Author: Yun-Hee KimPublisher:
ISBN:
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Languages : en
Pages :
Book Description
IntroductionUnderstanding recovery mechanism and predicting recovery outcome after stroke are important to implicate individually-tailored rehabilitation strategy. In case of recovery of upper extremity (UE) motor function, most biomarkers demonstrate low predictive power in severe stroke. This study focused on investigation of functional imaging biomarkers optimally predicting recovery of UE motor function in severe stroke patients.MethodsForty-two subacute ischemic stroke patients (24 males, mean age 57.2u00b112.4 years) with severe motor impairment participated. They underwent T1-weighted, diffusion tensor imaging, resting-state functional MRI at 2 weeks after stroke. Motor function was assessed using the Fugl-Meyer Assessment scale at 2 weeks and 3 months after stroke. Participants were divided into the Fitter and Non-fitter groups according to whether the UE motor recovery achieves 70% of initial impairment based on proportional recovery. Important neuroimaging measures were investigated; fractional anisotropy (FA) values of the corticospinal tract (CST), corpus callosum (CC), superior cerebellar peduncle (SCP), lesion load of the CST, and interhemispheric connectivity were extracted. ResultsFA value of the SCP was considered as a significant predictive biomarker for UE motor recovery in the Fitter group. FA value of the CC and strength of functional connectivity between visual and auditory areas were predictive biomarkers in the Non-fitter group.ConclusionsDomains of predictive biomarkers were different according to the recovery pattern in severe stroke. Especially, non-motor domains were considered as predictive biomarkers for UE recovery in Non-fitter group. This study implies the need to develop different strategy for UE motor recovery in severe stroke patients with highly unpredictable individuals. AcknowledgementThis study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; NRF-2017R1A2A1A05000730, NRF-2017R1D1A1B03034109, NRF-2017M3A9G5083690) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI17C1501).
