Evidence map›Paper›PMID 30356825›Full record

ArticleFrontiers in aging neuroscience2018

Increased Functional Brain Network Efficiency During Audiovisual Temporal Asynchrony Integration Task in Aging.

Bin Wang, Peizhen Li, Dandan Li, Yan Niu, Ting Yan, Ting Li, Rui Cao, Pengfei Yan, Yuxiang Guo, Weiping Yang and 7 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
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

17 citing papers in PubMed, 33 citations in OpenAlex.

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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

17 authors at 5 institutions in 2 countries.

Bin WangCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Peizhen LiCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Dandan LiCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Yan NiuCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Ting YanTranslational Medicine Research Center, Shanxi Medical University, Taiyuan, China.
Ting LiCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Rui CaoCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Pengfei YanCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Yuxiang GuoCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Weiping YangDepartment of Psychology, Faculty of Education, Hubei University, Wuhan, China.
Yanna RenMedical Humanities College, Guiyang University of Traditional Chinese Medicine, Guiyang, China.
Xinrui LiSuzhou North America High School, Suzhou, China.
Fusheng WangHefei No. 8 Senior High School, Hefei, China.
Tianyi YanSchool of Life Science, Beijing Institute of Technology, Beijing, China.
Jinglong WuKey Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing, China.
Hui ZhangDepartment of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China.
Jie XiangCollege of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
Taiyuan University of Technology · CNBeijing Institute of Technology · CNGuiyang College of Traditional Chinese Medicine · CNShanxi Medical University · CNHubei University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Audiovisual integration significantly changes over the lifespan, but age-related functional connectivity in audiovisual temporal asynchrony integration tasks remains underexplored. In the present study, electroencephalograms (EEGs) of 27 young adults (22-25 years) and 25 old adults (61-76 years) were recorded during an audiovisual temporal asynchrony integration task with seven conditions [auditory (A), visual (V), AV, A50V, A100V, V50A and V100A]. We calculated the phase lag index (PLI)-weighted connectivity networks modulated by the audiovisual tasks and found that the PLI connections showed obvious dynamic changes after stimulus onset. In the theta (4-7 Hz) and alpha (8-13 Hz) bands, the AV and V50A conditions induced stronger functional connections and higher global and local efficiencies, reflecting a stronger audiovisual integration effect, which was attributed to the auditory information arriving at the primary auditory cortex earlier than the visual information reaching the primary visual cortex. Importantly, the functional connectivity and network efficiencies of old adults revealed higher global and local efficiencies and higher degree in both the theta and alpha bands. These larger network efficiencies indicated that old adults might experience more difficulties in attention and cognitive control during the audiovisual integration task with temporal asynchrony than young adults. There were significant associations between network efficiencies and peak time of integration only in young adults. We propose that an audiovisual task with multiple conditions might arouse the appropriate attention in young adults but would lead to a ceiling effect in old adults. Our findings provide new insights into the network topography of old adults during audiovisual integration and highlight higher functional connectivity and network efficiencies due to greater cognitive demand.

Indexed as

agingaudiovisual integrationEEGfunctional connectivitytheta and alpha bands

Identifiers

PMID30356825
PMCPMC6189604
OpenAlexW2895831613

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.