A Narrative Review on the Current Landscape of Invasive Neuromodulation for Poststroke Motor Recovery: Mechanisms, Challenges, and Future Directions
Bibliographic record
Abstract
OBJECTIVES: Stroke is a leading cause of disability. Despite rehabilitation efforts, most survivors of stroke do not fully recover. Invasive neuromodulation has shown promise but has not yet become standard of care in poststroke rehabilitation. Given the inherent drawbacks of invasive modalities, a critical evaluation is warranted. This review examines invasive neuromodulation strategies for poststroke recovery, focusing on their mechanisms of action, clinical evidence, and technical challenges. MATERIALS AND METHODS: A structured search was conducted using PubMed for studies from 2000 to 2025, with the terms ("Stroke"[MeSH] OR "Stroke Rehabilitation"[MeSH]) and ("Neurostimulation" OR "Invasive Electrical Stimulation" OR "Deep Brain Stimulation" OR "Epidural Stimulation" OR "Spinal Cord Stimulation" OR "Cortical Stimulation" OR "Cerebellar Stimulation"). Only human studies were included. Moreover, clinical trials from ClinicalTrials.gov and the European Union Clinical Trials Register were cross-referenced, and preclinical studies underpinning selected clinical trials were integrated. RESULTS: Vagal nerve stimulation has received Food and Drug Administration approval, whereas motor cortex stimulation, cerebellar stimulation, and spinal cord stimulation remain investigational. These methods aim to recruit residual motor networks and promote plasticity. However, narrow cohorts, variability in stroke location and timing, differences in rehabilitation intensity, and inconsistencies in outcome measures present significant challenges to achieving consistent and broadly applicable therapeutic outcomes across trials. CONCLUSIONS: Given the risks associated with invasive techniques, a deeper understanding of their mechanisms is essential to maximizing their therapeutic potential. Nevertheless, advances in electrode technology, adaptive stimulation, and multimodal approaches hold promise for optimizing the effectiveness of invasive neuromodulation and improving patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".