Evidence map›Paper›PMID 35418848›Full record

ReviewFrontiers in aging neuroscience2022

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Yang Jiang, William Jessee, Stevie Hoyng, Soheil Borhani, Ziming Liu, Xiaopeng Zhao, Lacey K Price, Walter High, Jeremiah Suhl, Sylvia Cerel-Suhl

Registry-linked trialAbstract readReview
In one paragraph

Review in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04446481 (Tuning Up Memory-related Brain Potentials Using Real-time Neurofeedback in Older Veterans), which is not on this map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–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.

NCT04446481 nacompletednot on this map

Tuning Up Memory-related Brain Potentials Using Real-time Neurofeedback in Older Veterans

TypeinterventionalSponsorVA Office of Research and DevelopmentRan2022 to 2025Enrolled4ConditionsMild Cognitive ImpairmentArmsNeurofeedback
3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Yang JiangLexington Veteran Affairs Medical Center, Lexington, KY, United States.
William JesseeCollege of Medicine, University of Kentucky, Lexington, KY, United States.
Stevie HoyngCollege of Medicine, University of Kentucky, Lexington, KY, United States.
Soheil BorhaniDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States.
Ziming LiuDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States.
Xiaopeng ZhaoDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States.
Lacey K PriceLexington Veteran Affairs Medical Center, Lexington, KY, United States.
Walter HighNew Mexico Veteran Affairs Medical Center, Albuquerque, NM, United States.
Jeremiah SuhlLexington Veteran Affairs Medical Center, Lexington, KY, United States.
Sylvia Cerel-SuhlLexington Veteran Affairs Medical Center, Lexington, KY, United States.

Funding

Predictive Neurophysiological Biomarkers for Risk of Alzheimer’s Disease (AD) and Related Dementia (ADRD) Induced Cognitive Impairment in Sino-US CohortsR56AG060608 · NIA · UNIVERSITY OF KENTUCKY · PI JIANG, YANG · 2018 to 2018
$440k
Tuning Up Memory-related Brain Potentials using Real-time Neurofeedback in Older Veterans"I21RX003173 · VA · VA MEDICAL CENTER - LEXINGTON, KY · PI JIANG, YANG · 2020 to 2023
–
NIA NIH HHS R56 AG060608RRD VA I21 RX003173
6 · The paper itself

Abstract

Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of

Indexed as

BCI illiteracybiofeedbackbrain computer interface (BCI)closed-loop feedbackEEG-ERPsworking memory

Identifiers

PMID35418848
PMCPMC8995767

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.