Evidence map›Paper›PMID 41081225›Full record

ArticleQuantitative imaging in medicine and surgery2025

Brain-computer interface training for multimodal functional recovery in patients with brain injury: a case series.

Peili Cao, Shijie Guo, Gangli Zhang, Xiang Zan, Junhao Wang, Fulong Zhang, Javier Muñoz, Brandon Lucke-Wold, Rui Cheng

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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.

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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Brain-computer interface: an update for the clinicians.Frontiers in human neuroscience · 2026
    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

9 authors.

Peili Cao *The Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China.
Shijie Guo *Department of Neurosurgery, Jinan University Affiliated First Hospital, Guangzhou, China.
Gangli ZhangThe Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China.
Xiang ZanThe Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China.
Junhao WangThe Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China.
Fulong ZhangThe Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China.
Javier MuñozICU, Hospital General Universitario Gregorio Marañón, Madrid, Spain.
Brandon Lucke-WoldDepartment of Neurosurgery, University of Florida, Gainesville, FL, USA.
Rui ChengThe Neurosurgery Department of Shanxi Provincial People's Hospital, Shanxi Medical University, Taiyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients with impaired brain function often face sequelae such as limb movement, cognitive, and language impairment, and there are limitations in the efficiency of traditional rehabilitation methods. This study examined whether motor imagery-based brain-computer interface (BCI) training could promote multimodal functional recovery-including limb movement, speech, and cognition-in patients with subacute brain injury. Unlike traditional BCI research focused on single functional domains, we combined multidimensional clinical assessments with multimodal neural analysis to examine cross-network plasticity. Methods: Five patients with subacute brain injury (four males and one female; mean age 54.4±10.3 years) underwent 5 weeks of BCI training between 2021 and 2023. Pre- and post-intervention evaluations included the Fugl-Meyer Assessment Scale (FMA), Modified Ashworth Scale (MAS), Western Aphasia Battery (WAB), and Mini-Mental State Examination (MMSE). Neurophysiological metrics included classification accuracy (CA), power spectral density (PSD), and electroencephalography (EEG) topography. Functional connectivity analyses were conducted with functional magnetic resonance imaging (fMRI) and individualized connectomics based on the Human Connectome Project parcellation. Results: All five patients showed clinical improvement in motor, cognitive, or language functions. The average motor imagery CA increased by 14.2%. PSD flattening and event-related desynchronization (ERD) were observed in the central motor regions. EEG topographies showed enhanced activation converging toward the sensorimotor cortex. Patient-specific functional connectivity analyses revealed strengthened interactions among sensorimotor, language, and attention networks-most notably in one patient with marked clinical gains. Distinct patterns of connectivity reorganization were observed between patients with cortical and subcortical lesions. A critical 3-week time window for neural plasticity was identified. Conclusions: Motor imagery-based BCI training may facilitate recovery across motor, language, and cognitive domains in patients with subacute brain injury. Functional gains were supported by neurophysiological and connectomics evidence of cross-network reorganization. These preliminary findings suggest that personalized BCI protocols could represent a promising avenue for multimodal neurorehabilitation.

Indexed as

Brain-computer interface (BCI)case seriesconnectomicsneural plasticityrehabilitation

Identifiers

PMID41081225
PMCPMC12514666

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LicenceCC BY-NC-ND
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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.