The impact of transcranial direct current stimulation on brain network connectivity and topology in post-stroke cognitive impairment patients: a resting-state fMRI study
Bibliographic record
Abstract
Abstract Background Post-stroke cognitive impairment (PSCI) is a common and severe consequence of ischemic stroke (IS) that significantly affects patient outcomes. Transcranial direct current stimulation (tDCS) has shown promise in enhancing cognitive function in IS patients, but its underlying mechanisms are not fully understood. This study investigates the effects of tDCS on brain functional connectivity and network topology using resting-state functional magnetic resonance imaging (rs-fMRI). Methods In this double-blind study, sixty-five IS patients with PSCI were randomly assigned to either the tDCS or control group. Rs-fMRI data were acquired before and after the intervention. We analyzed functional connectivity (FC) and graph theory-based topological properties. Cognitive performance was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Results after treatment, both groups showed improvements in MMSE and MoCA scores, with the tDCS group demonstrating significantly greater improvements (p < 0.05). In the tDCS group, FC significantly increased between four pairs of brain regions (p < 0.05, FDR-corrected). Additionally, Global Efficiency (E g ) significantly improved (p < 0.05, FDR-corrected), and this improvement positively correlated with enhancements in MMSE scores (r = 0.403, p = 0.037). Conclusion These findings suggest that tDCS improves cognitive function in PSCI by altering brain network connectivity and topological organization, providing neuroimaging evidence to support its therapeutic mechanisms.
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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) | 0.001 | 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".