臺灣博碩士論文加值系統

研究目的：舞蹈家與鋼琴家雖同為藝術家，但是在專業的訓練養成上有很大的差異。兩者均需要長時間且高強度的訓練，進而邁向該領域的大師。舞蹈家的訓練是屬於全身性的訓練，舞者必須整合視覺、聽覺與身體動作的資訊來完成舞蹈動作。但就鋼琴家的訓練來說比較針對身體局部特定區域的訓練，主要是整合聽覺與細微的手部動作的資訊。近期的研究顯示大腦白質結構的變化可以用來辨別所對應之訓練。然而目前針對長期舞蹈與鋼琴訓練之大腦結構網絡型態還未明確，我們假設長期舞蹈或鋼琴訓練可以重塑受訓者的大腦白質連結體之間的協同作用。本研究我們使用一種嶄新的大腦白質神經纖維之共變性分析(tract covariance analysis)去觀察長期接受舞蹈及鋼琴訓練後大腦白質神經纖維之共變性變化，進而利用圖形理論(graph theory)分析作為新的途徑比較接受不同訓練之大腦網路結構變化。

研究方法：我們招收29名受正規舞蹈藝術訓練之在學學生、31名受正規鋼琴藝術訓練之學生與37名未受過相關藝術訓練的控制組，我們於3T磁振造影儀進行97位受試者之磁振造影擴散頻譜影像(diffusion spectrum imaging, DSI)掃描，觀察腦部內白質神經纖維束之水分子擴散特性，計算水分子在白質神經纖維中之非等向性指標(generalized fractional anisotropy, GFA)，透過全腦基於神經束之自動化分析(Tract-based automatic analysis, TBAA)來擷取全腦主要76條神經纖維束之GFA值，我們平均每條神經纖維束之路徑上的GFA值後進行神經纖維束之共變性分析。神經纖維束之共變性是使用淨相關(partial correlation)的方法控制年齡和性別之影響後，進行76條神經纖維束中任意兩條在同一群體受試者之間之共變性分析，並以多重比較的方法控制錯誤發現率(false discovery rate)來移除圖形中不真實的連結。在這篇論文中，我們將大腦白質神經纖維之共變性變化矩陣(tract covariance matrix)並搭配圖形理論分析大腦結構網路，另外再將大腦網絡參數之組間比較拆成兩個層面來探討，第一層先探討經過長期訓練之舞者與鋼琴家合在一起稱為藝術家組的腦結構網絡參數與沒有經過訓練的控制組是否有差異;第二層再將舞者與鋼琴家分成各自一組。分別各自作組間比較。我們利用排列檢定(permutation test)進行組間大腦網絡參數分析。

研究結果與討論：在藝術家與控制組的比較中，我們發現在經長期藝術訓練的族群大腦網絡結構會有比較好的整體效率(global efficiency)，這些神經束包括連結腳趾運動皮質區的皮質脊髓束(corticospinal tract of the toe component)以及連結兩側海馬迴的連合纖維(callosal fibers connecting bilateral hippocampi)，另外我們也觀察到藝術家有較佳的局部效率(local efficiency)，這些神經束包括上縱束(superior longitudinal fasciculus I)、連結腳趾運動皮質區的皮質脊髓束、丘腦與運動感覺皮質區(thalamus to postcentral gyrus)以及連結兩側運動輔助皮質區的連合纖維(callosal fibers connecting bilateral supplementary motor areas)，這些結果顯示經過長期的藝術訓練可以改變腦部運動感覺區域的網絡結構轉向更優化的網絡組織。在鋼琴家與舞者的比較中，我們發現舞者在一些關於高等視覺(bilateral perpendicular fasciculi)、認知功能(left uncinated fasciculus and callosal fibers connecting bilateral orbitofrontal cortices)與運動感覺(left frontal-striatum to precentral gyrus and callosal fibers connecting bilateral supplementary motor areas)相關的神經束有比較高的局部效率。我們的研究結果顯示長期舞蹈訓練與長期鋼琴訓練會造成不同的大腦結構改變，而這些改變不僅僅是白質結構的改變還包括大腦神經網絡協同作用的改變，而這些系統性的改變，將會讓經過長期該項技能訓練的人在執行該項技能時更有效率。我們的工作提供了一個網路層級的代表，將研究人類大腦的視野從區域到全域。腦網路的拓撲結構可能為主要的潛在影響，有助於我們了解大腦在經過長期訓練後重組的變化。而白質神經纖維束之共變性這樣的分析方把可以幫助我們更加了解神經可塑性與長期藝術訓練對我們大腦網絡所造成的影響。

Introduction: Dancers and pianists receive shared and distinct protocols of training, both require long-term and intense physical training to master their skills. Dancers engage the whole body and require the integration of visual, auditory and motor information. In comparison, pianists engage specific parts of the body and primarily require the integration of auditory and motor information. Recent studies have shown that dancers and pianists exhibit different patterns of change in brain structures, which might associate with different training experiences. However, the brain structures at the network levels corresponding to long-term training remain unexplored. Here, we hypothesized that these two types of long-term training modulate the brain networks differently. We proposed a novel metric called tract covariance to indicate connection between any pair of tracts, and used graph theory analysis to characterize the topological properties of tract covariance in these two types of trainees.

Materials and Methods: Participants included people with long-term dance training (age: 22.76 ± 2.65 years, 6 males and 23 females), people with long-term piano training (age: 21.42 ± 1.26 years, 9 males and 22 females) and untrained controls (age: 23.11 ± 1.68 years, 9 males and 28 females). They received scanning of the head using diffusion spectrum imaging (DSI) on a 3T MRI scanner to obtain information about white matter microstructural property. We used whole brain tract-based automatic analysis to obtain a 2D connectogram for each DSI dataset. The connectogram provides generalized fractional anisotropy (GFA) profiles of 76 white matter tract bundles. We averaged the 100 values of each tract profile to obtain a mean GFA value for each tract. Tract covariance was defined as the partial correlation (controlling age and sex) between each pair of tracts in variations of GFA values across subjects. Correction for multiple comparisons was done using a false discovery rate at 0.05 to remove spurious connections in the tract covariance matrix. For each group, we analyzed the network properties of tract covariance matrix by graph theory. Statistical comparison of the network properties between groups was performed. We performed the comparison at two levels: (i) untrained controls versus trainees including dancers and pianists, and (ii) long-term dance trainees versus long-term piano trainees, using a nonparametric permutation test procedure.

Results and Discussions: As compared with controls, trainees exhibited higher nodal global efficiency in the right corticospinal tract (CST) of the toe component and lower global efficiency in the anterior commissure, CF connecting bilateral hippocampi and CF connecting bilateral precuneus. Trainees also showed higher nodal local efficiency in the right superior longitudinal fasciculus (SLF) I, the right CST of the toe component, the left thalamus to precentral gyrus, the right thalamus to the postcentral gyrus and the CF connecting bilateral supplementary motor areas (SMA). The increase in nodal global and local efficiency in trainees suggests that long-term training induce a shift towards a more optimal topological organization. By comparing with pianists, analysis of nodal local efficiency revealed that dancers had greater involvement of tracts related to high level visual, cognition, motor-sensory processing including the bilateral perpendicular fasciculi, the right uncinate fasciculus, the left frontal-striatum (FS) to the precentral gyrus, the CF connecting bilateral orbitofrontal cortices and the CF connecting bilateral SMA. On the other hand, pianists had higher nodal global efficiency in the left stria terminalis and CF connecting bilateral amygdales. We did not observe any significant differences in global topological properties between trainees and controls, dancers and pianists. Our findings indicate that people with long-term dance training have different structural network properties from those of people with long-term piano training. This possibly reflects a network-level reorganization that might lead to a more efficient organization in subjects with specific training. In summary, tract covariance matrix combined with graph theory analysis can provide a network-level representation of training effects in the human brain.

誌謝 i
中文摘要 ii
英文摘要 iv
目錄 vii
Contents of Figures x
Contents of Tables xi
Chapter 1 Introduction 1
1.1 Neuroplasticity of Brain Structure after Long-term Art Training 1
1.1.1 Structural Neuroplasticity in Piano Training 1
1.1.2 Structural Neuroplasticity in Dance Training 3
1.2 Introduction to Structural Covariance and Network Analysis 5
1.3 Imaging Tract Covariance 7
1.4 Aim and Hypotheses of the Thesis 9
Chapter 2 Materials and Methods 13
2.1 Participants 13
2.2 Image Acquisition 14
2.3 DSI Data Quality Assurance 15
2.4 DSI Data Reconstruction 16
2.5 Analyses 18
2.5.1 Tract-based Automatic Analysis (TBAA) 18
2.5.2 Threshold Free Cluster Weighted (TFCW) Scores for Group Analysis 19
2.5.3 White Matter Tract Covariance Analysis 21
2.5.4 Network Analysis 21
2.5.5 Statistical Analysis 26
2.5.6 Correlation Analyses 27
Chapter 3 Results 28
3.1 Training-induced Modulation of White Matter Tracts 28
3.2 Relationship between GFA value and Training Duration 33
3.3 Group Difference in Topological Properties between Subjects with Long-term Art Training and Untrained Controls 35
3.4 Different Types of Art Training on Network Properties 38
Chapter 4 Discussion 40
4.1 Summary of Main Findings 40
4.2 Training-induced Alterations of Fiber Tracts 42
4.3 Anatomical Coupling of Tracts in Long-term Art Training 51
4.4 Difference in Topological Properties at Different Types of Training 56
Chapter 5 Conclusion 60
Chapter 6 Limitations and Future Directions 61
References 62
Appendix 72

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