Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis

Author:

Strik Myrte1ORCID,Eijlers Anand JC2,Dekker Iris3,Broeders Tommy AA2,Douw Linda2,Killestein Joep3,Kolbe Scott C4ORCID,Geurts Jeroen JG2,Schoonheim Menno M2ORCID

Affiliation:

1. Melbourne Brain Centre Imaging Unit, Department of Radiology, University of Melbourne, Melbourne, VIC, Australia/MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands

2. MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands

3. MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, the Netherlands

4. Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia

Abstract

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.

Funder

University of Melbourne

Stichting MS Research

Publisher

SAGE Publications

Subject

Neurology (clinical),Neurology

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