Evidence map›Paper›PMID 42036584›Full record

ReviewAdvances in experimental medicine and biology2026

Neurofeedback in Major Depression.

Hsin-Jung Tsai, Shih-Jen Tsai

Abstract readReview
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In one paragraph

Review in Advances in experimental medicine and biology, 2026. 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

2 authors.

Hsin-Jung TsaiInstitute of Brain Science, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Shih-Jen TsaiInstitute of Brain Science, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Emerging evidence highlights the significant interplay between mental health and brain health, underscoring the potential of non-pharmacological interventions for major depressive disorder. Brain-computer interfaces offer a promising avenue in psychiatry, advancing self-regulation techniques to elucidate relationships among human behavior, emotional processes, and brain functionality. By visualizing brain function and enabling active modulation of cortical activity through real-time feedback, neurofeedback utilizes signals derived from electroencephalography and/or real-time functional magnetic resonance imaging, providing patients with interactive indicators for self-brain training. This closed-loop system targets specific brain activity or regions known to be associated with depression for upregulation or downregulation, thereby enhancing emotion regulation and executive functioning via mechanisms underlying neuroplasticity. Clinical evidence demonstrates promising outcomes, including strengthened neural connectivity, symptom improvement, and increased remission rates in depression. By coupling the advantages of psychotherapy and neuromodulation, neurofeedback aligns with the field's shift toward personalized, technology-driven psychiatry. This chapter also addresses the practical challenges, including protocol standardization, precision targeting, long-term assessment, and scalable delivery, that are essential for translating promising pilot data into routine clinical practice and for empowering patients to actively engage in their brain health toward mental health improvement.

Indexed as

BrainMajor Depressive DisorderNeurofeedbackBrain-Computer InterfacesElectroencephalographyHumansMagnetic Resonance ImagingNeuronal PlasticityBiofeedbackBrain-Computer InterfaceElectroencephalogramNeurofeedbackReal-Time fMRISelf-Brain Regulation

Identifiers

What OpenQuestion holds

Textmetadata
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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.