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Hum Brain Mapp. 2016 Jan;37(1):381-94. doi: 10.1002/hbm.23037. Epub 2015 Oct 29.

Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means.

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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.
Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Tianfu College, Southwestern University of Finance and Economics, Chengdu, 610052, China.
Center for Brain Disorders and Cognitive Neuroscience, Shenzhen University, Shenzhen, 518060, China.


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.


frequency tagging approach; large scale networks; low frequency oscillations; low frequency steady-state brain responses; triple network system

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