發(fā)布時(shí)間：2018-04-23 15:09

  本文選題：遺忘型輕度認(rèn)知障礙 + 灰質(zhì)體積�。� 參考：《醫(yī)學(xué)研究生學(xué)報(bào)》2014年08期

【摘要】：目的近年來多元模式分析(multivariate pattern analysis,MVPA)方法的出現(xiàn)被認(rèn)為是可以對(duì)各種神經(jīng)精神疾病進(jìn)行自動(dòng)化識(shí)別的很有前途的工具,支持向量機(jī)(support vector machine,SVM)則是一種最廣泛使用的MVPA方法。文中采用SVM分類器對(duì)遺忘型輕度認(rèn)知障礙(amnestic mild cognitive impairment,aMCI)患者和無記憶障礙及其他相關(guān)疾病者進(jìn)行MVPA研究,旨在構(gòu)建具有較高判別能力的個(gè)體診斷模型,并從多變量分析的角度來解析aMCI患者的灰質(zhì)損傷模式。方法采用3.0T磁共振對(duì)51例aMCI患者和68例正常對(duì)照者進(jìn)行高分辨率三維T1-weighted掃描,為每個(gè)受試者計(jì)算灰質(zhì)體積圖譜,該圖譜用于之后的判別分析。使用特征選擇方法去除冗余信息后訓(xùn)練SVM分類器,使用留一交叉驗(yàn)證估計(jì)分類器的性能,最后識(shí)別出最有判別能力的灰質(zhì)模式。結(jié)果該方法的分類準(zhǔn)確率為83.19%,敏感性為76.47%,特異性為88.24%,接收者操作特性曲線下的面積是0.8368。對(duì)分類貢獻(xiàn)最大的灰質(zhì)區(qū)域包括雙側(cè)海馬旁回、雙側(cè)海馬、雙側(cè)杏仁核、雙側(cè)丘腦、右側(cè)扣帶回、右側(cè)楔前葉、左側(cè)尾狀核、左側(cè)顳上回、左側(cè)顳中回、左側(cè)島葉以及左側(cè)眶額皮層。結(jié)論構(gòu)建的分類模型對(duì)aMCI患者具有較好的識(shí)別能力,可顯示aMCI患者全腦灰質(zhì)萎縮情況,對(duì)臨床早期診斷aMCI患者有重要意義。
[Abstract]:Objective in recent years, the multivariate pattern analysis (MVPA) method is considered to be a promising tool for the automatic identification of various neuropsychiatric disorders, and support vector machine (SVM) is the most widely used MVPA method. In this paper, SVM classifier was used to study MVPA in amnestic mild cognitive impairment of amnesia and those without memory disorder and other related diseases. The purpose of this study was to construct a diagnosis model with high discriminant ability. The gray matter damage model of aMCI patients was analyzed by multivariate analysis. Methods High-resolution three-dimensional T1-weighted scanning was performed on 51 patients with aMCI and 68 normal controls with 3.0T magnetic resonance imaging. The gray matter volume map was calculated for each subject and used for later discriminant analysis. The feature selection method is used to train the SVM classifier after the redundant information is removed, and the performance of the classifier is estimated by using a cross-validation method. Finally, the gray matter pattern with the most discriminating ability is identified. Results the classification accuracy of this method is 83.19, the sensitivity is 76.477.The specificity is 88.24.The area under the operating characteristic curve of receiver is 0.8368. The areas of gray matter that contribute most to the classification include bilateral perihippocampal gyrus, bilateral hippocampus, bilateral amygdala, bilateral thalamus, right cingulate gyrus, right anterior cuneate lobe, left caudate nucleus, left superior temporal gyrus, left middle temporal gyrus. Left insular lobe and left orbital frontal cortex. Conclusion the classification model has good recognition ability for aMCI patients and can show the whole brain gray matter atrophy in patients with aMCI. It is of great significance for early diagnosis of aMCI patients.
【作者單位】： 首都醫(yī)科大學(xué)宣武醫(yī)院神經(jīng)內(nèi)科;牡丹江醫(yī)學(xué)院紅旗醫(yī)院;北京師范大學(xué)認(rèn)知神經(jīng)科學(xué)與學(xué)習(xí)國(guó)家重點(diǎn)實(shí)驗(yàn)室&IDG/麥戈文腦科學(xué)研究院;牡丹江醫(yī)學(xué)院紅旗醫(yī)院神經(jīng)內(nèi)科;首都醫(yī)科大學(xué)宣武醫(yī)院放射科;
【基金】：國(guó)家自然科學(xué)基金(81261120571,30970823,31371007) 北京市科委首都市民健康培育項(xiàng)目(Z131100006813022)
【分類號(hào)】：R749.16

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