Wang Yi-Feng, Long Zhiliang, Cui Qian, Liu Feng, Jing Xiu-Juan, Chen Heng, Guo Xiao-Nan, Yan Jin H, Chen Hua-Fu
Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, 610054, China.
School of Political Science and Public Administration, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Hum Brain Mapp. 2016 Jan;37(1):381-94. doi: 10.1002/hbm.23037. Epub 2015 Oct 29.
Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.
神经振荡对大脑功能至关重要。研究表明,对于更综合和更远程的通信,神经振荡的频率较低。基于此,一些静息态研究表明,大规模网络在极低频范围(<1 Hz)发挥作用。然而,由于静息态研究和传统频率标记方法都无法在可控的认知活动中同时捕捉多个大规模网络,因此很难确定大脑网络的频率特征。在这项初步研究中,我们旨在检验大规模网络是否可以被任务诱发的低频稳态脑反应(lfSSBRs)以频率特异性模式进行调制。在一项修订后的注意力网络测试中,lfSSBRs在三重网络系统和感觉运动系统中被诱发,这表明大规模网络可以通过频率标记的方式进行调制。此外,网络间和网络内的同步以及相干性在基频和一次谐波处增加,而不是在其他频段,这表明信息通信存在频率特异性调制。然而,注意力条件之间没有差异,这表明lfSSBRs对一般注意力状态的调制比对区分注意力条件的调制要强得多。这项研究为lfSSBRs的优势和机制提供了见解。更重要的是,它为研究频率特异性大规模脑活动开辟了一条新途径。