Neurofeedback as a form of cognitive rehabilitation therapy following stroke: A systematic review
Bibliographic record
Abstract
Neurofeedback therapy (NFT) has been used within a number of populations however it has not been applied or thoroughly examined as a form of cognitive rehabilitation within a stroke population. Objectives for this systematic review included: i) identifying how NFT is utilized to treat cognitive deficits following stroke, ii) examining the strength and quality of evidence to support the use of NFT as a form of cognitive rehabilitation therapy (CRT) and iii) providing recommendations for future investigations. Searches were conducted using OVID (Medline, Health Star, Embase + Embase Classic) and PubMed databases. Additional searches were completed using the Cochrane Reviews library database, Google Scholar, the University of Toronto online library catalogue, ClinicalTrials.gov website and select journals. Searches were completed Feb/March 2015 and updated in June/July/Aug 2015. Eight studies were eligible for inclusion in this review. Studies were eligible for inclusion if they: i) were specific to a stroke population, ii) delivered CRT via a NFT protocol, iii) included participants who were affected by a cognitive deficit(s) following stroke (i.e. memory loss, loss of executive function, speech impairment etc.). NFT protocols were highly specific and varied within each study. The majority of studies identified improvements in participant cognitive deficits following the initiation of therapy. Reviewers assessed study quality using the Downs and Black Checklist for Measuring Study Quality tool; limited study quality and strength of evidence restricted generalizability of conclusions regarding the use of this therapy to the greater stroke population. Progression in this field requires further inquiry to strengthen methodology quality and study design. Future investigations should aim to standardize NFT protocols in an effort to understand the dose-response relationship between NFT and improvements in functional outcome. Future investigations should also place a large emphasis on long-term participant follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".