Evidence map›Paper›PMID 42045603›Full record

ArticleScientific reports2026

Addressing arbitrary choices of frequency band of interest in fNIRS hyperscanning.

Xin Zhou, Florrie F Y Ng, Patrick C M Wong

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Xin ZhouBrain and Mind Institute, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China. xinzhou@cuhk.edu.hk.
Florrie F Y NgBrain and Mind Institute, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.
Patrick C M WongBrain and Mind Institute, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.

Funding

Research Grants Council of Hong Kong C4001-22EF
6 · The paper itself

Abstract

Neuroimaging hyperscanning-the monitoring of brain activity of two or more persons simultaneously-has emerged as a popular tool to uncover the neural mechanisms of social interactions. The use of functional near-infrared spectroscopy (fNIRS)-a non-invasive, child-friendly technique tolerant of motion artifacts-has significantly advanced the research of social interactions. Despite its popularity, the field has yet to agree on best practices for quantifying inter-brain connections (IBC) during social interactions, including the frequency band of interest (FOI) for signal analysis. Various choices of FOIs, along with subject-level physiological differences or experimental design, may have contributed to inconsistent findings across prior studies. In this study, we reviewed various methods used and their corresponding FOI results in previous fNIRS hyperscanning research focused on the topics of cooperation. Additionally, we propose a new methodology to quantify FOI that aims to point to the origin of synchronization between brains. We tested the proposed method on three independent fNIRS hyperscanning datasets. The three datasets involved three different populations and three types of social interactions commonly studied in the literature. We examined the effect of sample sizes and data exclusion rates on the calculation of FOIs and statistical results. We offer a method for testing and adoption within the fNIRS community, aimed at eliminating arbitrary FOI selections and potentially enhancing the reproducibility of results in future fNIRS hyperscanning research.

Indexed as

BrainFunctional NeuroimagingBrain MappingHumansSocial InteractionSpectroscopy, Near-InfraredfNIRS hyperscanningFrequency band of interestInter-brain connection

Identifiers

PMID42045603
PMCPMC13287691

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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.