北京生物醫(yī)學(xué)工程

基于靜息態(tài)fMRI信號(hào)復(fù)雜度的MCI識(shí)別研究

MCI recognition based on the complexity of resting state fMRI signal

作者：董建鑫王川

單位：首都醫(yī)科大學(xué)燕京醫(yī)學(xué)院（北京 101300） <br />通信作者：董建鑫。E-mail: [email protected] 

關(guān)鍵詞：輕度認(rèn)知障礙；靜息態(tài)功能磁共振成像；Hurst指數(shù)；獨(dú)立成份分析；支持向量機(jī)

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2022·41·6（564-568）

摘要：

目的基于靜息態(tài)功能磁共振圖像，提取默認(rèn)網(wǎng)絡(luò)特征腦區(qū)的信號(hào)復(fù)雜度參數(shù)建立輕度認(rèn)知障礙（mild cognitive impairment, MCI）的分類模型。方法研究數(shù)據(jù)來(lái)源于阿爾茨海默癥神經(jīng)成像數(shù)據(jù)庫(kù)，包含48名正常對(duì)照人群和53位MCI。首先進(jìn)行獨(dú)立成份分析，針對(duì)分離出的獨(dú)立成份分別計(jì)算對(duì)應(yīng)時(shí)間序列的Hurst指數(shù)。然后在體素水平上采用雙樣本T檢驗(yàn)選擇左側(cè)眶部額下回、左側(cè)額上回和左側(cè)額中回作為特征腦區(qū)，計(jì)算其Hurst指數(shù)作為分類特征。最后用支持向量機(jī)對(duì)MCI患者進(jìn)行識(shí)別，并評(píng)價(jià)模型的準(zhǔn)確率、靈敏度、特異度以及接收操作特征（receiver operating characteristics, ROC）曲線下面積。結(jié)果基于MCI和正常對(duì)照兩組構(gòu)建的分類模型，獲得了最高88.71%的分類準(zhǔn)確率、90.91%的靈敏度和86.21%的特異度。此外，ROC曲線的最大線下面積為0.96。結(jié)論 Hurst指數(shù)可以反映MCI患者異常腦功能活動(dòng)，基于獨(dú)立成份分析和支持向量機(jī)的方法能有效地識(shí)別MCI患者，具有一定的臨床輔助診斷意義。

Objective Based on resting-state functional magnetic resonance images, signal complexity parameters of the default network characteristic brain regions were extracted to establish the classification model of mild cognitive impairment (MCI). Methods Data is from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database included 48 normal controls and 53 MCI patients. Firstly, the independent component analysis is carried out, and the Hurst exponent of corresponding time series is calculated for the separated independent components. Then, at the voxel level, two-sample T test was used to select the left orbital part of inferior frontal gyrus, left superior frontal gyrus and left middle frontal grus as characteristic brain regions, and their Hurst exponent was calculated as classification features. Finally, support vector machine was used to identify MCI patients, and the accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve of the model were evaluated. Results The classification model based on MCI and normal control group obtained the highest classification accuracy of 88.71%, sensitivity of 90.91% and specificity of 86.21%. In addition, the maximum area under ROC curve was 0.96. Conclusions Hurst exponent can reflect abnormal brain functional activities of MCI patients. Methods based on independent component analysis and support vector machine can effectively identify MCI patients, which has certain clinical diagnostic significance.

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