本文關鍵詞：強直少動型帕金森病的腎虛血瘀證候評分與腦結構網絡相關性研究　出處：《北京中醫(yī)藥大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 帕金森病 腦結構網絡 中醫(yī)證候 癥狀量表評分

【摘要】：研究背景:隨著近年來對帕金森病理認識的進步,發(fā)現(xiàn)帕金森病理改變不始于亦不局限于中腦黑質,因其累及多處部位,癥狀多樣,不易早期診斷。研究者試通過多種方式總結帕金森病癥狀規(guī)律特征,提高帕金森病的早期診斷率,以及早治療,改善患者預后。強直少動型帕金森病是以動作遲緩、肌強直為特征的帕金森病亞型,因其震顫癥狀不顯,早期診斷更為困難。已有研究證明中藥益腎逐瘀法治療強直少動型帕金森病可有效改善患者多種癥狀評分,包括運動癥狀及非運動癥狀(自主神經功能、精神癥狀、感覺障礙、睡眠障礙),且腦功能連接也在療后有所改變,但中醫(yī)證候與腦結構網絡是否存在相關性仍有待進一步研究。"證候"是中醫(yī)實現(xiàn)個體化治療的根本依據(jù)。關于"證"的來源與發(fā)展問題一直為中醫(yī)學所關注�，F(xiàn)代醫(yī)學用多種科技手段研究中醫(yī)證候與現(xiàn)代理化指標之間的關系,以此論證中醫(yī)"證候"概念并非僅僅只是歷史經驗總結及疾病根關癥狀堆砌,更存在共性的內在生物學基礎。研究證候評分成為該探索過程中重要的量化指標。支持向量機(SVM)是一種較為先進的監(jiān)督學習方法,可用于解決一些樣本小,維度高的統(tǒng)計學問題。本研究試用支持向量機(Support Vector Machine,SVM)分類(SVC)及回歸(SVR)方法比較治療前PD組與正常人之間腦結構性網絡差異,分析各癥狀量表評分(包括中醫(yī)證候量表評分)與腦結構網絡相關性。研究目的:(1)比較強直少動型PD患者不同于正常人的腦結構網絡特征,為帕金森病精確診斷提供輔助手段;(2)比較三種PD量表評分(NMSS、UPDRS、TCM)與腦結構網絡的相關性;(3)分析強直少動型帕金森病腎虛血瘀證是否存在腦結構網絡特征性改變?yōu)樵撟C候的內在生物學基礎。研究方法:本研究依照試驗標準由北京中醫(yī)藥大學東直門醫(yī)院納入9例強直少動型帕金森病患者,并予口服中藥——培元解痙湯加減治療,分別于治療前后行NMSS(帕金森病非運動癥狀量表)、UPDRS(統(tǒng)一帕金病森評分量表Ⅲ)、TCM(PD中醫(yī)證候量化分級表)三種量表評分,并采集核磁影像,將之與9名與治療組年齡、文化程度基本匹配的正常人核磁影像對照研究。采用FreeSUrfer5.3進行皮層分割,FATCAT工具包行白質纖維追蹤,分別按確定性算法(Deterministic tractography,DET),簡略概率算法(Mini-Probabilistic Tracking,MINIP)和全概率算法(Probabilistic Tracking,PROB)計算纖維連接。用支持向量機(Support Vector Machine,SVM)分類(SVC)比較治療前PD組與正常人之間腦結構性網絡的差異,并行交叉驗證。采用支持向量機回歸(SVR)分析三種量表評分(NMSS、UPDRS、TCM)與腦結構網絡相關性。研究結果:1.強直少動型帕金森病患者與正常人相比存在腦結構網絡的特異性改變;其NMSS、UPDRS、TCM癥狀量表評分與腦連接改變具有相關性。2.SVC中對識別PD組貢獻度大的連接以中央前回、間腦腹側、額下回蓋部為主要節(jié)點,以中央前回-額下回蓋部、中央前回-間腦腹側的連接改變?yōu)轱@著;對識別正常對照組貢獻度大的連接以額上回、豆狀核殼、杏仁核、扣帶回為節(jié)點;以緣上回-島葉、扣帶回-額上回的連接改變?yōu)轱@著。3.與NMSS量表評分正相關的連接主要以間腦腹側、中央前回、頂上小葉、豆狀核殼為節(jié)點,以間腦腹側-中央前回、頂上小葉-豆狀核殼的連接相關性為顯著;負相關連接主要以內嗅皮質、頂下小葉、額上回、緣上回為節(jié)點,以頂下小葉-緣上回,內嗅皮質-杏仁核相關性顯著。4.與UPDRS量表評分正相關的連接主要以豆狀核殼、內嗅皮質、額上回、顳極為節(jié)點,以內嗅皮質-顳極、頂上小葉-豆狀核殼、額上回-間腦腹側、中央前回-島葉連接相關性為顯著;負相關連接主要以海馬、內嗅皮質、丘腦本體為節(jié)點,以海馬-內嗅皮質、丘腦本體-海馬連接相關顯著。5.與TCM量表評分正相關連接主要以舌回、眶外側回為節(jié)點,結構連接以眶外側回-顳上回,舌回-扣帶回、舌回-顳下回相關性顯著;負相關的連接以內嗅皮質、海馬、中央旁小葉為主要節(jié)點,連接中以海馬-內嗅皮質、內嗅皮質-豆狀核殼、海馬-間腦腹側、內嗅皮質-杏仁核、中央旁小葉-中央前回相關性顯著。結論:1.強直少動型帕金森病患者與正常人相比存在腦結構網絡的改變;NMSS、UPDRS、TCM癥狀量表評分與腦連接改變具有相關性;可有助于PD臨床診斷與證候量化;2.以內嗅皮質和基底節(jié)(主要為豆狀核殼、杏仁核)為節(jié)點的結構連接改變符合帕金森病原發(fā)病理改變特征;3.TCM量表相關的連接較NMSS及UPDRS具有個性及規(guī)律性,提示PD腎虛血瘀證不僅為PD癥狀集合,亦存在相應腦結構連接特征為生物學基礎。
[Abstract]:Background: with the pathology of Parkinson in recent years progress, found that Parkinson does not start with the pathological changes was not confined to the substantia nigra, because it involved multiple sites, diverse symptoms, early diagnosis is not easy. The researchers tried through a variety of ways to summarize the characteristic symptoms of Parkinson's disease law, improve the rate of early diagnosis of Parkinson's disease, and early treatment, improve the prognosis of patients with Parkinson disease. Less action is slow, myotonia characterized the Parkinson disease subtypes, because the tremor is not significant, early diagnosis is more difficult. It has been shown that the traditional Chinese medicine of Tonifying the kidney and removing blood stasis in treatment of ankylosing hypokinesia type of Parkinson's disease can effectively improve symptoms in patients with a variety of score. Including motor symptoms and non motor symptoms (autonomic function, psychiatric symptoms, sensory disturbances, sleep disorders), and brain functional connectivity also changes in the after treatment, but the TCM syndrome and brain structure network The correlation remains to be further studied. The "syndrome" is the fundamental basis of TCM individual treatment. The origin and development of "syndrome" in traditional Chinese medicine has been concerned. The relationship between modern medical research in a variety of technical means of traditional Chinese medicine and modern physical and chemical indicators, in order to prove TCM "syndrome" concept not only the summary of the historical experience and disease symptoms of root pile, more inherent biological basis. Research on common syndrome score become important quantitative indicators of the exploration process. Support vector machine (SVM) is an advanced supervised learning method, can be used to solve some small samples, statistical problems of high dimensions. The trial research on support vector machine (Support Vector Machine, SVM) (SVC) classification and regression (SVR) method for brain structural network differences between PD group and normal people, analysis of each symptom scale ( Including the TCM Syndrome Scale scores) associated with structural brain networks. Objective: (1) compare the features of brain structure rigidity dynamic network of PD were different from normal people, provide supplementary means for accurate diagnosis of Parkinson's disease; (2) the score of three form PD (NMSS, UPDRS, TCM) correlation with the brain structure of the network; (3) analysis with less dynamic Parkinson disease of kidney deficiency and blood stasis in the existence of brain structure network characteristic changes for the syndrome of internal biological basis. Methods: in this study, in accordance with the standard test by the Beijing University of Chinese Medicine Dongzhimen hospital in 9 cases of patients with Parkinson's disease with less, and with oral administration of traditional Chinese Medicine - Peiyuan spasmolysis decoction, NMSS respectively before and after the treatment (non motor symptoms of Parkinson's Disease Rating Scale (UPDRS), unified Parkin's Disease Rating Scale III Sen (PD TCM), TCM Syndrome Scale) three kinds of scale, and acquisition of magnetic The 9 images, and the treatment group and the control of age, normal MRI, basic education. Using FreeSUrfer5.3 cortex segmentation, FATCAT tool kit for white matter fiber tracking, respectively according to the deterministic algorithm (Deterministic tractography DET), a probabilistic algorithm (Mini-Probabilistic Tracking MINIP) and total probability algorithm (Probabilistic Tracking, PROB) calculation of fiber connection. Support vector machine (Support Vector Machine, SVM) classification (SVC) comparison between the treatment group and PD normal human brain structural network difference, parallel cross validation. The support vector machine regression (SVR) analysis of three scores (NMSS, UPDRS, TCM) associated with structural brain networks. Results: 1. patients with Parkinson's disease with less specificity compared with the normal brain structure changes in the network; NMSS, UPDRS, TCM symptom scale score and brain connection With time-varying correlation in.2.SVC to identify the PD group contribution degree in order to precentral gyrus, inferior frontal ventral diencephalon, cover as the main node in the precentral gyrus - inferior frontal gyrus cover, precentral gyrus - ventral diencephalon connections significantly; recognition of the normal control group contribution connection in the superior frontal gyrus, putamen, amygdala, cingulate as nodes; in the supramarginal gyrus insula, cingulate gyrus - connected frontal gyrus change score in the ventral diencephalon is connected to the main related table was.3. and NMSS, precentral gyrus, superior parietal lobule, putamen for nodes. In the diencephalon ventral - precentral gyrus, superior parietal lobule - connected between putamen was significantly negatively correlated; main connection within the entorhinal cortex, inferior parietal lobule, superior frontal gyrus, supramarginal gyrus as nodes, with inferior parietal lobule - supramarginal gyrus, amygdala entorhinal cortex significantly correlated.4. and UPDRS scale. The main points are related connection 浠ヨ眴鐘舵牳澹，

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