EDSS scores,12,40 others have not found such a correlation.73,74 The difference in our observations again may be owing to differences in our patient populations.
Our study includes some limitations. The cohort we analyzed consisted of untreated female patients with RRMS. If disease-modifying treatments were used, if the patient population was male, or if patients with progressive MS were examined, the findings may have been different. Although our patient population was larger than those of previous VBM studies, it is by no means representative of all patients with MS, and future studies with even larger patient populations may demonstrate more subtle effects. In addition, this approach could be applied to any number of other clinical assessments beyond the MSFC components. With that said, the results of some assessments may be masked by the complex, multisystem nature of some tasks or by the brain’s inherent plasticity.
Further studies warrant a longitudinal analysis to correlate longitudinal change in a specific disability with longitudinal change in voxelwise GM atrophy. The insights gained may be important in identifying new biomarkers for use in future trials that focus on neuroprotective treatments targeting those specific disabilities because specific areas of GM loss could be tracked longitudinally for responsiveness to treatment over time.
Previous reports3,9,40,41 have found strong correlations between GM atrophy and clinical disability in MS; however, few studies have focused on the association between localized GM atrophy and specific disabilities.39 In this cross-sectional study, we have demonstrated that specific disabilities in patients with MS are associated with voxelwise GM volume loss in clinically eloquent regions. These findings are biology driven rather than hypothesis driven, emerged from the MRI scans, and unveiled GM atrophy associated with common disabilities observed in MS. Identification of such regions could lead to further investigation of their longitudinal change as a biomarker in a trial targeting treatment of the disability served by that region.
Corresponding Author: Allan MacKenzie-Graham, PhD, Department of Neurology, David Geffen School of Medicine at University of California, Los Angeles, 635 Charles Young Dr S, Ste 225-Z, Los Angeles, CA 90095 (firstname.lastname@example.org).
Accepted for Publication: March 10, 2016.
Published Online: June 13, 2016. doi:10.1001/jamaneurol.2016.0966.
Author Contributions: Dr MacKenzie-Graham had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: MacKenzie-Graham, Kurth, Voskuhl.
Acquisition, analysis, or interpretation of the data: MacKenzie-Graham, Kurth, Itoh, Wang, Montag, Elashoff.
Drafting of the manuscript: MacKenzie-Graham, Kurth, Itoh, Wang.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Kurth, Itoh, Wang, Elashoff.
Obtained funding: MacKenzie-Graham, Voskuhl.
Administrative, technical, or material support: MacKenzie-Graham, Montag, Voskuhl.
Study supervision: MacKenzie-Graham, Voskuhl.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was supported by grant 20130231 from the Conrad N. Hilton Foundation, grant RO1NS051591 from the National Institutes of Health (NIH) (Dr Voskuhl), grant RG3915 from the National Multiple Sclerosis Society (Dr Voskuhl), the Brain Mapping Medical Research Organization, Brain Mapping Support Foundation, Pierson-Lovelace Foundation, The Ahmanson Foundation, Capital Group Companies Charitable Foundation, William M. and Linda R. Dietel Philanthropic Fund, Northstar Fund, and awards C06RR012169, C06RR015431, and S10OD011939 from the National Center for Research Resources and the Office of the Director of the NIH.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
DH. Brain atrophy in clinically early relapsing-remitting multiple sclerosis. Brain
. 2002;125(pt 2):327-337.PubMedGoogle ScholarCrossref
AC. Focal cortical atrophy in multiple sclerosis: relation to lesion load and disability. Neuroimage
. 2007;34(2):509-517.PubMedGoogle ScholarCrossref
R. Gray matter involvement in multiple sclerosis. Neurology
. 2007;68(9):634-642.PubMedGoogle ScholarCrossref
et al. Cortical atrophy is relevant in multiple sclerosis at clinical onset. J Neurol
. 2007;254(9):1212-1220.PubMedGoogle ScholarCrossref
et al. Early development of multiple sclerosis is associated with progressive grey matter atrophy in patients presenting with clinically isolated syndromes. Brain
. 2004;127(pt 5):1101-1107.PubMedGoogle ScholarCrossref
et al. Evidence of early cortical atrophy in MS: relevance to white matter changes and disability. Neurology
. 2003;60(7):1157-1162.PubMedGoogle ScholarCrossref
M. Evidence for progressive gray matter loss in patients with relapsing-remitting MS. Neurology
. 2005;65(7):1126-1128.PubMedGoogle ScholarCrossref
et al. Progressive grey matter atrophy in clinically early relapsing-remitting multiple sclerosis. Mult Scler
. 2004;10(4):387-391.PubMedGoogle ScholarCrossref
RA. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol
. 2008;64(3):255-265.PubMedGoogle ScholarCrossref
et al. Neocortical volume decrease in relapsing-remitting MS patients with mild cognitive impairment. Neurology
. 2004;63(1):89-93.PubMedGoogle ScholarCrossref
et al. Focal thinning of the cerebral cortex in multiple sclerosis. Brain
. 2003;126(pt 8):1734-1744.PubMedGoogle ScholarCrossref
R. The relationship between whole brain volume and disability in multiple sclerosis: a comparison of normalized gray vs white matter with misclassification correction. Neuroimage
. 2005;26(4):1068-1077.PubMedGoogle ScholarCrossref
R. Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology
. 2006;66(5):685-692.PubMedGoogle ScholarCrossref
R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol
. 2006;5(2):158-170.PubMedGoogle ScholarCrossref
et al. Eight-year follow-up study of brain atrophy in patients with MS. Neurology
. 2002;59(9):1412-1420.PubMedGoogle ScholarCrossref
van den Elskamp
et al. Cerebral atrophy as outcome measure in short-term phase 2 clinical trials in multiple sclerosis. Neuroradiology
. 2010;52(10):875-881.PubMedGoogle ScholarCrossref
E. Multiple sclerosis–related cognitive changes: a review of cross-sectional and longitudinal studies. J Neurol Sci
. 2006;245(1-2):41-46.PubMedGoogle ScholarCrossref
F. Cognitive dysfunction in multiple sclerosis, II: impact on employment and social functioning. Neurology
. 1991;41(5):692-696.PubMedGoogle ScholarCrossref
et al. Evidence for a direct association between cortical atrophy and cognitive impairment in relapsing-remitting MS. Neuroimage
. 2006;30(3):891-898.PubMedGoogle ScholarCrossref
F. Frontal cortex atrophy predicts cognitive impairment in multiple sclerosis. J Neuropsychiatry Clin Neurosci
. 2002;14(1):44-51.PubMedGoogle ScholarCrossref
R. Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol
. 2004;61(2):226-230.PubMedGoogle ScholarCrossref
MA. Brain atrophy as a marker of cognitive impairment in mildly disabling relapsing-remitting multiple sclerosis. Eur J Neurol
. 2008;15(10):1091-1099.PubMedGoogle ScholarCrossref
et al. Differential cognitive impairment for diverse forms of multiple sclerosis. BMC Neurosci
. 2006;7:39.PubMedGoogle ScholarCrossref
et al. Independent contributions of cortical gray matter atrophy and ventricle enlargement for predicting neuropsychological impairment in multiple sclerosis. Neuroimage
. 2007;36(4):1294-1300.PubMedGoogle ScholarCrossref
et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain
. 2005;128(pt 11):2705-2712.PubMedGoogle ScholarCrossref
BD. Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol
. 2001;50(3):389-400.PubMedGoogle ScholarCrossref
M, De Stefano
et al. Detection of cortical inflammatory lesions by double inversion recovery magnetic resonance imaging in patients with multiple sclerosis. Arch Neurol
. 2007;64(10):1416-1422.PubMedGoogle ScholarCrossref
C. Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. Neuroimage
. 2004;23(1):17-20.PubMedGoogle ScholarCrossref
et al. Voxel-wise assessment of progression of regional brain atrophy in relapsing-remitting multiple sclerosis. J Neurol Sci
. 2009;282(1-2):55-60.PubMedGoogle ScholarCrossref
et al. Spatiotemporal distribution pattern of white matter lesion volumes and their association with regional grey matter volume reductions in relapsing-remitting multiple sclerosis. Hum Brain Mapp
. 2010;31(10):1542-1555.PubMedGoogle ScholarCrossref
et al. Association of regional gray matter volume loss and progression of white matter lesions in multiple sclerosis: a longitudinal voxel-based morphometry study. Neuroimage
. 2009;45(1):60-67.PubMedGoogle ScholarCrossref
et al. A voxel-based morphometry study of grey matter loss in MS patients with different clinical phenotypes. Neuroimage
. 2008;42(1):315-322.PubMedGoogle ScholarCrossref
et al. Gray- and white-matter changes 1 year after first clinical episode of multiple sclerosis: MR imaging. Radiology
. 2010;257(2):448-454.PubMedGoogle ScholarCrossref
et al. Neuroprotective effects of testosterone treatment in men with multiple sclerosis. Neuroimage Clin
. 2014;4:454-460.PubMedGoogle ScholarCrossref
O. Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: an in vivo study with TBSS and VBM. Hum Brain Mapp
. 2009;30(9):2852-2861.PubMedGoogle ScholarCrossref
et al. Assessing the correlation between grey and white matter damage with motor and cognitive impairment in multiple sclerosis patients. PLoS One
. 2013;8(5):e63250.PubMedGoogle ScholarCrossref
et al. Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol
. 2008;64(3):247-254.PubMedGoogle ScholarCrossref
F. Shifting imaging targets in multiple sclerosis: from inflammation to neurodegeneration. J Magn Reson Imaging
. 2012;36(1):1-19.PubMedGoogle ScholarCrossref
et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci
. 2001;356(1412):1293-1322.PubMedGoogle ScholarCrossref
et al. Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas. Neuroimage
. 2005;26(4):1009-1018.PubMedGoogle ScholarCrossref
et al. Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cereb Cortex
. 2001;11(1):1-16.PubMedGoogle ScholarCrossref
et al. Estriol combined with glatiramer acetate for women with relapsing-remitting multiple sclerosis: a randomised, placebo-controlled, phase 2 trial. Lancet Neurol
. 2016;15(1):35-46.PubMedGoogle ScholarCrossref
JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology
. 1983;33(11):1444-1452.PubMedGoogle ScholarCrossref
et al. Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain
. 1999;122(pt 5):871-882.PubMedGoogle ScholarCrossref
AW. Why sex matters: brain size independent differences in gray matter distributions between men and women. J Neurosci
. 2009;29(45):14265-14270.PubMedGoogle ScholarCrossref
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):223-228.PubMedGoogle ScholarCrossref
et al. The impact of lesion in-painting and registration methods on voxel-based morphometry in detecting regional cerebral gray matter atrophy in multiple sclerosis. AJNR Am J Neuroradiol
. 2012;33(8):1579-1585.PubMedGoogle ScholarCrossref
A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage
. 2004;23(1):84-97.PubMedGoogle ScholarCrossref
JL. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans Med Imaging
. 1997;16(2):176-186.PubMedGoogle ScholarCrossref
M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging
. 2010;31(1):192-203.PubMedGoogle ScholarCrossref
JP. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Trans Med Imaging
. 2005;24(12):1548-1565.PubMedGoogle ScholarCrossref
CD. Detecting activations in PET and fMRI: levels of inference and power. Neuroimage
. 1996;4(3, pt 1):223-235.PubMedGoogle ScholarCrossref
TE. Nonstationary cluster-size inference with random field and permutation methods. Neuroimage
. 2004;22(2):676-687.PubMedGoogle ScholarCrossref
et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage
. 2005;25(4):1325-1335.PubMedGoogle ScholarCrossref
et al. Two different areas within the primary motor cortex of man. Nature
. 1996;382(6594):805-807.PubMedGoogle ScholarCrossref
K. Observer-independent cytoarchitectonic mapping of the human superior parietal cortex. Cereb Cortex
. 2008;18(4):846-867.PubMedGoogle ScholarCrossref
K. Broca’s region revisited: cytoarchitecture and intersubject variability. J Comp Neurol
. 1999;412(2):319-341.PubMedGoogle ScholarCrossref
K. Human primary auditory cortex: cytoarchitectonic subdivisions and mapping into a spatial reference system. Neuroimage
. 2001;13(4):684-701.PubMedGoogle ScholarCrossref
K. Cytoarchitecture and probabilistic maps of the human posterior insular cortex. Cereb Cortex
. 2010;20(6):1448-1461.PubMedGoogle ScholarCrossref
et al. The role of rostral Brodmann area 6 in mental-operation tasks: an integrative neuroimaging approach. Cereb Cortex
. 2002;12(11):1157-1170.PubMedGoogle ScholarCrossref
N. Modality-specific cognitive function of medial and lateral human Brodmann area 6. J Neurosci
. 2005;25(2):496-501.PubMedGoogle ScholarCrossref
H. A fronto-parietal circuit for object manipulation in man: evidence from an fMRI-study. Eur J Neurosci
. 1999;11(9):3276-3286.PubMedGoogle ScholarCrossref
RE. The preparation, execution and suppression of copied movements in the human brain. Exp Brain Res
. 1998;120(3):386-398.PubMedGoogle ScholarCrossref
V. Neurophysiological mechanisms underlying the understanding and imitation of action. Nat Rev Neurosci
. 2001;2(9):661-670.PubMedGoogle ScholarCrossref
G. A PET study on cortical and subcortical control of pelvic floor musculature in women. J Comp Neurol
. 1997;389(3):535-544.PubMedGoogle ScholarCrossref
I. Cerebral activation during micturition in normal men. Brain
. 2000;123(pt 4):781-789.PubMedGoogle ScholarCrossref
et al. Grey matter loss in relapsing-remitting multiple sclerosis: a voxel-based morphometry study. Neuroimage
. 2006;29(3):859-867.PubMedGoogle ScholarCrossref
DL. Brain atrophy in relapsing-remitting multiple sclerosis: fractional volumetric analysis of gray matter and white matter. Radiology
. 2001;220(3):606-610.PubMedGoogle ScholarCrossref