從1980年代被提出的擴散磁振造影理論(Diffusion MRI theory)至1994年推導出的擴散張量磁振造影(diffusion tensor imaging)的理論，擴散磁振造影在現今已具有高重現性，擴散磁振造影由一開始的結構影像進階到具有向量資訊的腦神經影像，不難見得在非侵入式的醫學診斷儀器發展得相當蓬勃，此項技術應用於判斷血性腦中風，可以準確評估腦部、肝臟腫瘤的治療效果。擴散張量影像技術，它主要應用在神經疾病的研究。更進一步的使用神經纖維成像技術來檢測神經網路。神經纖維成像技術，在追蹤神經網路的做法可以分為確定型神經徑路追蹤演算法(deterministic tractography methods)，機率型神經徑路追蹤演算法(probabilistic tractography methods)。在過去的研究中，所使用的確定型追蹤法已有一定的發展在於白質區域的神經追蹤，但是比較困難去描述神經在擴散方向不明確的灰質區域。我們所使用的機率型神經徑路追蹤演算法，追蹤腦迴區域間的神經纖維。透過區分不同的大腦皮質區域，並計算各個腦迴區域之間的神經連結。我們的研究運動為棒球，並將其分成三組，Skilled group(S組)：大專盃甲組(公開組)選手，包含擁有棒球專項體育保送生、體育資優生；Intermediate group(I組)：大專盃乙組(一般組)選手，或是擁有類似層級的比賽經驗，如：系際盃、社會棒球聯賽等；Control group(C組)：無棒球運動經驗者(普通體育課除外)。三組各15名受試者，共45人。我們以灰質腦區的連結機率、區域非等向性(Fractional anisotropy)和平均擴散(mean diffusivity)數據來判斷，FA的數值為0~1之間，數值越大越表此部分水分子越以單一方向擴散，臨床上可能代表神經纖維密度(fiber density)或髓鞘化(myelination)的程度。我們在灰質部分發現Paracentral Lobule、Precentral這些腦區在S組與C組受試者有顯著差異，並且也具有較大的連結機率，在白質發現Posterior corona radiata、Superior longitudinal fasciculus中S組與C組同樣有著顯著差異。本研究以棒球運動員為對象，探討長期訓練下對與腦部連結性的改變，初步結果已發現白質組織擴散影像的連結特性會因為訓練而造成群組間的差異，同時以會在灰質間的連結性找到群組間的差異，此部分結果未來可進一步與其他結構資訊(皮質厚度、體積)做比較。

擴散權重影像與神經纖維追蹤可以用來探討腦區域之間的連結性，目前透過網路分析方式已經證實腦網路是有小世界的特性，最近也有研究不同受試者或者是病人之間的網路連結量測集中程度，但是擴散權重影像所運算出來的網路參數中間要經過很多步驟，這些中間步驟可能會影響到網路參數。所以有必要對於量測網路參數的受試者間變異性和重複量測重現性進行研究。本研究的目標是利用機率式神經纖維追蹤術量測大腦網路參數的重現性，探討三個會影響計算網路參數的重現性的變因，分別是，路徑定義方式、有無損耗正規化、受試者群體的網路連結篩選機制。變異係數定義（Coefficient of Variance, CV）為標準差除以平均值，分別計算二次量測之間的變異係數（CVwithin），以及受試者之間的變異係數（CVbetween），另外也計算組內相關係數（Intraclass correlation coefficient, ICC）。 掃描30受試者（15男，15女，年齡20~26）。每人掃描二次，並利用機率式神經纖維追蹤術計算網路連結，網路節點則是使用AAL標準模板定義的節點。若使用Wij = 1 – Pij定義長度，三項網路參數（區域效率、全域效率及損耗）重現性皆可接受（CVwithin<1.08%, CVwithin ≤ 10% and ICC > 0.7）。如果使用Wij=1/Pij定義長度，其損耗的CVwithin相較於Wij = 1 – Pij的大。如果長度的全距大，區域效率會不尋常地增加。如果二次掃描分別實施連結篩選，全域效率的CVwithin會較大。 本研究探討不同的網路建構方式將會影響測試內重現度，不同的研究團隊，縱使是採用相同的受試者群體和相同的儀器，所發表出來的網路參數可能會因為纖維追蹤術造成的誤差而不一致，因此實驗必須謹慎的分析資料以及闡述結果。 / Diffusion tensor imaging (DTI) with associate tractography can be used to access the connectivity of cortical regions in brain. Network analysis applied to connectivity matrix has demonstrated that brain has small world property. Recent studies also use network analysis to study the variation of concentricity among different group of subjects and patients. However the estimation of network metrics from DTI takes sophisticated processing steps. These intermediate steps may influence the estimation of network metric. It is therefore needed to investigate the potential variation of estimated network metrics using reproducibility test. The goal is to study the reproducibility of network properties derived from diffusion connectivity matrix constructed using probabilistic tractography. The effects of three factors on the reproducibility of network metrics estimation were studied. They are definition of path lengths of network matrix, path with and without cost normalization, the application of threshold to subjects groups. Coefficient of Variation (CV) defined as standard deviation divided by mean is used to test the intra-session (CVwithin) and inter subject (CVbetween) variability. Intra-class correlation coefficient (ICC) was also calculated. Images were acquired from 30 healthy participants (15 male, 15 female, aged 20-26 years). Each subject was scanned twice, denoted as N1 and N2. Probabilistic tractography was performed to mapping of cortico-cortical anatomical connections between regions defined from an anatomical atlas. All three of the tested network metrics (local efficiency, global efficiency and cost) were identified as acceptable (CVwithin < 1.08%, CVwithin ≤ 10% and ICC > 0.7) using path length defined as Wij = 1 – Pij. When the path length is defined as Wij = 1/Pij, cost showed higher CVwithin compared to Wij = 1 – Pij. It is unusual that local efficiency increase when the range of path length of edges is large. Global efficiency showed higher CVwithin as threshold is applied to N1 and N2 separately compared to both scans together. The present study revealed that different ways to construct cortical network had an effect on intra-session reproducibility. Our study also showed that despite evaluation of identical subjects using the same MRI system, variation of network metrics may be found by different research groups due to the potential errors from tractography. Replication of the experiment need to be carefully analyzed and interpreted.