Evidence map›Paper›PMID 42568667›Full record

ReviewBrain & NeuroRehabilitation2026

Neuroplasticity After Stroke: Clinical Evidence and Its Implication for Effective Rehabilitation Strategies.

Yun-Hee Kim, Seyoung Shin, Jinuk Kim

Abstract readReview
In one paragraph

Review in Brain & NeuroRehabilitation, 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

3 authors.

Yun-Hee KimDepartment of Physical and Rehabilitation Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0001-6101-8851
Seyoung ShinDepartment of Rehabilitation Medicine, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Korea.ORCID https://orcid.org/0000-0002-7834-9494
Jinuk KimDepartment of Physical and Rehabilitation Medicine, Sungkyunkwan University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0003-2941-4904

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke remains a major cause of long-term disability as it often leads to persistent motor deficits, which hinder functional recovery. Neuroplasticity is an important driver of poststroke recovery as it supports synaptic remodeling, network reorganization, and activity-dependent adaptation. This review outlines the neurobiological mechanisms of recovery, including Hebbian and homeostatic plasticity. It also discusses how intensive, task-specific rehabilitation leverages these processes. Technological innovations, such as robotic-assisted rehabilitation and sensor-based feedback, further enable high-dose, repetitive, and personalized training. In addition, adjunctive strategies, including pharmacological modulation, noninvasive brain stimulation, and stem cell therapies, which aim to enhance neuroplasticity, are highlighted. Despite their promise, these interventions show inconsistent efficacy, indicating the need for individualized approaches tailored to lesion characteristics, neurophysiological markers, and recovery stage. Future directions should emphasize the integration of biomarkers, neuroimaging, and precision neurotechnologies, together with multimodal combination therapies. These efforts are essential to establish adaptive rehabilitation protocols that can enhance both the consistency and the magnitude of functional recovery.

Indexed as

NeuroplasticityNeurorehabilitationRecoveryStroke

Identifiers

PMID42568667
PMCPMC13447379

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.