TY - JOUR
T1 - Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis
AU - Strik, Myrte
AU - Eijlers, Anand J. C.
AU - Dekker, Iris
AU - Broeders, Tommy A. A.
AU - Douw, Linda
AU - Killestein, Joep
AU - Kolbe, Scott C.
AU - Geurts, Jeroen J. G.
AU - Schoonheim, Menno M.
N1 - Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This study was supported by the Dutch MS Research Foundation (grant nos 13-820 and 14-358e) and the Melbourne University (Melbourne Research Scholarship). Funding Information: The author(s) declared the following potential conflicts of interest with respect to the research, authorship and/or publication of this article: M.S. was supported by a research grant from Melbourne University. A.J.C.E., I.D., T.A.A.B. and L.D. report no disclosures. J.K. reports grants from Biogen, Novartis, TEVA, Bayer Schering Pharma, GlaxoSmithKline, Merck, Genzyme and Roche. S.C.K. receives grant income from the National Health and Medical Research Council of Australia and has received honoraria from Novartis and Biogen. J.J.G.G. has served as a consultant for or received research support from Biogen, Celgene, Genzyme, MedDay, Merck, Novartis and Teva. M.M.S. serves on the editorial board of Neurology and Frontiers in Neurology, receives research support from the Dutch MS Research Foundation, Eurostars-EUREKA, ARSEP, Amsterdam Neuroscience, MAGNIMS and ZonMW and has served as a consultant for or received research support from Atara Biotherapeutics, Biogen, Celgene/Bristol Myers Squibb, Genzyme, MedDay and Merck. Publisher Copyright: © The Author(s), 2022.
PY - 2022
Y1 - 2022
N2 - Background: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). Objective: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. Methods: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. Results: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). Conclusion: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.
AB - Background: Upper and lower limb disabilities are hypothesized to have partially independent underlying (network) disturbances in multiple sclerosis (MS). Objective: This study investigated functional network predictors and longitudinal network changes related to upper and lower limb progression in MS. Methods: Two-hundred fourteen MS patients and 58 controls underwent functional magnetic resonance imaging (fMRI), dexterity (9-Hole Peg Test) and mobility (Timed 25-Foot Walk) measurements (baseline and 5 years). Patients were stratified into progressors (>20% decline) or non-progressors. Functional network efficiency was calculated using static (over entire scan) and dynamic (fluctuations during scan) approaches. Baseline measurements were used to predict progression; significant predictors were explored over time. Results: In both limbs, progression was related to supplementary motor area and caudate efficiency (dynamic and static, respectively). Upper limb progression showed additional specific predictors; cortical grey matter volume, putamen static efficiency and posterior associative sensory (PAS) cortex, putamen, primary somatosensory cortex and thalamus dynamic efficiency. Additional lower limb predictors included motor network grey matter volume, caudate (dynamic) and PAS (static). Only the caudate showed a decline in efficiency over time in one group (non-progressors). Conclusion: Disability progression can be predicted using sensorimotor network measures. Upper and lower limb progression showed unique predictors, possibly indicating different network disturbances underlying these types of progression in MS.
KW - Functional magnetic resonance imaging
KW - disability progression
KW - longitudinal
KW - network dynamics
KW - network efficiency
KW - upper and lower limbs
UR - http://www.scopus.com/inward/record.url?scp=85139222625&partnerID=8YFLogxK
U2 - https://doi.org/10.1177/13524585221125372
DO - https://doi.org/10.1177/13524585221125372
M3 - Article
C2 - 36177896
SN - 1352-4585
JO - MULTIPLE SCLEROSIS JOURNAL
JF - MULTIPLE SCLEROSIS JOURNAL
ER -