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）嘗試提出利用腦內(nèi)自發(fā)低頻波動活動的振幅在個體間的差異來構建不同腦區(qū)的功能關聯(lián)，提出功能協(xié)方差網(wǎng)絡方法,。
人們的生理和心理個體差異潛含非常重要的生物進化及多樣性信息。在腦成像研究領域，以往關于腦結(jié)構（比如：灰質(zhì)體積,、密度和皮層厚度）的研究已經(jīng)注意到這種個體差異對于揭示人腦結(jié)構組織的方式頗具啟發(fā)性。但是,，認知科學家研究具體認知任務時大都將個體差異視為干擾因素而不予重視,。新近逐步受到重視、不依賴于具體任務設計的成像技術——靜息態(tài)腦成像,，則給予研究人員新的機會來考察這種個體差異在大腦內(nèi)在功能架構上的表現(xiàn),。目前，利用功能磁共振成像時間序列（秒尺度）和腦結(jié)構測量協(xié)方差（年尺度）的網(wǎng)絡方法已經(jīng)分別在不同的時間尺度描繪了人腦功能和結(jié)構組織,。但是,，研究人員尚未對介于前兩種尺度之間的人腦的功能網(wǎng)絡架構進行刻畫。
研究人員通過研究默認網(wǎng)絡,、注意網(wǎng)絡和感覺網(wǎng)絡圖譜,，比較其與以前兩種不同時間尺度網(wǎng)絡方法的特點。實驗結(jié)果發(fā)現(xiàn),，這三種不同尺度的網(wǎng)絡有很大程度的空間重疊,，并且兩種較短時間尺度的功能網(wǎng)絡具有更明顯的模塊化性質(zhì)。最令人感興趣的是,，網(wǎng)絡分析表明,，功能協(xié)方差網(wǎng)絡是由反相的高階認知系統(tǒng)和低階感知系統(tǒng)所組成的“二分”網(wǎng)絡（如圖）,，這是繼時間序列相關方法發(fā)現(xiàn)人腦具有反相的默認網(wǎng)絡和注意網(wǎng)絡之后的又一新觀測。
該研究得到了國家自然科學基金委（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.
