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|中風復健後與虛擬實境物理參數 相關的動作網絡重組;Reorganization of the motor network associated with virtual reality parameters in response to rehabilitation after stroke
|動態因果模型;中風復原機制;虛擬實境復健;動作力學;dynamic casual modeling;recovery mechanism;virtual reality (VR) based rehabilitation;motor kinetics
|2016-10-13 13:03:40 (UTC+8)
研究結果顯示有22條連結的變化在復健過後是與運動力學參數以及量表進步率有顯著相關，其中投籃項目裡有10條連結，而拋接球項目有12條連結。例如iPM gamma對SMA alpha抑制減弱跟復原相關: 抑制減弱較少的病人復原較好，同時，這減弱與復健系統裡的接觸球前與接觸球後的瞬時速度改變有正相關: 抑制減弱較少的病人瞬時速度會減慢。此外，cM1 beta對SMA alpha的抑制減弱也跟復原相關: 抑制減弱較少的病人復原較好，同時，這減弱與復健系統裡患側手運動的瞬時速度改變有負相關，抑制減弱較少的病人瞬時速度增加。這樣的結果顯示抑制SMA跟調控患側手運動的速度有關。
;Motor deficits of the affected upper limb (UL) after stroke affect up to two-thirds of stroke patients and conventional rehabilitation aims at reducing UL impairment, but significant variability exists between patients regarding rehabilitation efficacy. To date, the mechanisms induced by rehabilitation that can mediate the recovery process are not fully understood. In this study, we aim to identify the improvement-related motor network alternations that are correlated with the parameters acquired during rehabilitation. To this end, a home-made virtual reality (VR) based rehabilitation programme was designed to record the motion kinetics during rehabilitation treatment and EEG and dynamical causal modelling of induced responses (DCM_IR) were employed to analysis the motor network.
18 stroke subjects were recruited and underwent a VR based rehabilitation program with the frequency of 1 hour per day, five days a week. The parameters of human kinetics, such as speed/max speed, velocity and trajectory during VR-based rehabilitation were recorded digitally for correlation analysis. Before and after the rehabilitation, EEG data and were acquired during upper limb movements and the Fugl-Meyer Assessment of Physical Performance (FM) was estimated. DCM_IR was used to model the network parameters using EEG data and the changes of DCM_IR parameters after rehabilitation were then tested statistically by ANOVA. Significant changes of DCM_IR parameters were then correlated with the changes of FM scores after rehabilitation (i.e. improvement). Having established the improvement-related motor network changes, we then further tested whether a relationship existed between these changes and the changes of the parameters of motion kinetics during rehabilitation treatment.
We have identified 22 coupling changes that are significantly correlated with the changes of motor kinetics induced by rehabilitation, of which 10 and 12 network changes were associated with the kinetics changes in the shooting and juggling VR games, respectively. Specifically, less inhibition loss from the ipsilateral pre-motor (iPM) gamma to SMA alpha led to improvements and this inhibitory change is positively correlated with the change in the instantaneous velocity during the shooting game. In addition, in the juggling game, we found that greater cM1 beta inhibition toward SMA alpha through less inhibition loss led to better outcome and this change was negatively related to the change in the instantaneous velocity during the affected hand movement.
In conclusion, we have shown for the first time the functional role of the improvement-related motor network alternations in response to rehabilitation. The significance of this study is that we provide insights into the underlying recovery mechanism and the finding is translational in the clinical practice to develop a knowledge-based rehabilitation program that can facilitate the rehabilitation efficacy.
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