Evidence map›Paper›PMID 28348527›Full record

ReviewFrontiers in aging neuroscience2017

Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback.

Yang Jiang, Reza Abiri, Xiaopeng Zhao

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in aging neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
2.6field-weighted citation impact, top 10% 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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.

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  6. Neurofeedback: Applications, advancements, and future directions.PCN reports : psychiatry and clinical neurosciences · 2025
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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

3 authors at 3 institutions in 1 country.

Yang JiangAging Brain and Cognition Laboratory, Department of Behavioral Science, College of Medicine, University of KentuckyLexington, KY, USA; Sanders-Brown Center on Aging, College of Medicine, University of KentuckyLexington, KY, USA.
Reza AbiriDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee Knoxville, TN, USA.
Xiaopeng ZhaoDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of TennesseeKnoxville, TN, USA; Institute for Medical Engineering and Science, Massachusetts Institute of TechnologyCambridge, MA, USA.
Massachusetts Institute of Technology · USUniversity of Kentucky · USUniversity of Tennessee at Knoxville · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.

Indexed as

BCIbiofeedbackbrain modulationcognitive agingEEGERPSVM

Identifiers

PMID28348527
PMCPMC5346575
OpenAlexW2591969807

What OpenQuestion holds

Textmetadata
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.