A well-characterized neural network is associated with motor learning, involving several brain regions known to have functional and structural deficits in persons with multiple sclerosis (PwMS). However, it is not known how MS affects postural motor learning or the neural networks involved. The aim of this study was to gain a better understanding of the neural networks underlying adaptation of postural responses within PwMS. Participants stood on a hydraulically driven, servo-controlled platform that translated horizontally forward and backward in a continuous sinusoidal pattern across multiple trials over two consecutive days. Our results show similar postural adaptation between PwMS and age-matched control participants despite overall deficits in postural motor control in PwMS. Moreover, PwMS demonstrated better retention the following day. PwMS had significantly reduced functional connectivity within both the cortico-cerebellar and cortico-striatal motor loops; neural networks that subserve implicit motor learning. In PwMS, greater connectivity strength within the cortico-cerebellar circuit was strongly related to better baseline postural control, but not to postural adaptation as it was in control participants. Further, anti-correlated cortico-striatal connectivity within the right hemisphere was related to improved postural adaptation in both groups. Taken together with previous studies showing a reduced reliance on cerebellar- and proprioceptive-related feedback control in PwMS, we suggest that PwMS may rely on cortico-striatal circuitry to a greater extent than cortico-cerebellar circuitry for the acquisition and retention of motor skills.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Clinical Neurology
- Cognitive Neuroscience