A study to investigate the relationship between transcranial magnetic stimulation on cognitive impairment and neurotrophic factor in post-stroke patients
Bibliographic record
Abstract
Objective This study aimed to investigate the efficacy of transcranial magnetic stimulation on post-stroke patients in terms of cognitive impairment, and to observe its relationship with peripheral blood neurotrophic factor concentration and changes in brain area function. Methods Sixty patients with cognitive impairment after ischemic stroke were randomly assigned to group A (n = 30) and group B (n = 30) to receive TMS and sham stimulation of the left dorsolateral prefrontal cortex, respectively. The frequency of magnetic stimulation intensity in the TMS group was 10 Hz, and 10 stimulations were applied in the left DLPFC. Montreal Cognitive Assessment (MoCA), Digital Breadth Test (DST) and N-Back reaction times as well as determination of peripheral blood BDNF, NGF concentrations were assessed before and 2 weeks after stimulation, respectively, and the functional connectivity of each brain region in the assessment task state was analyzed using near infrared spectroscopy (fNIRS). Finally, a correlation study between peripheral neurotrophic factors and brain regions and in relation to cognitive scales was performed. Results After stimulation, patients in the TMS group had increased MoCA (p = 0.026), DST (p = 0.008) and N-Back (p = 0.007) scores compared to the sham stimulation group, as well as increased peripheral blood BDNF (t = 2.448, p = 0.021) and NGF (t = 2.885, p = 0.007) concentrations. The Pearson’s correlation interaction effect was significant between the patients’ left DLPFC brain region and the right DLPFC brain region (r = 0.492, p = 0.038). BDNF was negatively correlated with the N-Back (r = −0.4668, p = 0.038), NGF was significantly negatively correlated with the N-Back (r = −0.5692, p = 0.0019), and the rDLPFC brain region was positively correlated with the N -Back reaction times was positively correlated (r = −0.6516, p = 0.0243), and LDLPFC brain region was positively correlated with N-Back (r = −0.5012, p = 0.0244). Conclusion TMS improves cognitive function in post-stroke patients, changes in brain-derived neurotrophic factor concentrations under the influence of TMS, and also enhances connectivity in the bilateral DLPFC brain area network. Clinical trial registration https://www.chictr.org.cn/showproj.html?proj=216761 .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".