本文選題：大腦功能　切入點(diǎn)：特征選擇　出處：《智能系統(tǒng)學(xué)報(bào)》2017年03期

【摘要】：目前腦功能連接網(wǎng)絡(luò)已被廣泛用于大腦疾病診斷,然而傳統(tǒng)的腦網(wǎng)絡(luò)分類方法無法評(píng)估疾病所處的階段以及預(yù)測(cè)病情的發(fā)展。近期的研究表明,腦疾病的臨床變量值可以有效地幫助醫(yī)生進(jìn)行疾病評(píng)估,為此提出一種基于腦連接網(wǎng)絡(luò)的方法,用于對(duì)阿爾茨海默病臨床變量值進(jìn)行預(yù)測(cè)。首先從腦影像中提取功能連接網(wǎng)絡(luò),然后使用LASSO進(jìn)行特征選擇,剔除不具有判別性的邊。同時(shí)融合網(wǎng)絡(luò)的聚類系數(shù)和邊的權(quán)重作為特征。最后使用支持向量回歸機(jī)預(yù)估臨床變量值。在ADNI數(shù)據(jù)集上對(duì)提出的方法進(jìn)行驗(yàn)證,實(shí)驗(yàn)結(jié)果表明,提出的方法不僅能夠準(zhǔn)確地預(yù)測(cè)疾病臨床變量值而且還驗(yàn)證了多種特征融合的有效性。
[Abstract]:At present, brain functional connectivity network has been widely used in the diagnosis of brain diseases. However, traditional brain network classification methods can not assess the stage of disease and predict the development of disease. Recent studies have shown that, The clinical variable value of brain disease can effectively help doctors to evaluate the disease. Therefore, a method based on brain connection network is proposed to predict the clinical variable value of Alzheimer's disease. Firstly, the functional connection network is extracted from brain image. Then use LASSO for feature selection, At the same time, the clustering coefficient and edge weight of the network are fused as the features. Finally, the support vector regression machine is used to predict the clinical variables. The proposed method is verified on the ADNI dataset. The experimental results show that, The proposed method can not only accurately predict the clinical variables of disease, but also verify the validity of multiple feature fusion.
【作者單位】： 南京航空航天大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61422204,61473149) 江蘇省杰出青年基金項(xiàng)目(BK20130034) 高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金課題(20123218110009) 南京航空航天大學(xué)基本科研業(yè)務(wù)費(fèi)項(xiàng)目(NE2013105)
【分類號(hào)】：O212.1;R749.16

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