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基于動(dòng)態(tài)圖論特征的阿爾茨海默病早期預(yù)測(cè) Early prediction of Alzheimer disease based on dynamic graph theory |
| 作者： 董國(guó)昭 楊柳 張逸鶴 張勇 唐曉英 |
| 單位：北京理工大學(xué)生命學(xué)院(北京 100081); 首都醫(yī)科大學(xué)宣武醫(yī)院神經(jīng)內(nèi)科(北京100032) |
| 關(guān)鍵詞： 阿爾茨海默病； 主觀認(rèn)識(shí)下降； 靜息態(tài)功能核磁共振； 動(dòng)態(tài)圖論； 時(shí)域變換靈活性； 空間分布廣泛性 |
| 分類號(hào)：R318.04 |
| 出版年·卷·期（頁(yè)碼）：2019·38·6（560-567） |
| 摘要： |
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目的 阿爾茨海默病是常見(jiàn)的神經(jīng)退行性疾病，而主觀認(rèn)知下降可發(fā)生于阿爾茨海默病臨床前期，但傳統(tǒng)核磁共振數(shù)據(jù)分析對(duì)于主觀認(rèn)知下降的區(qū)分度較低。本研究通過(guò)動(dòng)態(tài)圖論特征在核磁共振數(shù)據(jù)中的應(yīng)用，研究主觀認(rèn)知下降患者腦動(dòng)態(tài)功能連接的變化。方法 隨機(jī)納入30例主觀認(rèn)知下降患者以及年齡、性別等相匹配的30例健康受試者的靜息態(tài)核磁共振數(shù)據(jù)，分別計(jì)算兩組受試者時(shí)域變換靈活性和空間分布廣泛性兩個(gè)動(dòng)態(tài)圖論特征，同時(shí)構(gòu)建基于支持向量機(jī)的分類器，研究動(dòng)態(tài)圖論特征的分類效果，分析兩組受試者動(dòng)態(tài)圖論特征的主要分類貢獻(xiàn)腦區(qū)的變化差異。結(jié)果 研究發(fā)現(xiàn)主觀認(rèn)知下降患者在額上回、楔前葉等認(rèn)知相關(guān)腦區(qū)的動(dòng)態(tài)圖論特征較之于正常受試者有顯著減弱，且動(dòng)態(tài)圖論特征在分類中能夠獲得較高的準(zhǔn)確度，優(yōu)于傳統(tǒng)靜態(tài)圖論特征。結(jié)論 動(dòng)態(tài)圖論特征能夠分析主觀認(rèn)知下降患者腦動(dòng)態(tài)功能連接的變化，為阿爾茨海默病的早期診斷提供理論依據(jù)。 |
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Objective Alzheimer disease is the most common neurodegenerative disease and subjective cognitive decline (SCD) is the early stage of Alzheimer disease. However, the recognition for SCD of traditional MRI analysis is low. This paper aims to find the changes of dynamic functional connectivity in SCD patients. Methods Temporal flexibility and spatiotemporal diversity of 30 individuals with SCD and 30 matched normal controls were calculated by using the resting-state functional MRI data. Classifiers based on support vector machine were constructed to study the classification effect of dynamic graph theory parameters. The differences of main brain regions chose by dynamic graph theory parameters were analyzed. Results We found that the dynamic graph features of cognitive-related brain regions such as frontal gyrus and anterior wedge of SCD patients were significantly weaker than normal subjects, demonstrated higher classification accuracies than conventional static parameters. Conclusions Dynamic graph theory features can reveal the changes of brain dynamic function connections of SCD patients, providing a theoretical basis for the early diagnosis of Alzheimer disease. |
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