近日,，中科院心理研究所特聘研究員左西年等人在PLoS One期刊上發(fā)表了他們最新研究結(jié)果"Resting-State Brain Organization Revealed by Functional Covariance Networks"，在這項(xiàng)研究中,，左西年與國內(nèi)合作單位（南京軍區(qū)總醫(yī)院和電子科技大學(xué)）嘗試提出利用腦內(nèi)自發(fā)低頻波動(dòng)活動(dòng)的振幅在個(gè)體間的差異來構(gòu)建不同腦區(qū)的功能關(guān)聯(lián),，提出功能協(xié)方差網(wǎng)絡(luò)方法。
人們的生理和心理個(gè)體差異潛含非常重要的生物進(jìn)化及多樣性信息,。在腦成像研究領(lǐng)域,，以往關(guān)于腦結(jié)構(gòu)（比如：灰質(zhì)體積、密度和皮層厚度）的研究已經(jīng)注意到這種個(gè)體差異對(duì)于揭示人腦結(jié)構(gòu)組織的方式頗具啟發(fā)性,。但是,，認(rèn)知科學(xué)家研究具體認(rèn)知任務(wù)時(shí)大都將個(gè)體差異視為干擾因素而不予重視。新近逐步受到重視,、不依賴于具體任務(wù)設(shè)計(jì)的成像技術(shù)——靜息態(tài)腦成像,，則給予研究人員新的機(jī)會(huì)來考察這種個(gè)體差異在大腦內(nèi)在功能架構(gòu)上的表現(xiàn)。目前,，利用功能磁共振成像時(shí)間序列（秒尺度）和腦結(jié)構(gòu)測(cè)量協(xié)方差（年尺度）的網(wǎng)絡(luò)方法已經(jīng)分別在不同的時(shí)間尺度描繪了人腦功能和結(jié)構(gòu)組織,。但是，研究人員尚未對(duì)介于前兩種尺度之間的人腦的功能網(wǎng)絡(luò)架構(gòu)進(jìn)行刻畫,。
研究人員通過研究默認(rèn)網(wǎng)絡(luò),、注意網(wǎng)絡(luò)和感覺網(wǎng)絡(luò)圖譜，比較其與以前兩種不同時(shí)間尺度網(wǎng)絡(luò)方法的特點(diǎn),。實(shí)驗(yàn)結(jié)果發(fā)現(xiàn),，這三種不同尺度的網(wǎng)絡(luò)有很大程度的空間重疊，并且兩種較短時(shí)間尺度的功能網(wǎng)絡(luò)具有更明顯的模塊化性質(zhì),。最令人感興趣的是,，網(wǎng)絡(luò)分析表明，功能協(xié)方差網(wǎng)絡(luò)是由反相的高階認(rèn)知系統(tǒng)和低階感知系統(tǒng)所組成的“二分”網(wǎng)絡(luò)（如圖）,，這是繼時(shí)間序列相關(guān)方法發(fā)現(xiàn)人腦具有反相的默認(rèn)網(wǎng)絡(luò)和注意網(wǎng)絡(luò)之后的又一新觀測(cè),。
該研究得到了國家自然科學(xué)基金委（30800264,，30971019，81020108022）和金陵醫(yī)院青年基金（Q2008063,，Q2011060）的資助,。（生物谷Bioon.com）
doi:10.1371/journal.pone.0028817
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Resting-State Brain Organization Revealed by Functional Covariance Networks
Zhiqiang Zhang1#, Wei Liao2#, Xi-Nian Zuo3,4#, Zhengge Wang1, Cuiping Yuan1, Qing Jiao1, Huafu Chen2, Bharat B. Biswal5, Guangming Lu1*, Yijun Liu6
Background
Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN) and structural covariance network (SCN) have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization.
Methodology and Principal Findings
We proposed a functional covariance network (FCN) method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network.
Conclusion
The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.
