A Systematic Review of effect of Non-Invasive Brain Stimulation on Cognition Impairment after a Stroke and Traumatic Brain Injury
Bibliographic record
Abstract
ABSTRACT Background In recent years, the potential of non-invasive brain stimulation (NIBS) for therapeutic effects on cognitive functions has been explored for stroke and traumatic brain injury (TBI) populations. Methods All English articles from the following sources were searched from inception up to December 31, 2018: PubMed, Scopus, CINAHL, Embase, PsycINFO and CENTRAL. Randomized and prospective controlled trials, including cross-over studies, were included for analysis. Studies with at least five individuals post stroke or TBI, whereby at least five sessions of NIBS were provided and used standardized neuropsychological measurement of cognition, were included. Results A total of 17 studies met eligibility criteria which included 546 patients receiving either repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS). Sample sizes ranged 5-25 subjects per group. Seven studies used rTMS and ten studies used tDCS. Target symptoms included global cognition (n=8), memory (n=1), attention (n=1), and unilateral spatial neglect (USN) (n=7). Nine studies combined rehabilitation or additional therapy with NIBS. Six of ten studies showed significant improvement in attention, memory, working memory, and executive function. In the USN study, five of the seven studies had a significant improvement in the intervention group. Conclusions The effect of NIBS on executive functions including attention and memory post stroke or TBI yielded mixed results with variable stimulation parameters. A significant, consistent improvement was observed for USN post stroke or TBI. Future studies using advanced neurophysiological and neuroimaging tools to allow network-based approach to NIBS for cognitive symptoms post stroke or TBI are warranted.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".