Evidence map›Paper›PMID 38949487›Full record

ArticleHuman brain mapping2024

Measuring functional connectivity in frequency-domain helps to better characterize brain function.

Limin Peng, Jianpo Su, Dewen Hu, Yang Yu, Huilin Wei, Ming Li

Abstract read
In one paragraph

Article in Human brain mapping, 2024. 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
–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

1 citing paper in PubMed.

  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.

Limin PengCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.ORCID 0000-0003-4273-0717
Jianpo SuCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.
Dewen HuCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.ORCID 0000-0001-7357-0053
Yang YuCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.
Huilin WeiSystems Engineering Institute, Academy of Military Sciences, Beijing, China.
Ming LiCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.

Funding

National Natural Science Foundation of China 62036013National Natural Science Foundation of China 62076248National Natural Science Foundation of China 62103437National Natural Science Foundation of China 62306327
6 · The paper itself

Abstract

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Indexed as

ConnectomeMagnetic Resonance ImagingAdultBrainFemaleHumansMachine LearningMaleNerve NetYoung Adultcoherencefunctional connectivityfunctional parcellationmultivariate pattern analysisresting‐state fMRI

Identifiers

PMID38949487
PMCPMC11215841

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.