Evidence map›Paper›PMID 28872110›Full record

ArticleJournal of visualized experiments : JoVE2017

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training.

Matthew S Sherwood, Emily E Diller, Elizabeth Ey, Subhashini Ganapathy, Jeremy T Nelson, Jason G Parker

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. A Cumulants-Based Human Brain Decoding.Computational intelligence and neuroscience · 2022
    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.

Matthew S SherwoodOffice of the Vice President for Research and Graduate Studies, Wright State University; Department of Biomedical, Industrial and Human Factors Engineering, Wright State University; matt.sherwood@wright.edu.
Emily E DillerDepartment of Biomedical, Industrial and Human Factors Engineering, Wright State University.
Elizabeth EyPediatric Radiology and Medical Imaging, Dayton Children's Hospital.
Subhashini GanapathyDepartment of Biomedical, Industrial and Human Factors Engineering, Wright State University; Department of Trauma Care and Surgery, Boonshoft School of Medicine, Wright State University.
Jeremy T NelsonDepartment of Defense Hearing Center of Excellence, JBSA-Lackland.
Jason G ParkerOffice of the Vice President for Research and Graduate Studies, Wright State University; Department of Neurology, Boonshoft School of Medicine, Wright State University.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

Indexed as

BrainHumansMagnetic Resonance ImagingNeurofeedback

Identifiers

PMID28872110
PMCPMC5614365

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.