Does Repetitive Transcranial Magnetic Stimulation of Alzheimer’s Patients Improve Cognition or Depression or Both?
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is used clinically to treat major depression and has more recently been applied as a potential treatment for Alzheimer’s disease (AD). Given that the rTMS treatment protocols for AD are similar to those used for depression, we investigated whether the AD participants’ cognition change after rTMS was, in part, due to a change in depressive level. Twenty-eight participants of an rTMS treatment study for AD participated in this study. We collected cognitive measures to partition them into 2 groups of subjects with marked or moderate responses (n = 13) and those with responses of small or none (n = 15). Besides, we recorded pre and post Electrovestibulography (EVestG) signals, and 2 EVestG features targeting depression were calculated from the averaged field potential curve (FP ave ) and low-frequency modulation of the recorded firing pattern (33-interval histogram [IH33]), respectively. We then compared these features in the above-mentioned cognitive-wise response groups. The FP ave and IH33 depression-related features showed no substantial difference between pre- and post-treatment in either group in response to rTMS treatment. The change in these EVestG depression features of the AD participants was also poorly correlated with Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) change with treatment. This study’s results demonstrate that cognitive improvement post rTMS is not predominantly a result of an improvement in depression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".