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Sensorimotor network dynamics predict decline in upper and lower limb function in people with multiple sclerosis
被引:5
|作者:
Strik, Myrte
[1
,2
,3
]
Eijlers, Anand J. C.
[3
]
Dekker, Iris
[4
]
Broeders, Tommy A. A.
[3
]
Douw, Linda
[3
]
Killestein, Joep
[4
]
Kolbe, Scott C.
[5
]
Geurts, Jeroen J. G.
[3
]
Schoonheim, Menno M.
[3
]
机构:
[1] Univ Melbourne, Melbourne Med Sch, Dept Radiol, Melbourne Brain Ctr,Imaging Unit, Level 1,Kenneth Myer Bldg, Melbourne, Vic 3010, Australia
[2] Univ Melbourne, Dept Radiol, Melbourne Brain Ctr, Imaging Unit, Melbourne, Vic, Australia
[3] Vrije Univ Amsterdam, MS Ctr Amsterdam Anat & Neurosci, Amsterdam UMC, Locat VUmc, Amsterdam, Netherlands
[4] Vrije Univ Amsterdam, Amsterdam Neurosci, MS Ctr Amsterdam Neurol, Amsterdam UMC,Locat VUmc, Amsterdam, Netherlands
[5] Monash Univ, Dept Neurosci, Cent Clin Sch, Melbourne, Vic, Australia
关键词:
Functional magnetic resonance imaging;
upper and lower limbs;
disability progression;
network efficiency;
network dynamics;
longitudinal;
TIMED 25-FOOT WALK;
9-HOLE PEG TEST;
DISABILITY;
CONNECTIVITY;
IMPAIRMENT;
D O I:
10.1177/13524585221125372
中图分类号:
R74 [神经病学与精神病学];
学科分类号:
摘要:
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.
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页码:81 / 91
页数:11
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