Neuromodulation in the Treatment of Alzheimer’s Disease: Current and Emerging Approaches
Bibliographic record
Abstract
Neuromodulation as a treatment strategy for psychiatric and neurological diseases has grown in popularity in recent years, with the approval of repetitive transcranial magnetic stimulation (rTMS) for the treatment of depression being one such example. These approaches offer new hope in the treatment of diseases that have proven largely intractable to traditional pharmacological approaches. For this reason, neuromodulation is increasingly being explored for the treatment of Alzheimer's disease. However, such approaches have variable, and, in many cases, very limited evidence for safety and efficacy, with most human evidence obtained in small clinical trials. Here we review work in animal models and humans with Alzheimer's disease exploring emerging neuromodulation modalities. Approaches reviewed include deep brain stimulation, transcranial magnetic stimulation, transcranial electrical stimulation, ultrasound stimulation, photobiomodulation, and visual or auditory stimulation. In doing so, we clarify the current evidence for these approaches in treating Alzheimer's disease and identify specific areas where additional work is needed to facilitate their clinical translation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".