Evidence map›Paper›PMID 37594145›Full record

ArticleBrain and behavior2023

Preliminary findings on long-term effects of fMRI neurofeedback training on functional networks involved in sustained attention.

Gustavo Santo Pedro Pamplona, Jennifer Heldner, Robert Langner, Yury Koush, Lars Michels, Silvio Ionta, Carlos Ernesto Garrido Salmon, Frank Scharnowski

Open access · goldAbstract read
In one paragraph

Article in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

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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

8 authors at 6 institutions in 5 countries.

Gustavo Santo Pedro PamplonaSensory-Motor Laboratory (SeMoLa), Jules-Gonin Eye Hospital/Fondation Asile des Aveugles, Department of Ophthalmology/University of Lausanne, Lausanne, Switzerland.ORCID 0000-0002-0278-203X
Jennifer HeldnerDepartment of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
Robert LangnerInstitute of Systems Neuroscience, Heinrich Heine University Dusseldorf, Dusseldorf, Germany.
Yury KoushDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, USA.
Lars MichelsDepartment of Neuroradiology, University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0003-3750-1100
Silvio IontaSensory-Motor Laboratory (SeMoLa), Jules-Gonin Eye Hospital/Fondation Asile des Aveugles, Department of Ophthalmology/University of Lausanne, Lausanne, Switzerland.
Carlos Ernesto Garrido SalmonInBrain Lab, Department of Physics, University of Sao Paulo, Ribeirao Preto, Brazil.
Frank ScharnowskiDepartment of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.
Universidade de Ribeirão Preto · BRUniversity of Zurich · CHFondation Asile des Aveugles · CHHeinrich Heine University Düsseldorf · DEUniversity of Vienna · ATYale University · US

Funding

Swiss National Science Foundation 100014_178841Swiss National Science Foundation 32003B_166566Swiss National Science Foundation BSSG10_155915Swiss National Science Foundation PP00P1_170506/1
6 · The paper itself

Abstract

introductionNeurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long-term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long-term learning effects of neurofeedback training remain under-explored.

methodsHere, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs from 15 healthy individuals.

resultsNeurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow-up transfer runs but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up.

conclusionThe discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.

Indexed as

AttentionBrainNerve NetNeurofeedbackAdultBrain MappingDefault Mode NetworkFemaleHumansMagnetic Resonance ImagingMaleYoung AdultfMRI-neurofeedbackfollow-upfunctional connectivitysustained attention

Identifiers

PMID37594145
PMCPMC10570501
OpenAlexW4385969306

What OpenQuestion holds

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LicenceCC BY
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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.