Efficacy of non‐invasive brain stimulation on global cognition in Alzheimer’s disease and mild cognitive impairment: A meta‐analysis
Bibliographic record
Abstract
Abstract Background Non‐invasive brain stimulation (NIBS) is a non‐pharmacological intervention that has shown some promise in improving cognition in people with Alzheimer’s disease (AD), but clinical trials involving NIBS have shown inconsistent results. This meta‐analysis investigated the efficacy of NIBS, particularly repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS) compared to sham stimulation on global cognition in people with AD and its prodromal stage, mild cognitive impairment (MCI). Method Multi‐session randomized sham‐controlled clinical trials were identified though Medline, PsycInfo, and Embase until November 2020. Standardized mean differences (SMD) and 95% confidence intervals (CI) between the two groups were calculated using random‐effects meta‐analyses. Outcome measures for global cognition included scores on the Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS‐cog). Heterogeneity, from different NIBS techniques, disease populations, or tests used to assess global cognition, was measured using chi‐square and I2, and investigated using subgroup analyses. Result A total of 17 studies (Nactive=233, Nsham=218) were included. NIBS (rTMS [11 studies] + tDCS [6 studies]) significantly improved global cognition in patients with AD and MCI (SMD=1.11; 95% CI=0.42,1.80; p=0.002). Subgroup analyses showed that rTMS (SMD=1.08; CI=0.32,1.84; p=0.005) but not tDCS improved global cognition. Patients with AD [13 studies] (SMD=1.02; 95% CI=0.28,1.76; p=0.007) but not MCI [4 studies] showed significant improvement on global cognition following NIBS as compared to the sham group. Additionally, significant improvement on both the MMSE (SMD=0.67; 95% CI=0.13,1.22, p=0.016) and ADAS‐cog (SMD=1.34; 95% CI=0.30, 2.39; p=0.012) scores were seen in patients with AD following rTMS, when analyzed separately. There was substantial heterogeneity across all analyses (all I2 > 50%) that was not resolved by subgroup analyses. Egger’s test showed no evidence of a publication bias. Conclusion NIBS, particularly rTMS, improved global cognition in those with AD. Further studies with bigger sample sizes in MCI and those using tDCS will help to fully evaluate the specific NIBS techniques and population most likely to benefit on global cognitive measures.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".