Evidence map›Paper›PMID 41282693›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Neurofeedback-Enhanced Working Memory Training: A Proof-of-Concept for Reversing Age-Related Neural Slowing in Older Adults.

Ziming Liu, Yang Jiang, Sylvia Cerel-Suhl, Xiaopeng Zhao

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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

4 authors.

Ziming LiuSchool of Computer Science, University of Oklahoma, Norman, OK, USA.
Yang JiangAging Brain and Cognition Laboratory, Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, KY, USA.ORCID 0000-0003-4589-0097
Sylvia Cerel-SuhlVeterans Affairs Lexington Medical Center, Lexington, KY, USA.
Xiaopeng ZhaoDepartment of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA.

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
NIA NIH HHS R56 AG060608RRD VA I21 RX003173
6 · The paper itself

Abstract

Specific electrophysiological (EEG) brain patterns may serve as neural biomarkers of aging, predicting Mild Cognitive Impairment (MCI) in older adults, while other patterns are more closely associated with preserved brain health, such as improved working memory retrieval. In this pilot feasibility study, we developed and tested a novel visual neurofeedback (NF) protocol designed to reward EEG-based brainwave patterns linked to optimal working memory retrieval. We hypothesize that reinforcing these brainwave patterns could enhance cognitive efficiency by promoting increased alpha power, associated with focused attention, while reducing delta power, which is linked to memory lapses. Three older adults completed 8-16 NF training sessions using the protocol. Resting EEG (eyes-closed) was recorded before and after training to explore changes in brainwave patterns. Preliminary findings suggest a potential increase in alpha oscillations and a reduction in delta waves, providing initial support for the feasibility of this NF approach and motivating further evaluation in larger-scale studies.

Indexed as

Continuous brainwave measurementPilot feasibility studyReal-time EEG NeurofeedbackWorking memory retrieval

Identifiers

PMID41282693
PMCPMC12633098

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.