Investigating the structure-function relationship of the corticomotor system early after stroke using machine learning.

ABSTRACT:

Background

Motor outcomes after stroke can be predicted using structural and functional biomarkers of the descending corticomotor pathway, typically measured using magnetic resonance imaging and transcranial magnetic stimulation, respectively. However, the precise structural determinants of intact corticomotor function are unknown. Identifying structure-function links in the corticomotor pathway could provide valuable insight into the mechanisms of post-stroke motor impairment. This study used supervised machine learning to classify upper limb motor evoked potential status using MRI metrics obtained early after stroke.

Methods

Retrospective data from 91 patients (49 women, age 35-97 years) with moderate to severe upper limb weakness within a week after stroke were included i

SUBMITTER: Chong B 

PROVIDER: S-EPMC8741596 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature