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Record W3126283921 · doi:10.3390/diagnostics11020227

The Effect of Non-Invasive Brain Stimulation (NIBS) on Attention and Memory Function in Stroke Rehabilitation Patients: A Systematic Review and Meta-Analysis

2021· review· en· W3126283921 on OpenAlexaff
Takatoshi Hara, Aturan Shanmugalingam, Amanda McIntyre, Amer M. Burhan

Bibliographic record

VenueDiagnostics · 2021
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of TorontoWestern UniversityOntario Shores Centre for Mental Health SciencesSt Joseph's Health CareParkwood Institute
Fundersnot available
KeywordsTranscranial direct-current stimulationBrain stimulationMeta-analysisTranscranial magnetic stimulationPhysical medicine and rehabilitationPsycINFOStroke (engine)CognitionNeuropsychologyRehabilitationCINAHLRandomized controlled trialPsychologyMEDLINEPhysical therapyMedicineStimulationNeurosciencePsychiatryInternal medicinePsychological intervention

Abstract

fetched live from OpenAlex

Background: In recent years, the potential of non-invasive brain stimulation (NIBS) for therapeutic effects on cognitive functions has been explored for populations with stroke. There are various NIBS methods depending on the stimulation site and stimulation parameters. However, there is no systematic NIBS review of post-stroke cognitive impairment with a focus on stimulation sites and stimulation parameters. The purpose of this study is to conduct a systematic review and meta-analysis on effectiveness and safety of NIBS for cognitive impairment after a stroke to obtain new insights. This study was prospectively registered with the PROSPERO database of systematic reviews (CRD42020183298). Methods: All English articles from MEDLINE, Scopus, CINAHL, Embase, PsycINFO, and CENTRAL were searched from inception up to 31 December 2020. Randomized and prospective controlled trials were included for the analysis. Studies with at least five individuals post-stroke, whereby at least five sessions of NIBS were provided and using standardized neuropsychological measurement of cognition, were included. We assessed the methodological quality of selected studies as described in the Physiotherapy Evidence Database (PEDro) scoring system. Results: A total of 10 studies met eligibility criteria. Six studies used repetitive transcranial magnetic stimulation (rTMS) and four studies used transcranial direct current stimulation (tDCS). The pooled sample size was 221 and 196 individuals who received rTMS and tDCS respectively. Eight studies combined general rehabilitation, cognitive training, or additional therapy with NIBS. In rTMS studies, target symptoms included global cognition (n = 4), attention (n = 3), memory (n = 4), working memory (WM) (n = 3), and executive function (n = 2). Five studies selected the left dorsolateral prefrontal cortex (DPLFC) as the stimulation target. One rTMS study selected the right DLPFC as the inhibitory stimulation target. Four of six studies showed significant improvement. In tDCS studies, target symptoms included global cognition (n = 2), attention (n = 4), memory (n = 2) and WM (n = 2). Three studies selected the frontal area as the stimulation target. All studies showed significant improvement. In the meta-analysis, rTMS showed a significant effect on attention, memory, WM and global cognition classified by neuropsychological tests. On the other hand, tDCS had no significant effect. Conclusions: In post-stroke patients with deficits in cognitive function, including attention, memory, and WM, NIBS shows promising positive effects. However, this effect is limited, suggesting that further studies are needed with more precision in stimulation sites and stimulation parameters. Future studies using advanced neurophysiological and neuroimaging tools to allow for a network-based approach to treat cognitive symptoms post-stroke with NIBS are warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0220.036
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.037
GPT teacher head0.328
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations69
Published2021
Admission routes1
Has abstractyes

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