Evidence map›Paper›PMID 26512872›Full record

ArticleHuman brain mapping2016

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

Yi-Feng Wang, Zhiliang Long, Qian Cui, Feng Liu, Xiu-Juan Jing, Heng Chen, Xiao-Nan Guo, Jin H Yan, Hua-Fu Chen

Abstract read
In one paragraph

Article in Human brain mapping, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yi-Feng WangKey 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.
Zhiliang LongKey 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.
Qian CuiSchool of Political Science and Public Administration, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Feng LiuKey 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.
Xiu-Juan JingTianfu College, Southwestern University of Finance and Economics, Chengdu, 610052, China.
Heng ChenKey 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.
Xiao-Nan GuoKey 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.
Jin H YanCenter for Brain Disorders and Cognitive Neuroscience, Shenzhen University, Shenzhen, 518060, China.
Hua-Fu ChenKey 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Brain MappingAdolescentAttentionBrainExecutive FunctionFemaleFourier AnalysisHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleNeural PathwaysNeuropsychological TestsOrientationOxygenPhotic StimulationOxygenfrequency tagging approachlarge scale networkslow frequency oscillationslow frequency steady-state brain responsestriple network system

Identifiers

PMID26512872
PMCPMC6867441

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.