動作抑制是指一個人停止正在執行中但是卻已經不再適合的動作，這是一個在日常生活當中非常重要的能力。然而在過去的研究當中，大腦網絡之間的連結是否能夠用來解釋動作抑制的個別差異尚未明朗。因此，本論文使用圖形理論的觀點來檢驗17個由Shaefer等（2018）定義之大腦網絡的特徵與動作抑制之間的關聯。

本論文包含三個研究：研究一探討靜息態功能性的網路特徵與動作抑制之間的關聯；研究二探討灰質體積網絡（結構網絡）的特徵與動作抑制之間的關聯；研究三利用不同的機器學習模型來預測動作抑制，以作為未來的實際應用。本論文分析141名年齡介於20到78歲的健康成年人在靜息態時的腦功能性及結構性的磁振造影資料，以及其在執行停止訊號作業時的表現。本論文使用的圖形理論之特徵包含代表網絡內連結程度的模塊內連結程度（within-module-degree）以及代表網絡間連結程度的參與係數（participation coefficient）。研究一和二使用多元線性回歸和留一交叉驗證來檢驗圖形理論的特徵與動作抑制之間的相關。研究三使用八種機器學習模型預測動作抑制的能力，並使用均方根差（root mean square error）來選擇和評估模型，同時也計算最優模型的判定係數（R2）及平均絕對誤差（mean absolute error）來評估模型。其中，機器學習模型包含多元線性迴歸、最小絕對值收斂和選擇算子（LASSO）、脊回歸（ridge regression）、彈性網絡（elastic net）、支持向量回歸（support vector regression）、關聯向量機（relevance vector machine）、高斯過程回歸（gaussian process regression）、偏最小二乘回歸（partial least square regression）。

在結果方面，研究一發現：警覺腹側注意網絡（salient ventral attention network）與背側注意網絡（dorsal attention network）的網絡間功能性連結程度擁有顯著動作抑制之個別差異的解釋力。研究二發現：警覺腹側注意網絡、預設模式網絡（default mode network）的網絡間結構性連結程度以及預設模式網絡的網絡內結構性連結程度擁有顯著動作抑制之個別差異的解釋力。研究二也展示出警覺腹側注意網絡的網絡間功能性、結構性連結程度同時可以顯著解釋動作抑制的個別差異，這表示這個網絡與動作抑制相關的功能性活化可能與其灰質體積表現有關。研究三的不同機器學習模型也顯示，在研究一和二中擁有最高解釋力的功能性與結構性的網絡特徵也同樣展現出最好的動作抑制能力的預測力。此外，研究三也顯示：配合最小絕對值收斂和選擇子、彈性網路模型，預設網絡模式的網絡間結構性連結程度具有最好的預測力。

總結而言，研究一和二的結果都有呼應過去的功能性及結構性影像研究：指出額葉扣帶迴頂葉網絡（frontal-cingulate-parietal network）及其子網絡可以解釋動作抑制的個別差異。除此之外，研究一和研究二進一步區分出這些子網絡與動作抑制相關的網絡連結種類。尤其在所有子網絡中，只有覆蓋了額下回（inferior frontal gyrus）及前運動輔助區（pre-supplementary motor area）的警覺腹側注意網絡同時在功能性及結構性網絡特徵可以解釋動作抑制的個體差異，並表現在其網絡間連結的特徵上。這表示在警覺腹側注意網絡的神經元密度可能影響其動作抑制相關的靜息態皮質活化，而其他顯著子網絡（包含背側注意網絡及預設模式網絡）的神經元密度和其動作抑制相關的靜息態皮質變化是互相獨立的。其中，與預設網絡內與動作抑制相關的神經元密度可能是由負責錯誤及行為監控的網絡間連結以及負責動作抑制的網絡間連結組成。另一方面，研究三使用不同機器學習模型也驗證了研究一和二的結果，尤其使用可以處理多重共線性的彈性網路模型可以有最好的預測表現。因此本論文也建議彈性網路模型可以套用在未來功能性及結構性網絡特徵的研究上。此外，研究三也發現結構性網絡特徵相較於功能性網絡特徵有比較好的預測能力。總體來說，本論文提供了對於使用功能性及結構性網絡特徵來預測動作抑制的結果及觀點，並可以使用在評估動作抑制缺損的臨床族群。

Motor inhibition refers to the ability to refrain from someone’s ongoing action that is no longer suitable and is crucial for daily activities. However, whether a brain network connecting spatially distinct brain regions can explain individual differences in motor inhibition is unknown. Therefore, the present thesis took a graph-theoretic perspective to examine the relationship between the properties of 17 brain networks defined by Schaefer et al. (2018) and motor inhibition.

The present thesis contains three studies: Study 1 focused on the relationship between properties of the resting-state functional network and motor inhibition; Study 2 focused on the relationship between properties of the gray matter volume network (or structural network) and the motor inhibition; Study 3 focused on the future practical application of predicting motor inhibition with brain properties by using machine learning algorithms that adopted by the previous neuroimaging studies. The present thesis analyzed data from 141 healthy adults aged 20 to 78, who underwent resting-state functional and structural magnetic resonance imaging and performed a stop-signal task along with neuropsychological assessments outside the scanner. The graph-theoretic properties used in the preset thesis included within-module-degree, a measurement of within-network connectivity, and participation coefficient, a measurement of between-network connectivity. Study 1 and 2 employed multiple linear regression with leave-one-out cross-validation to examine how these graph-theoretic properties were associated with motor inhibition. Study 3 applied eight machine learning algorithms to predict motor inhibition, used the root mean square error for selecting and assessing the model, and also calculated the selected models’ R2 and mean absolute error for model assessment. These machine learning algorithms included multiple linear regression, least absolute shrinkage and selection regression (LASSO), ridge regression, elastic net, support vector regression, relevance vector machine, Gaussian process regression, and partial least square regression.

For the results, the results of Study 1 showed that between-network functional connectivity of the salient ventral attention network and dorsal attention network significantly explained the highest and second-highest variances of individual differences in motor inhibition. Study 2 showed that the between-network structural connectivity of the salient ventral attention network and default mode network and within-network structural connectivity of default mode network significantly explained the top three variances of individual differences in motor inhibition. Study 3 showed that the top significant results of Study 1 and 2 also had the best predictability among functional or structural network properties. Also, Study 3 showed that the between-network structural connectivity of the default mode network has the best predictability to motor inhibition with LASSO and elastic net.

To summarize, the findings of Study 1 and 2 echoed the previous functional- and structural-imaging studies that the sub-networks within the frontal-cingulate-parietal network could explain the individual difference of motor inhibition. Additionally, Study 1and 2 further distinguished the type of network connectivity regarding motor inhibition for those sub-networks. Especially, among these sub-networks, only the between-network connectivity of salient ventral attention network, which covers the inferior frontal gyrus and pre-supplementary motor area, could explain the variance of individual difference in motor inhibition with both functional- and structural-network properties, suggesting that the neuron density may interact with the predictive cortical activation in the resting-state for motor inhibition in this sub-network, while the neuron density and the cortical activation in the resting-state may be independent for motor inhibition in the other significant sub-networks including dorsal attention network and default mode network. In particular, our findings suggest that the neuron density within the default mode network correlates to motor inhibition by composing within-network connectivity which is responsible for the error and behavior monitoring and between-network connectivity which is responsible for motor inhibition. On the other hand, the findings in Study 3 verified the findings in Study 1 and 2 with eight machine learning algorithms, especially with elastic net which can deal with the multicollinearity problem and is recommended for future studies of functional- and structural-network properties. Also, Study 3 showed that the structural-network property had better prediction performance than the functional-network properties. Overall, the present thesis provides new insight into the predictive values of functional- and structural-network properties and their implication for assessing motor inhibition deficit in the clinical population.

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