北京生物醫(yī)學(xué)工程

基于靜息態(tài)功能性磁共振成像的個(gè)體認(rèn)知多標(biāo)簽分類

Multi-label classification of individual cognition based on resting state functional magnetic resonance imaging

作者：吳怊慧

單位：北京交通大學(xué)計(jì)算機(jī)與信息技術(shù)學(xué)院（北京１０００４４）

關(guān)鍵詞：靜息態(tài)功能性磁共振成像；個(gè)體認(rèn)知；機(jī)器學(xué)習(xí)；多標(biāo)簽分類；算法對(duì)比

分類號(hào)：R318.04

出版年·卷·期（頁碼）：2020·39·2（138-144）

摘要：

目的與單標(biāo)簽分類相比,多標(biāo)簽分類在現(xiàn)實(shí)世界中更為常見。在神經(jīng)影像學(xué)領(lǐng)域,對(duì)個(gè)體認(rèn)知單標(biāo)簽分類的研究有很多,但是卻沒有關(guān)于個(gè)體認(rèn)知多標(biāo)簽分類的研究。本研究嘗試運(yùn)用機(jī)器學(xué)習(xí)多標(biāo)簽分類算法,利用靜息態(tài)fMRI數(shù)據(jù),對(duì)個(gè)體認(rèn)知進(jìn)行多標(biāo)簽分類研究。方法基于390 名(≥18 歲)成年人的靜息態(tài)fMRI數(shù)據(jù),通過5種具有代表性的機(jī)器學(xué)習(xí)多標(biāo)簽分類算法: ML-kNN、hMuLab、LIFT、ML-LOC 和 GLOCAL 對(duì)個(gè)人認(rèn)知進(jìn)行多標(biāo)簽分類,采用十折交叉驗(yàn)證進(jìn)行訓(xùn)練和測試,并用多標(biāo)簽分類常用的評(píng)價(jià)指標(biāo)進(jìn)行結(jié)果的檢驗(yàn)。結(jié)果這5種算法都能用于靜息態(tài)fMRI的多標(biāo)簽分類研究,且經(jīng)過算法對(duì)比發(fā)現(xiàn)hMuLab算法的分類效果最好。結(jié)論本研究成功地將多標(biāo)簽分類算法應(yīng)用于神經(jīng)影像學(xué)領(lǐng)域并對(duì)個(gè)體認(rèn)知進(jìn)行多標(biāo)簽分類,且通過算法對(duì)比發(fā)現(xiàn)樣本的鄰域信息對(duì)分類結(jié)果很重要。

Objective Compared with single label classification, multi-label classification is more common in the real world.In the field of neuroimaging,there are many studies on single label classification of individual cognition,and no research on multi-label classification of individual cognition.We try to use machine learning multi-label classification algorithm and resting state fMRI data to study the multi-label classification of individual cognition.Methods Based on the resting state fMRI data of 390 adults ( ≥18 years old), five representative algorithms of machine learning multi-label classification: ML-kNN,hMuLab,LIFT,ML-LOC and GLOCAL were used to classify individual cognition,ten fold cross validation was used for training and testing, and commonly used evaluation criteria for multi-label classification were used to test the results.Results The results of each evaluation criterion showed that these five algorithms could be used in the multi-label classification of resting state fMRI, and the hMuLab algorithm had the best classification effect compared with other algorithms.Conclusions We successfully applied the multi-label classification algorithm to multi-label classification of individual cognition in the field of neuroimaging, and found that the sample neighborhood information was important by comparing the five algorithms.

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