Functional connectivity underlying postural motor adaptation in people with multiple sclerosis.
Brett W Fling1, Geetanjali Gera Dutta2, Fay B Horak1
1Department of Neurology, School of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd., Portland, OR 97239-3098, USA ; Portland VA Medical Center, 3710 SW US Veterans Hospital Rd., Portland, OR 97239-9264, USA.
Neuroimage. Clinical
|June 25, 2015
Summary
Persons with multiple sclerosis (PwMS) show similar postural adaptation to controls, despite motor control deficits. PwMS exhibit enhanced motor learning retention, suggesting altered neural network reliance.
Area of Science:
- Neuroscience
- Motor Control
- Multiple Sclerosis Research
Background:
- Motor learning involves specific neural networks, including cortico-cerebellar and cortico-striatal circuits.
- Multiple sclerosis (MS) is known to cause deficits in these brain regions, but its impact on postural motor learning is unclear.
Purpose of the Study:
- To investigate the neural networks underlying postural motor learning and adaptation in persons with multiple sclerosis (PwMS).
- To compare postural adaptation and retention between PwMS and healthy controls.
Main Methods:
- Participants performed postural tasks on a servo-controlled platform over two days.
- Functional connectivity within motor-related neural networks was assessed in PwMS and controls.
Main Results:
- PwMS showed comparable postural adaptation and superior retention compared to controls, despite baseline motor control deficits.
- PwMS exhibited reduced functional connectivity in cortico-cerebellar and cortico-striatal motor loops.
- Cortico-cerebellar connectivity correlated with baseline control in PwMS, while cortico-striatal connectivity related to adaptation in both groups.
Conclusions:
- PwMS may compensate for cerebellar and proprioceptive deficits by increasing reliance on cortico-striatal pathways for motor skill acquisition and retention.
- Altered functional connectivity within motor loops is a key feature of motor learning in PwMS.
