Evidence map›Paper›PMID 35060063›Full record

ArticleNeuroscience bulletin2022

Frequency-Resolved Connectome Hubs and Their Test-Retest Reliability in the Resting Human Brain.

Lei Wang, Xiaodan Chen, Yuehua Xu, Miao Cao, Xuhong Liao, Yong He

Open access · greenAbstract read
In one paragraph

Article in Neuroscience bulletin, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 47% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
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

6 authors at 3 institutions in 1 country.

Lei WangState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Xiaodan ChenState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. xiaodanchen17@gmail.com.
Yuehua XuState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Miao CaoInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
Xuhong LiaoSchool of Systems Science, Beijing Normal University, Beijing, 100875, China. liaoxuhong@bnu.edu.cn.
Yong HeState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. yong.he@bnu.edu.cn.
Beijing Normal University · CNChinese Institute for Brain Research · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional hubs with disproportionately extensive connectivities play a crucial role in global information integration in human brain networks. However, most resting-state functional magnetic resonance imaging (R-fMRI) studies have identified functional hubs by examining spontaneous fluctuations of the blood oxygen level-dependent signal within a typical low-frequency band (e.g., 0.01-0.08 Hz or 0.01-0.1 Hz). Little is known about how the spatial distributions of functional hubs depend on frequency bands of interest. Here, we used repeatedly measured R-fMRI data from 53 healthy young adults and a degree centrality analysis to identify voxelwise frequency-resolved functional hubs and further examined their test-retest reliability across two sessions. We showed that a wide-range frequency band (0.01-0.24 Hz) accessible with a typical sampling rate (f

Indexed as

ConnectomeBrainHumansMagnetic Resonance ImagingReproducibility of ResultsRestYoung AdultConnectomeDegree centralityFrequencyHubsTest-retest reliability

Identifiers

PMID35060063
PMCPMC9106786
OpenAlexW4206602112

What Socratic holds

Textmetadata
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 Socratic graph.