Effects of rTMS Treatment on Cognitive Impairment and Resting-State Brain Activity in Stroke Patients: A Randomized Clinical Trial
Bibliographic record
Abstract
Background: Repetitive transcranial magnetic stimulation (rTMS) has been employed for motor function rehabilitation for stroke patients, but its effects on post-stroke cognitive impairment (PSCI) remains controversial. Objective: To identify the effects of rTMS intervention on PSCI patients and its potential neural correlates to behavioral improvements. Methods: We recruited 34 PSCI patients for 20 sessions of 10 Hz rTMS or no-stim control treatments over the left dorsal lateral prefrontal cortex (DLPFC). Cognitive function was evaluated with the Montreal Cognitive Assessment Scale, Victoria Stroop Test, Rivermead Behavior Memory Test, and Activities of Daily Living (ADL) assessed with the Modified Barthel Index. 14 patients received functional MRI scan, a useful non-invasive technique of determining how structurally segregated and functionally specialized brain areas were interconnected, which was reflected by blood oxygenation level-dependent signals. The amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) were applied as the analytical approaches, which were used to measure the resting-state brain activity and functional connection. Results: rTMS improved cognitive functions and ADLs for PSCI patients relative to patients who received no-stim control treatment. The cognitive improvements correlated to increased ALFF of the left medial prefrontal cortex, and increased FC of right medial prefrontal cortex and right ventral anterior cingulate cortex. Conclusion: 10 Hz rTMS at DLPFC could improve cognitive function and quality of life for PSCI patients, which is associated with an altered frontal cortical activity. Clinical Registration: Chinese Clinical Trial Registry, ChiCTR-IPR-17011908, http://www.chictr.org.cn/index.aspx.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".