中風復健後與虛擬實境物理參數 相關的動作網絡重組

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黃致榮(Chih-Jung Huang) 查詢紙本館藏

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生物醫學工程研究所

論文名稱

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)

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摘要(中)

中風後會有三分之二的患者有上肢的運動功能障礙，常規的復健計畫目的在於減少上肢在運動時所受到的阻礙，但是關於患者的復原功效卻是存在著許多變異性。到目前為止，可以介入復原過程中的復原誘發機制尚未被完全了解。我們這項研究的主要目標在檢驗復原過程中有那些相關運動參數的改變，是有利於改善整個大腦的運動網絡。因此我們使用自製的虛擬實境復健系統，記錄病患復健過程中的物理參數，並使用腦電圖搭配動態因果模型的誘發反應用來分析整個運動網絡連結的變化。
本研究總共招募18位中風受試者，進行20小時為一個療程的虛擬實境復健計畫，頻率為每天至多一個鐘頭，每周五天。我們在虛擬實境復健過程中記錄著每次復健之速度、最大速度、壓力、軌跡、效率等具體的物理參數，此外，我們也會在復健前以及復健後收集腦電波資料與進行Fugl-Meyer上肢動作量表的評估。收集的腦電波資料經由動態因果模型進行，將誘發響應找出最適合當下大腦網絡的模型，透過大腦網絡的連結參數於復健後的變化再與Fugl-Meyer的量表進步率進行相關性分析，找出與復原相關的網絡連結變化。最後，再將顯著有助於復原的網絡連結的變化與復健過程中，運動指標的變化做相關性分析，以確立中風復健後與虛擬實境物理參數相關的動作網絡重組。
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。
總之，我們發現量表進步率與動作網絡的改變在復建的過程中所扮演的功能及作用，再搭配上物理參數的調控並可扮演其中復原的推手。而這項研究的意義在於我們可以利用復健前的腦波資訊，提供一個潛在的復原機制和發展一種建立在所知訊息下而設立的個人化復健療程，並且應用於臨床復健治療來達到更有利的復健療效。

摘要(英)

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.

關鍵字(中)

★ 動態因果模型
★ 中風復原機制
★ 虛擬實境復健
★ 動作力學

關鍵字(英)

★ dynamic casual modeling
★ recovery mechanism
★ virtual reality (VR) based rehabilitation
★ motor kinetics

論文目次

摘要 i
Abstract iii
目錄 v
圖目錄 vii
表目錄 ix
第一章緒論 1
1-1研究背景與動機 1
1-2研究目的 3
第二章文獻回顧 4
2-1中風評估量表 4
2-2中風復健 5
2-2-1傳統復健方法 5
2-2-2虛擬實境復健方法 6
2-3特異頻帶腦波 7
2-4復健後大腦網絡重組 8
2-5動態因果模型 10
2-5-1功能性連結與有效性連結 10
2-5-2誘發響應的動態因果模型 11
2-5-3線性與非線性效應 13
2-5-4增益性與抑制性連結 14
第三章實驗方法與流程 16
3-1受試者資料 16
3-2虛擬實境復健療程 19
3-3運動試驗 21
3-4資料處理 23
3-4-1訊號前處理 23
3-4-2動態因果模型 24
3-4-3訊號分析方式 25
第四章研究結果 28
4-1前後測運動功能量表分數 28
4-2動態因果模型 30
4-2-1每個人的大腦模型選擇 30
4-2-2復健前後大腦活化情形 33
4-3傳統量表分數與連結強度改變量的相關性 38
4-3-1所有人與前後測顯著相關連結 39
4-3-2所有人與進步率顯著相關連結 40
4-3-3好壞組與進步率顯著相關連結 42
4-4虛擬實境指標與連結強度改變量的相關性 47
4-4-1好壞組中與連結顯著相關的投籃VR指標 48
4-4-2好壞組中與連結顯著相關的拋接球VR指標 52
4-4-3好壞組中與連結顯著相關的VR指標總整理 54
第五章討論 59
5-1影響研究結果的參數 59
5-2網絡重組與物理參數對復健的影響 61
第六章結論與未來展望 62
6-1結論 62
6-2未來展望 63
第七章附錄 64
7-1 物理參數Max值變化顯著相關數據 64
第八章參考文獻 67

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指導教授

陳純娟(Chun-Chuan Chen)

審核日期

2016-8-17

推文