Evidence map›Paper›PMID 37325778›Full record

ArticleNeurophotonics2023

Gihyoun Lee, Daeun Ro, Seyoung Shin, Yun-Hee Kim

Abstract read
In one paragraph

Article in Neurophotonics, 2023. 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

4 authors.

Gihyoun LeeSungkyunkwan University School of Medicine, Department of Physical and Rehabilitation Medicine, Suwon, Republic of Korea.ORCID https://orcid.org/0000-0002-0969-4884
Daeun RoSungkyunkwan University School of Medicine, Department of Physical and Rehabilitation Medicine, Suwon, Republic of Korea.
Seyoung ShinCHA University School of Medicine, CHA Bundang Medical Center, Department of Rehabilitation Medicine, Seongnam, Republic of Korea.
Yun-Hee KimSungkyunkwan University School of Medicine, Department of Physical and Rehabilitation Medicine, Suwon, Republic of Korea.ORCID https://orcid.org/0000-0001-6101-8851

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: We developed a MATLAB-based toolbox for the analysis of inter-brain synchrony (IBS) and performed an experimental study to confirm its performance. To the best of our knowledge, this is the first toolbox for IBS based on functional near-infrared spectroscopy (fNIRS) hyperscanning data that visually shows the results on two three-dimensional (3D) head models. Aim: Research on IBS using fNIRS hyperscanning is a nascent but expanding field. Although various analysis toolboxes for fNIRS exist, none can show inter-brain neuronal synchrony on a 3D head model. In 2019 and 2020, we released two MATLAB toolboxes named Approach: The developed Results: To evaluate the performance of the developed toolbox, we conducted an fNIRS hyperscanning study of 32 healthy adults. The fNIRS hyperscanning data were measured while the subjects performed traditional, paper-and-pencil-based, cognitive tasks or interactive, computer-assisted, cognitive tasks (ICT). The results visualized different inter-brain synchronization patterns according to the interactive nature of the given tasks; a more extensive inter-brain network was seen with the ICT. Conclusions: The developed toolbox has good performance of IBS analysis and helps even unskilled researchers to easily analyze fNIRS hyperscanning data.

Indexed as

cortical hemodynamic responsefunctional near-infrared spectroscopyinter-brain neuronal networkinter-brain synchronyinterpersonal brain network

Identifiers

PMID37325778
PMCPMC10261701

What OpenQuestion holds

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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.