Evidence map›Paper›PMID 40331031›Full record

ArticleMethodsX2025

Multimodal mobile brain and body imaging for quantification of dance motor sequence learning.

Russell W Chan, Victoria Lakomski, Johannes V R Pannermayr, Emma Wiechmann, Jan-Willem J R van 't Klooster, Willem B Verwey

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2025. 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

6 authors.

Russell W ChanFaculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, Netherlands.
Victoria LakomskiFaculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, Netherlands.
Johannes V R PannermayrFaculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, Netherlands.
Emma WiechmannFaculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, Netherlands.
Jan-Willem J R van 't KloosterFaculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, Netherlands.
Willem B VerweyFaculty of Behavioural, Management and Social Sciences, Section Cognition, Data and Education, University of Twente, Enschede, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding motor learning in naturalistic settings presents a key challenge in neuroscience. While paradigms like the Discrete Sequence Production (DSP) task have advanced our knowledge, investigating more naturalistic tasks like dance with multi-limbed coordination can help further advance the understanding of complex mechanisms. It can advance motor learning by providing more profound insights into coordination dynamics, movement execution, balance, and decision-making. We have developed a modified DSP methodology that replaces keyboard pressing with dance-stepping, allowing simultaneous electroencephalography (EEG), behavioral, and kinematic recordings to quantify neurophysiological and motor dynamics. Using an E-Prime

Indexed as

3D modellingCentre of massDiscrete sequence performance taskEEGKinematicsMoBIMotor neuroscienceMotor sequence learningMultimodal mobile brain and body imaging for the quantification of dance motor sequence learning

Identifiers

PMID40331031
PMCPMC12053982

What OpenQuestion holds

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LicenceCC BY
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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.