Transcranial direct current stimulation alleviates cognitive impairment and neurological dysfunction after stroke: a functional near-infrared spectroscopy study
Bibliographic record
Abstract
Cognitive impairment is common in stroke patients. Transcranial direct current stimulation (tDCS) is a promising tool for alleviating cognitive impairment and altering cerebral cortex excitability. In this study, we aimed to evaluate whether tDCS improves cognitive impairment after stroke by altering cortical activation. We recruited 30 post-stroke patients and 30 healthy individuals. We placed the anodes on the F3 and Fp2 electrodes with an intensity of 2 mA to observe changes in cortical activation during the N-back task in patients with cognitive impairment following stroke. Changes in cortical activation were observed with functional near-infrared spectroscopy during the N-back task in patients with cognitive impairment following stroke. Cognitive function was impaired after stroke; cortical activation in the left ventrolateral prefrontal cortex (VLPFC) during the N-back task decreased after stroke. Cognitive function and cortical activation in the left VLPFC during the N-back task increased after tDCSs in post-stroke patients, and reaction time changes during the N-back task were significantly correlated with changes in cortical activation in the left VLPFC and Montreal Cognitive Assessment after tDCSs. Cognitive impairment is common after a stroke, and deactivation of the left VLPFC can be used as a neural marker of cognitive impairment. tDCS is an effective technology that can improve cognitive function and cortical activation in patients with post-stroke cognitive impairment.
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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 | 0.000 |
| 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) | 0.001 | 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".