Evidence map›Paper›PMID 42381831›Full record

ReviewNeuropsychiatric disease and treatment2026

Advances in the Application of Brain-Computer Interface-Based Neurofeedback Training in the Rehabilitation of Patients with Major Depressive Disorder.

Junting Liu, Lingyu Liu, Han Chen, Shu Xuan, Xinke Leng

Abstract readReview
In one paragraph

Review in Neuropsychiatric disease and treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Junting LiuSchool of Physical Education, Hunan University, Changsha, Hunan, People's Republic of China.ORCID 0009-0006-1172-5375
Lingyu LiuSchool of Acupuncture, Rehabilitation and Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People's Republic of China.
Han ChenSchool of Physical Education, Hunan University, Changsha, Hunan, People's Republic of China.
Shu XuanSchool of Physical Education, Hunan University, Changsha, Hunan, People's Republic of China.
Xinke LengSchool of Physical Education, Hunan University, Changsha, Hunan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Major depressive disorder is a highly prevalent affective disorder worldwide, and existing pharmacological and psychological treatments continue to demonstrate notable limitations in terms of therapeutic stability, adverse effects, and relapse prevention. Brain-computer interface-based neurofeedback training (BCI‑NFT) guides patients to actively regulate abnormal neural functional states through real‑time feedback of brain activity signals, thereby providing a precise interventional pathway that acts directly at the level of neural function. This narrative review examines the theoretical foundations, neural mechanisms, and clinical application modalities of BCI‑NFT in depression rehabilitation, encompassing advances in both non‑invasive and invasive neurofeedback technologies, diverse combined intervention paradigms, and the application prospects of a concurrent intervention approach integrating wearable BCI technology with aerobic exercise at the interdisciplinary intersection of sports neuroscience and psychiatric rehabilitation medicine. Preliminary evidence suggests that BCI‑NFT may facilitate neural functional recovery in patients with major depressive disorder through three primary mechanisms: remodeling of emotion‑regulation‑related brain regions, correction of aberrant EEG activity patterns, and improvement of large‑scale brain network connectivity; however, given the sample sizes and methodological heterogeneity of existing studies, these conclusions still require further validation through large-scale randomized controlled trials. Nevertheless, the widespread clinical implementation of BCI‑NFT remains constrained by a lack of standardized training protocols, insufficient clarity regarding suitable patient populations, and a paucity of large‑sample clinical data. Future research should, within a precision psychiatry framework and through the integration of multimodal neuroimaging and large‑scale randomized controlled trials, further advance the standardized clinical translation of BCI‑NFT.

Indexed as

brain–computer interfacemajor depressive disorderneurofeedback trainingneuromodulationneuroplasticitypsychiatric rehabilitationsports neuroscience

Identifiers

PMID42381831
PMCPMC13315852

What OpenQuestion holds

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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.