比較DKI與DTI在識別帕金森氏症與前驅期的能力

研究背景:帕金森氏症（Parkinson's disease, PD）是種影響中樞神經系統的慢性神經退化疾病，主要影響運動功能，亦可能會影響到情緒或者智力等等，主要病變為黑質多巴胺神經元退化連帶影響基底核所有關於多巴胺牽涉的神經迴路，現階段帕金森氏症還未有根治方法，不過若能早期正確識別並且介入治療將可大大提升預後，因此早期診斷的生物標記相當重要，擴散MRI在偵測大腦微結構變化上具一定靈敏度和準確性，其中擴散峰值成像(Diffusion Kurtosis Imaging,DKI)被譽為是擴散張量成像(Diffusion Tensor Imaging,DTI)的進階版，識別能力是更加出眾的，本篇文章延續比較DKI與 DTI識別能力，不過相較過往大多都是疾病組與健康組的比較，本次對照組變成前驅症狀組，把識別難度提高，看在更為艱難的條件下DKI能否仍舊有更出眾的能力，除此之外還會進一步探討帕金森氏症可能的病理機制還有與帕金森氏症量表搭配看看能否與MRI技術組合出更好的生物嚴重程度指標。 材料與方法: 從PPMI(Parkinson's Progression Markers Initiative)資料庫，選出共45 名受試者進入此次研究，所有受試者均接受3T MRI檢查，另外還會對受試者做帕金森氏症統一評估量表（Unified Parkinsons Disease Rating Scale, UPDRS），蒙特婁認知評估(Montreal Cognitive Assessment, MoCA)，HY分期(Hoehn and Yahr Scale)等常見的帕金森氏症嚴重指數量表，接下來從PPMI每位受試者當中選取最佳切片並針對黑質、殼核、尾狀核、蒼白球在左、右兩側分別圈選ROI，之後在Matlab中的FanDTasia toolbox當中計算DKI和DTI參數，至於數學統計則在R4.5和Matlab中進行，第一是要檢測DKI和DTI的識別能力使用常見的組間T檢定，再來是要嘗試在參數在組間的變化，和圈選ROI左、右側，腦部構造的不同來試著推導出帕金森氏症可能的病理機制，最後則是用斯皮爾曼相關係數（Spearman's Rank Correlation Coefficient）來看帕金森氏症各量表搭配DKI和DTI參數能否組合出更好的生物嚴重程度指標。 結果: 本研究選用前驅期患者作為對照組以提升識別難度，在此條件下DKI仍顯示出相較於DTI更佳的判別能力，特別是在蒼白球的參數上具高度顯著差異，顯示其在困難辨識條件下仍具臨床潛力。本研究亦進一步分析參數變化以探討潛在的病理機制，結果顯示多數腦區在左右側ROI表現出顯著性差異的不對稱分布。部分結構僅在單側出現統計顯著差異，而對側則無顯著性，或P值具明顯落差，此現象與既有文獻中指出帕金森氏症早期至中期常呈現臨床不對稱症狀的觀察一致，進一步支持該病理變化具側向性發展特徵。 於影像參數與臨床評估量表的關聯性分析中，本研究發現部分DKI與DTI參數與UPDRS及HY分期呈現中等程度的相關性（Spearman’s ρ 值接近0.40），顯示這些參數可能具備反映疾病嚴重程度之潛力。 MoCA在與尾狀核相關的參數中，仍觀察到相對較佳之相關性。由於尾狀核與認知功能密切相關，該結果提示此構造之變化可能與帕金森氏症早期認知功能下降有潛在關聯，未來可作為進一步研究的方向。 結論:本研究表明DKI在識別難度提升的情況下仍舊有很好的判斷能力，除此之外透過參數變化也能試著去解釋一些可能的病理機制，與帕金森氏症量表也有部分不錯的搭配效果，因此對於DKI成為良好的帕金森氏症生物指標有著光明的未來。

Introduction: Parkinson’s disease (PD) is a chronic neurodegenerative disorder that affects the central nervous system, primarily impairing motor functions, but may also impact mood and cognitive abilities. The core pathology involves the degeneration of dopaminergic neurons in the substantia nigra, subsequently affecting all dopaminerelated circuits in the basal ganglia. Although no definitive cure exists at present, early and accurate identification followed by timely intervention can greatly improve prognosis. Therefore, the search for early diagnostic biomarkers is of critical importance. Diffusion MRI is known for its sensitivity and accuracy in detecting microstructural changes in the brain. Among these, Diffusion Kurtosis Imaging (DKI) is regarded as an advanced version of Diffusion Tensor Imaging (DTI), with superior discriminative ability. This study continues the comparison between DKI and DTI in terms of diagnostic performance; however, unlike previous studies that mostly compared disease groups with healthy controls, this research uses a prodromal symptom group as the control, thereby increasing the difficulty of discrimination. The aim is to evaluate whether DKI can maintain its superior capability under more challenging conditions. Furthermore, we explore the potential pathological mechanisms of PD and examine whether combining MRI parameters with clinical rating scales can provide better biomarkers for disease severity. Materials and Methods: A total of 45 subjects were selected from the Parkinson’s Progression Markers Initiative (PPMI) database. All participants underwent 3T MRI scanning and completed several clinical assessments, including the Unified Parkinson’s Disease Rating Scale (UPDRS), the Montreal Cognitive Assessment (MoCA), and the Hoehn and Yahr (HY) staging scale. For each participant, the optimal MRI slice was selected, and Regions of Interest (ROIs) were manually delineated for the substantia nigra, putamen, caudate nucleus, and globus pallidus on both the left and right hemispheres. DKI and DTI parameters were calculated using the FanDTasia toolbox in MATLAB. Statistical analyses were performed using R version 4.5 and MATLAB. The first step was to evaluate the discriminative power of DKI and DTI using common group-wise t-tests. Subsequently, we examined how these parameters varied across groups, hemispheres, and brain structures in order to infer possible pathological mechanisms of PD. Finally, we applied Spearman’s rank correlation coefficient to explore whether DKI and DTI parameters correlate with clinical rating scales, potentially serving as biomarkers of disease severity. Results: By using prodromal-phase patients as the control group to increase diagnostic challenge, DKI still demonstrated superior discriminative power compared to DTI, particularly in parameters related to the globus pallidus, where highly significant differences were observed. This indicates that DKI retains clinical potential even under difficult classification conditions. Further analysis of parameter asymmetry revealed significant lateralized distributions across most brain regions. Some structures showed statistical significance only on one side, while the contralateral side did not, or displayed clearly different p-values. This aligns with existing literature suggesting that early-to-mid-stage PD often presents with asymmetric clinical symptoms, supporting the notion of lateralized pathological progression. In correlation analyses between imaging parameters and clinical rating scales, several DKI and DTI parameters showed moderate correlations with UPDRS and HY staging (Spearman’s ρ approximately 0.40), suggesting their potential to reflect disease severity. Notably, parameters related to the caudate nucleus showed relatively better correlations with MoCA scores. Given the caudate nucleus’s known involvement in cognitive function, these findings suggest that structural changes in this region may be associated with early cognitive decline in PD, and could be a target for future investigations. Conclusion: This study demonstrates that DKI maintains excellent diagnostic performance even under increased difficulty conditions. In addition, the variation of imaging parameters provides potential explanations for PD pathology, and some parameters show promising associations with clinical rating scales. These findings suggest a bright future for DKI as a reliable biomarker in Parkinson’s disease.