Background and objectives
Thalamic atrophy (TA) represents a biomarker of neurodegeneration and associated dysfunction/decline in physical and cognitive functioning among persons with multiple sclerosis (MS). Aerobic fitness, as an end point of exercise training, represents a promising target for restoring function in MS, but it is unknown if such effects differ by TA. This cross-sectional study examined whether aerobic fitness was differentially associated with cognitive processing speed and walking endurance in persons with MS who present with and without TA.
44 fully ambulatory persons with MS completed a graded exercise test for measuring aerobic fitness (VO2peak) and underwent 3T MRI for measuring TA, the Symbol Digit Modalities Test (SDMT), and the 6-min walk (6MW). We performed Spearman correlations (rs) among VO2peak, SDMT, and 6MW scores overall, and in persons with and without TA. We applied Fisher’s z-test for comparing correlations based on TA status.
When controlling for age, EDSS score, and global MRI measures of atrophy, VO2peak was strongly associated with SDMT scores (prs = 0.74, p < 0.01) and 6MW performance (prs = 0.77, p < 0.01) in persons with TA, whereas VO2peak was not associated with SDMT scores (prs = − 0.01, p = 0.99) or 6MW performance (prs = 0.25, p = 0.38) in those without TA. The correlations between VO2peak and SDMT (z = 2.86, p < 0.01) and VO2peak and 6MW (z = 2.33, p = 0.02) were significantly stronger in the TA group.
This study provides initial evidence of strong, selective associations among aerobic fitness, cognitive processing speed, and walking endurance in persons with TA as a biomarker for MS-related neurodegeneration. Such data support TA as a moderator of the association among aerobic fitness, cognitive processing speed, and walking endurance in persons with MS. Future research should carefully consider the role of TA when designing trials of aerobic exercise, cognition, and mobility in MS.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
Wallin MT, Culpepper WJ, Campbell JD et al (2019) The prevalence of MS in the United States: a population-based estimate using health claims data. Neurology 92:e1029–e1040
Trapp BD, Nave K-A (2008) Multiple sclerosis: an immune or neurodegenerative disorder. Ann Rev Neurosci 31:247–269
Zivadinov R, Jakimovski D, Gandhi S et al (2016) Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine. Expert Rev Neurother 16(7):777–793
Sherman SM, Guillery RW (2002) The role of the thalamus in the flow of information to the cortex. Philos Trans R Soc Lond B Biol Sci 357(1428):1695–1708
Saalmann YB, Kastner S (2011) Cognitive and perceptual functions of the visual thalamus. Neuron 71(2):209–223
Bergsland N, Benedict RHB, Dwyer MG et al (2021) Thalamic nuclei volumes and their relationships to neuroperformance in multiple sclerosis: a cross-sectional MRI study. J Mag Reson Imaging 34:411–419
Motl RW, Sandroff BM, Benedict RHB, Hubbard EA, Pilutti LA, Sutton BP (2021) Do subcortical gray matter volumes and aerobic capacity account for cognitive-motor coupling in multiple sclerosis? Mult Scler 27(3):401–409
Rocca MA, Mesaros S, Pagani E, Sormani MP, Comi G, Filippi M (2010) Thalamic damage and long-term progression of disability in multiple sclerosis. Radiology 257(2):463–469
Magon S, Tsagkas C, Gaetano L et al (2020) Volume loss in the deep gray matter and thalamic subnuclei: a longitudinal study on disability progression in multiple sclerosis. J Neurol 267(5):1536–1546
Azevedo CJ, Cen SY, Khadka S et al (2018) Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease. Ann Neurol 83:223–234
Dekker I, Schoonheim MM, Venkatraghavan V et al (2021) The sequence of structural, functional and cognitive changes in multiple sclerosis. Neuroimage Clin 29:102550
Feinstein A, Freeman J, Lo AC (2015) Treatment of progressive multiple sclerosis: what works, what does not, and what is needed. Lancet Neurol 14:194–207
Motl RW, Sandroff BM, Kwakkel G et al (2017) Exercise in patients with multiple sclerosis. Lancet Neurol 16(10):848–856
Motl RW, Sandroff BM (2018) Exercise as a countermeasure to declining central nervous system function in multiple sclerosis. Clin Ther 40(1):16–25
Sandroff BM, Wylie GR, Sutton BP, Johnson CL, DeLuca J, Motl RW (2018) Treadmill walking exercise training and brain function in multiple sclerosis: preliminary evidence setting the stage for a network-based approach to rehabilitation. Mult Scler J Exp Transl Clin 4(1):2055217318760641
Sandroff BM, Wylie GR, Baird JF et al (2021) Effects of walking exercise training on learning and memory and hippocampal neuroimaging outcomes in MS: a targeted, pilot randomized controlled trial. Contemp Clin Trials 110:106563
Sandroff BM, Diggs MD, Bamman MM et al (2019) Protocol for a systematically-developed, Phase I/II, single-blind, randomized controlled trial of treadmill walking exercise training effects on cognition and brain function in persons with multiple sclerosis. Contemp Clin Trials 87:105878
McDonald WI, Compston A, Edan G et al (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 50(1):121–127
Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33:1444–1452
Thomas S, Reading J, Shephard RJ (1992) Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Can J Sport Sci 17(4):338–345
Feasel CD, Sandroff BM, Motl RW (2021) Cardiopulmonary exercise testing using the modified Balke protocol in fully ambulatory people with multiple sclerosis. Cardiopulm Phys Ther J 32(2):57–65
Smith A (1982) Symbol digit modalities test: manual. Western Psychological Services, Los Angeles
Benedict RH, DeLuca J, Phillips G et al (2017) Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis. Mult Scler 23(5):721–733
Parmenter BA, Testa SM, Schretlen DJ, Weinstock-Guttman B, Benedict RHB (2010) The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J Int Neuropsychol Soc 16(1):6–16
Sumowski JF, Benedict R, Enzinger C et al (2018) Cognition in multiple sclerosis: state of the field and priorities for the future. Neurology 90:278–288
Goldman MD, Marrie RA, Cohen JA (2008) Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls. Mult Scler 14(3):383–390
Learmonth YC, Dlugonski D, Pilutti LA, Sandroff BM, Motl RW (2013) The reliability, precision and clinically meaningful change of walking assessments in multiple sclerosis. Mult Scler 19(13):1784–1791
FreeSurfer FB (2012) Neuroimage 62(2):774–781
Potvin O, Mouiha A, Dieumegarde L, Duschesne S, Azheimer’s Disease Neuroimaging Initiative (2016) Normative data for subcortical regional volumes over the lifetime of the adult human brain. Neuroimage 137:9–20
Duara R, Loewenstein DA, Shen Q et al (2013) Regional patterns of atrophy on MRI in Alzheimer’s disease: neuropsychological features and progression rates in the ADNI cohort. Adv Alzheimer Dis 2(4):135–147
Sormani MP, Kappos L, Radue E-W et al (2017) Defining brain volume cutoffs to identify clinically relevant atrophy in RRMS. Mult Scler 23(5):656–664
Schmidt P, Gaser C, Arsic M et al (2012) An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59:3774–3783
Egger C, Opfer R, Wang C et al (2017) MRI FLAIR lesion segmentation in multiple sclerosis: does automated segmentation hold up with manual annotation? Neuroimage Clin 13:264–270
Rousselet GA, Pernet CR (2012) Improving standards in brain-behavior correlation analyses. Front Hum Neurosci 6:119
Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd ed. Lawrence Erlbaum Associates, Hillsdale
Tauhid S, Neema M, Healy BC, Weeiner HL, Bakshi R (2014) MRI phenotypes based on cerebral lesions and atrophy in patients with multiple sclerosis. J Neurol Sci 346(1–2):250–254
Sandroff BM, Motl RW, DeLuca J (2017) The influence of cognitive impairment on the fitness/cognition/relationship in MS. Med Sci Sports Exerc 49(6):1184–1189
Sandroff BM, Klaren RE, Pilutti LA, Dlugonski D, Benedict RHB, Motl RW (2014) Randomized controlled trial of physical activity, cognition, and walking in multiple sclerosis. J Neurol 261(2):363–372
Houtchens MK, Benedict RHB, Killiany R et al (2007) Thalamic atrophy and cognition in multiple sclerosis. Neurology 69(12):1213–1223
Batista S, Zivadinov R, Hoogs M et al (2012) Basal ganglia, thalamus, and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis. J Neurol 259(1):139–146
Bisecco A, Stamenova S, Caiazzo G et al (2018) Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume. Brain Imaging Behav 12(1):20–28
Motl RW, Hubbard EA, Sreekumar N et al (2015) Pallidal and caudate volumes correlate with walking function in multiple sclerosis. J Neurol Sci 354(1–2):33–36
Sandroff BM, Bollaert RE, Pilutti LA et al (2017) Multimodal exercise training in multiple sclerosis: a randomized controlled trial in persons with substantial mobility disability. Contemp Clin Trials 61:39–47
Motl RW, Sandroff BM (2020) Current perspectives on exercise training in the management of multiple sclerosis. Exp Rev Neurother 20(8):855–865
Sandroff BM, Schwartz CE, DeLuca J (2016) Measurement and maintenance of reserve in multiple sclerosis: a review. J Neurol 263:2158–2169
Sandroff BM, Sosnoff JJ, Motl RW (2013) Physical fitness, walking performance, and gait in multiple sclerosis. J Neurol Sci 328(1–2):70–76
Sandroff BM, Pilutti LA, Benedict RHB, Motl RW (2015) Association between physical fitness and cognitive function in multiple sclerosis: does disability status matter? Neurorehabil Neural Repair 29(3):214–223
Sandroff BM, Motl RW (2012) Fitness and cognitive processing speed in persons with multiple sclerosis: a cross-sectional investigation. J Clin Exp Neuropsychol 34(10):1041–1052
Sandroff BM, Pilutti LA, Motl RW (2015) Does the six-minute walk test measure walking performance or physical fitness in persons with multiple sclerosis? NeuroRehabilitation 37:149–155
Sandroff BM, Klaren RE, Motl RW (2015) Relationships among physical inactivity, deconditioning, and walking impairment in persons with multiple sclerosis. J Neurol Phys Ther 39(2):103–110
Sandroff BM, Motl RW (2021) Amato MP et al Cardiorespiratory fitness and free-living physical activity are not associated with cognition in persons with progressive multiple sclerosis: baseline analyses from the CogEx study. Mult Scler 28:1091–1100
The authors report no conflicts of interest directly or indirectly related to this manuscript. This study was supported, in part, by an investigator-initiated grant from EMD Serono, Inc. (One Technology Place, Rockland, MA 02370) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under Award Number R01HD091155.
Conflicts of interest
The authors declare that they have no competing interests.
Ethical standard statement
All study procedures were approved by the Institutional Review Board at the University of Illinois at Urbana-Champaign, University of Alabama at Birmingham, and Kessler Foundation.
All participants provided written informed consent.
About this article
Cite this article
Sandroff, B.M., Motl, R.W., Román, C.A.F. et al. Thalamic atrophy moderates associations among aerobic fitness, cognitive processing speed, and walking endurance in persons with multiple sclerosis. J Neurol 269, 5531–5540 (2022). https://doi.org/10.1007/s00415-022-11205-9