Advancing post-stroke cognitive rehabilitation through high-frequency neurostimulation: A retrospective evaluation of cortical excitability and biomarker modulation
Bibliographic record
Abstract
BACKGROUND: Post-stroke cognitive impairment (PSCI) poses significant challenges to patient independence, yet technological interventions like high-frequency repetitive transcranial magnetic stimulation (rTMS) remain underexplored in clinical neurorehabilitation. OBJECTIVE: This study evaluates the integration of high-frequency rTMS into standard care, focusing on its technological efficacy in modulating neuroplasticity and serum biomarkers to enhance cognitive and functional recovery. METHODS: A retrospective analysis of 80 PSCI patients (2021-2023) compared outcomes between a conventional care group (n = 30) and an rTMS group (n = 50) receiving 20 Hz stimulation (YRD-CCY-I device) targeting the dorsolateral prefrontal cortex. Key metrics included Montreal Cognitive Assessment (MoCA), Barthel Index (BI), cortical silent period (CL), central motor conduction time (CMCT), and serum neurotrophic factors (BDNF, VEGF, IGF-1). RESULTS: Post-intervention, the rTMS group demonstrated superior MoCA scores (19.25 vs. 15.24, p = 0.001), BI (76.36 vs. 70.13, p = 0.001), and IADL (20.38 vs. 18.13, p = 0.001) compared to controls. Neurophysiological markers revealed prolonged CL (25.30 vs. 24.02 ms, p = 0.001) and shortened CMCT (12.05 vs. 12.98 ms, p = 0.001), alongside elevated BDNF (9.56 vs. 7.34 ng/mL), VEGF (156.48 vs. 110.54 pg/mL), and IGF-1 (153.74 vs. 112.90 ng/mL, p = 0.001). Overall efficacy was 94% for rTMS versus 73.33% for conventional care (p = 0.047). CONCLUSION: High-frequency rTMS, as a targeted neurostimulation technology, enhances cognitive recovery and cortical adaptability in PSCI by modulating neuroplasticity and upregulating neurotrophic biomarkers. These findings underscore its potential as a scalable adjunct in technology-driven neurorehabilitation programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".