You are here
Pallidal and caudate volumes correlate with walking function in multiple sclerosis
Journal of the Neurological Sciences, 1-2, 354, pages 33 - 36
Walking dysfunction is common in multiple sclerosis (MS). The thalamus and basal ganglia seemingly have important associations with walking performance. The contribution of these subcortical gray matter (SGM) structures for walking dysfunction is poorly understood in MS.
This study examined associations among volumes of the thalamus and basal ganglia with walking outcomes in MS.
We enrolled 61 MS patients who underwent brain MRI and completed the 6-minute walk (6 MW) and timed 25-foot walk (T25FW). Volumes of the thalamus, caudate, putamen, and pallidum as well as whole-brain white matter (WM) and gray matter (GM) were calculated from 3D T1-weighted structural brain images. We examined associations using bivariate correlations (r) and partial correlations (pr) that controlled for age, MS clinical course, and whole-brain WM and GM volumes. We further performed hierarchical linear regression (HLR) for identifying the strongest SGM correlate of walking performance.
The 6 MW and T25FW correlated significantly with volumes of the thalamus (r's = .382 & .383), caudate (r's = .388 & .416), pallidum (r's = .457 & .457), and putamen (r's = .258 & .293) in bivariate correlations. The 6 MW and T25FW remained significantly correlated with caudate (pr's = .243 & .312) and pallidum (pr's = .321 & .345) volumes in partial correlations. Pallidum volume was the strongest SGM correlate of 6 MW (β = .39) and T25FW (β = .40) performance in HLR.
We provide novel evidence of possible SGM structures, particularly the pallidum and perhaps caudate, as correlates of walking performance in MS.
- Walking dysfunction is common in multiple sclerosis (MS).
- Thalamus and basal ganglia volumes might correlate with walking dysfunction in MS.
- Pallidum and caudate had independent association with walking outcomes in MS.
- Pallidal volume was the strongest correlate of walking performance in MS.
Keywords: Multiple sclerosis, Walking, Thalamus, Basal ganglia, MRI, Imaging.
Walking is one of the most valued functions among persons with multiple sclerosis (MS) and , and upwards of 75% of persons with this disease report mobility and walking problems  and . The dysfunction of walking is present early in MS  and worsens with disease and disability progression, , and .
To date, there has been limited research on subcortical gray matter (SGM) pathology as correlates of walking dysfunction in MS, , and . The thalamus and basal ganglia nuclei, in particular, represent SGM structures within the central nervous system that are important for motor control  and demonstrate atrophy in MS  . We focused on these nuclei considering the importance of the cortico-striato-pallido-thalamo-cortical circuit for controlling motor function  . Atrophy of the thalamus and basal ganglia nuclei of the caudate, putamen, and pallidum further has been associated with walking speed and motor function in older adults and , yet such associations are poorly understood in MS.
This study examined associations among volumes of the thalamus and basal ganglia nuclei with walking performance in persons with MS. We expected that volumes of thalamic and basal ganglia nuclei would be associated with walking outcomes, and that the associations would be independent of age, MS clinical course, and whole-brain white matter (WM) and gray matter (GM) volumes. We further explored the SGM structures most strongly associated with walking performance in MS.
The methods for this study were approved by an IRB, and all participants provided written informed consent. We enrolled 63 patients with clinically definite MS  . The sample was primarily female (n = 47, 77%) with a mean age of 50.0 (SD = 9.1) years, and the clinical course was relapsing–remitting MS (RRMS) in 46 patients (75%) and progressive (either primary or secondary) in the other 15 patients (25%). The mean disease duration of those with MS was 11.4 (SD = 8.3) years, and the median Expanded Disability Status Scale score was 5.5 (IQR = 3.0). All participants with MS were receiving an ongoing disease modifying therapy, but none were currently receiving short-course medications for ongoing relapse.
2.2. Walking performance
We included two walking performance outcomes based on psychometric criteria outlined in recent reviews, , and , and the outcomes were administered by personnel who were not involved in the MRI acquisition or analyses.
6 MW. The six-minute walk (6 MW) was included as a measure of walking endurance and administered according to standardized instructions for persons with MS and . Participants were instructed to walk as fast and as far as possible for 6 min in a single 75-foot long corridor with 180° turns marked by plastic cones. One experimenter followed approximately 3 ft behind the participant with a measuring wheel (Stanley MW50, New Briton, CT) and recorded the total distance traveled in feet.
T25FW. The timed 25-foot walk (T25FW) was administered as a measure of walking speed  . Briefly, participants underwent two trials of walking as fast as possible over a 25-foot course. The primary outcome of the T25FW was mean walking speed (ft/s) over the two trials (i.e., T25FW speed). This outcome measure is more normally distributed than the mean T25FW time  .
2.3. MRI acquisition and analysis of SGM structures
The MRI acquisition and analysis was conducted by personnel who were not involved in the administration of walking measures. Using a whole body Siemens Trio 3 Tesla MRI scanner (Erlangen, Germany), we acquired high-resolution 3D T1-weighted structural brain images using an MPRAGE sequence with the following parameters: 23 cm FOV, 256 × 256 × 192 matrix size with 0.9 mm isotropic resolution, echo time (TE)/repetition time (TR)/inversion time (TI) of 2.32/1900/900 ms, flip angle of 9°, GRAPPA accelerated factor of 2 with 24 reference lines. We then extracted the brain and excluded the skull by deleting non-brain tissue from our T1 whole-brain images using the Brain Extraction Tool  from FMRIB's Software Library. We used bias field correction in order to ensure accurate brain extraction. Next, we segmented the 3D T1 image into 3 tissue types, white matter, gray matter, and cerebrospinal fluid, using FMRIB's Automated Segmentation Tool  from FMRIB's Software Library. Using the 3D T1 images, we calculated volumes of the right and left thalami and basal ganglia nuclei using FMRIB's Integrated Registration and Segmentation Tool-algorithm  . This segments the T1 images into subcortical structures and measures the volumes. We computed a volume scaling factor by linear registration of the skull and brain masks using FSL's flirt, and extracting scaling parameters from the resulting affine transformation matrix, as is done in FSL's SIENAX, , and . We further computed whole-brain WM and GM volumes using SIENAX. We then normalized all volumes based on intracranial volume by multiplication with this volume scaling factor, and then normalized right and left volumes were averaged for inclusion as composites in the statistical analyses. This was based on strong correlations between right and left volumes (e.g., thalami:r = .93,p < .0001) and reducing possible inflated error rates with multiple comparisons. We further note that this has been undertaken in other research on SGM volumes in MS  .
The protocols for data collection were administered on two separate days separated by no more than a week. The participants initially completed the walking outcomes on the first day. There was a 15-minute period of seated rest between the walking assessments. The participants then underwent an MRI within seven days of the first testing session.
2.5. Data analysis
The data were analyzed in SPSS Statistics version 22.0 for Mac OS X operating system. Descriptive statistics were provided as mean ± standard deviation, unless otherwise noted. We examined the associations among 6 MW, T25FW, and thalamic, basal ganglia, and whole-brain WM and GM volumes using Pearson product-moment correlation coefficients (r). We then examined the associations among 6 MW, T25FW, and thalamic and basal ganglia volumes controlling for age (yrs), clinical course (0 = RRMS, 1 = secondary/primary progressive MS), and whole-brain WM and GM volumes using partial correlation coefficients (pr). The final analysis involved hierarchical linear regression models that explored the SGM structure most strongly associated with walking performance. We regressed walking outcomes on age (yrs), clinical course (0 = RRMS, 1 = secondary/primary progressive MS), and whole-brain WM and GM volumes using direct entry in Step 1, and thalamus and basal ganglia volumes were entered in Step 2 using a forward stepwise technique (entrance criterion ofp < .05 and exit criterion ofp = .10). This permitted an exploration of the strongest SGM predictors of walking performance over-and-beyond whole-brain WM and GM volumes.
3.1. Descriptive statistics for walking and MRI outcomes
The mean values for the 6 MW and T25FW were 1109 ± 637 ft and 3.7 ± 2.0 ft/s, respectively. The mean composite values for the thalamus, caudate, pallidum, and putamen volumes were 8562 ± 1193 mm3, 3931 ± 721 mm3, 1845 ± 292 mm3, and 5154 ± 883 mm3, respectively. The mean values for whole-brain WM and GM volumes were 719,443 ± 61,191 mm3and 669,709 ± 51,398 mm3, respectively.
3.2. Bivariate correlations among walking and MRI outcomes
The bivariate correlations among walking and MRI outcomes are provided in Table 1 . The 6 MW and T25FW were significantly correlated with composite volumes of the thalamus, caudate, pallidum, and putamen. Both the 6 MW and T25FW were significantly associated with whole-brain WM volume, whereas only 6 MW was associated with whole-brain GM volume. Volumes of the thalamus, caudate, pallidum, and putamen were associated with whole-brain WM and GM volumes.
p < .05.
p < .01.
Note. 6 MW = six-minute walk; T25FW = timed 25-foot walk test; WM = white matter; and GM = gray matter.
3.3. Partial correlations among walking and SGM outcomes
The bivariate correlations among walking and SGM outcomes are provided in Table 2 . The 6 MW and T25FW remained significant correlates of caudate and pallidum volumes, when controlling for age, MS clinical course, and whole-brain WM and GM volumes. Of note, the strength of the correlations was attenuated, as expected, considering the covariation between SGM and whole-brain structural volumes. The 6 MW and T25FW did not significantly correlate with thalamus and putamen volumes, when controlling for age, MS clinical course, and whole-brain WM and GM volumes.
3.4. Hierarchical regression analyses in MS
The results of the hierarchical linear regression analysis involving the 6 MW as an outcome are provided in Table 3 . After controlling for age, clinical course, and whole-brain WM and GM volumes in Step 1, we established that pallidum was the sole significant correlate of 6 MW performance in Step 2. Importantly, the effect of whole-brain WM volume in Step 1 became non-significant and attenuated when the pallidum entered the model in Step 2.
p < .05.
p < .01.
Note.R2 = .43 for Step 1,p < .0001; ∆R2 = .06 for Step 2,p < .01. B = unstandardized beta-coefficient. SE B = standard error of the unstandardized beta-coefficient. β = standardized beta-coefficient.
The results of the hierarchical linear regression analysis involving the T25FW as an outcome are provided in Table 4 . After controlling for age, clinical course, and whole-brain WM and GM volumes in Step 1, we again established that pallidum was the sole significant correlate of T25FW performance in Step 2. Again, the effect of whole-brain WM volume in Step 1 became non-significant and attenuated when the pallidum entered the regression model in Step 2.
p < .05.
p < .01
Note.R2 = .44 for step 1,p < .0001; ∆R2 = .06 for step 2,p < .01. B = unstandardized beta-coefficient. SE B = standard error of the unstandardized beta-coefficient. β = standardized beta-coefficient.
The present study was motivated by the interest in identifying SGM pathology as correlates of ambulatory dysfunction in MS. To that end, we examined the thalamus and basal ganglia nuclei as SGM correlates of walking outcomes in MS. The primary results indicated that thalamus, caudate, pallidum, and putamen volumes were associated with 6 MW and T25FW in bivariate analyses, but only pallidum and caudate volumes were associated with those outcomes in partial correlation analyses controlling for age, MS clinical course, and whole-brain WM and GM volumes. The regression models identified the pallidum as the strongest SGM predictor of walking performance over-and-beyond whole-brain WM and GM volumes. Such results are consistent with the notion of thalamic and basal ganglia structures being involved in motor control  , but there has been limited research on the contribution for walking performance outcomes in MS. The existing research has largely involved older adults rather than those with neurological diseases and . Importantly, there is evidence for the involvement of basal ganglia in walking based on movement disorders such as Parkinson's disease  . Our results suggest a novel role for thalamic and basal ganglia nuclei, particularly the pallidum and perhaps caudate, as correlates of walking outcomes in MS, and these variables could be included for studying disease and disability progression based on walking dysfunction over time.
There is a neurophysiological basis for examining the involvement of the basal ganglia in walking outcomes and . Indeed, locomotor centers in the brain stem receive GABAergic inputs from the basal ganglia, and locomotor function undergoes continuous regulation by such inhibitory projections and . This network represents a basal ganglia brainstem system and regulates locomotion as well as postural control. To that end, a plausible explanation for the association is that basal ganglia atrophy is associated with a disruption of information outflow in this system resulting in compromised locomotor performance based on walking metrics and .
We observed that the pallidum was the strongest SGM correlate of walking performance. This might be explained by the cortico-striato-pallido-thalamo-cortical circuit  . Indeed, the pallidum is the output region of the basal ganglia conveying information from the striatum (caudate and putamen) to thalamic and cortical regions involved in motor function  . The pallidum seemingly represents a common relay for cortico-subcortical loops involved in motor function in older adults and and perhaps patients with MS. This might explain its prominence as the strongest predictor of walking performance in our analyses.
There are limitations of the current study. We did not assess other MRI variables that might have accounted for variance in walking outcomes such as MR spectroscopy, magnetization transfer, and diffusion tensor imaging. The study only included cross-sectional data and analyses. The results regarding walking outcomes have not been replicated in a separate sample by an independent team of researchers. We controlled for age, clinical course of MS, and whole-brain WM and GM volumes, but there could be other variables that could confound the examination of associations between walking and volumes of SGM structures.
Overall, our paper provides novel inquiry and evidence for possible SGM pathology as correlates of MS-related walking dysfunction. We conclude that thalamus and basal ganglia atrophy are associated with walking dysfunction in MS, consistent with research involving older adults and , and these SGM structures, particularly the pallidum and perhaps caudate, might serve as regions of interest for explaining progression of walking impairment over time in MS.
Conflict of interest
RWM and RHBB have ongoing research grants with Acorda Therapeutics and Biogen Idec. The other authors have no conflict of interest.
-  C. Heesen, J. Böhm, C. Reich, J. Kasper, M. Goebel, S.M. Gold. Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable. Mult Scler. 2008;14:988-991 Crossref
-  N.G. Larocca. Impact of walking impairment in multiple sclerosis: perspectives of patients and care partners. Patient. 2011;4:189-201
-  J.C. Hobart, A. Riazi, D.L. Lamping, R. Fitzpatrick, A.J. Thompson. Measuring the impact of MS on walking ability: the 12-item MS walking scale (MSWS-12). Neurology. 2003;60:31-36 Crossref
-  R.W. Motl. Ambulation and multiple sclerosis. Phys Med Rehabil Clin N Am. 2013;24:325-336 Crossref
-  M.D. Goldman, R.A. Marrie, J.A. Cohen. Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls. Mult Scler. 2008;14:383-390 Crossref
-  Y.C. Learmonth, R.W. Motl, B.M. Sandroff, J.H. Pula, D. Cadavid. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol. 2013;13:37 Crossref
-  R.W. Motl, Y.C. Learmonth. Neurological disability and its association with walking impairment in multiple sclerosis: brief review. Neurodegener Dis Manag. 2014;4:491-500
-  O. Pearson, M. Busse, R. van Deursen, C. Wiles. Quantification of walking mobility in neurological disorders. Q J Med. 2004;97(8):467-475
-  T. Taniwaki, A. Okayama, T. Yoshiura, Y. Nakamura, Y. Goto, J. Kira, et al. Reappraisal of the motor role of basal ganglia: a functional magnetic resonance image study. J Neurosci. 2003;23:3432-3438
-  S. Batista, R. Zivadinov, M. Hoogs, N. Bergsland, M. Heininen-Brown, M.G. Dwyer, et al. Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J Neurol. 2012;259:139-146 Crossref
-  B. Draganski, F. Kherif, S. Kloppel, P.A. Cook, D.C. Alexander, G.J.M. Parker, et al. Evidence for segregated and integrative connectivity patterns in the human basal ganglia. J Neurosci. 2008;28:7143-7152 Crossref
-  J. Dumurgier, F. Crivello, B. Mazoyer, I. Ahmed, B. Tavernier, D. Grabli, et al. MRI atrophy of the caudate nucleus and slower walking speed in the elderly. Neuroimage. 2012;60:871-878 Crossref
-  C. Neiman, B. Godde, U.M. Staudinger, C. Voelcker-Rehage. Exercise-induced changes in basal ganglia volume and cognition in older adults. Neuroscience. 2014;281C:147-163
-  C.H. Polman, S.C. Reingold, G. Edan, M. Filippi, H.P. Hartung, L. Kappos, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol. 2005;58:840-846 Crossref
-  B.C. Kieseier, C. Pozzilli. Assessing walking disability in multiple sclerosis. Mult Scler. 2012;18:914-924 Crossref
-  R.W. Motl, Y. Suh, S. Balantrapu, B.M. Sandroff, J.J. Sosnoff, J. Pula, et al. Evidence for the different physiological significance of the 6- and 2-minute walk tests in multiple sclerosis. BMC Neurol. 2012;12:6 Crossref
-  J.S. Fischer, R.A. Rudick, G.R. Cutter, S.C. Reingold. The Multiple Sclerosis Functional Composite Measure (MSFC): an integrated approach to MS clinical outcome assessment. National MS Society Clinical Outcomes Assessment Task Force. Mult Scler. 1999;5(4):244-250
-  J. Hobart, A.R. Blight, A. Goodman, F. Lynn, N. Putzki. Timed 25-foot walk: direct evidence that improving 20% or greater is clinically meaningful in MS. Neurology. 2013;80:1509-1517 Crossref
-  S.M. Smith. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17:143-155 Crossref
-  Y. Zhang, M. Brady, S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20:45-57 Crossref
-  B. Patenaude, S.M. Smith, D. Kennedy, M. Jenkinson. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage. 2011;56:907-922 Crossref
-  M. Jenkinson, P. Bannister, M. Brady, S. Smith. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825-841
-  M. Jenkinson, S. Smith. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5:143-156 Crossref
-  S.M. Smith, Y. Zhang, M. Jenkinson, J. Chen, P.M. Matthews, A. Federico, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage. 2002;17:479-489 Crossref
-  M.R. DeLong, T. Wichmann. Circuits and circuit disorders of the basal ganglia. Arch Neurol. 2007;64:20-24 Crossref
-  C. Karachi, D. Grabli, F.A. Bernard, D. Tande, N. Wattiez, H. Belaid, et al. Cholinergic mesencephalic neurons are involved in gait and postural disorders in Parkinson disease. J Clin Invest. 2010;120:2745-2754 Crossref
-  K. Takakusaki, J. Oohinata-Sugimoto, K. Saitoh, T. Habaguchi. Role of basal ganglia–brainstem systems in the control of postural muscle tone and locomotion. Prog Brain Res. 2004;143:231-237 Crossref
a Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, USA
b Department of Bioengineering, University of Illinois at Urbana-Champaign, USA
c Department of Neurology, University at Buffalo, State University of NY, School of Medicine and Biomedical Sciences, USA
☆ Disclosures: This paper was supported, in part, by grants from the National Multiple Sclerosis Society (RG 4491A3/1, IL 0003, IL 0011).
© 2015 Elsevier B.V., All rights reserved.