Individualized frequency and montage tACS to engage theta-gamma coupling and enhance working memory in mild cognitive impairment
Bibliographic record
Abstract
Mild Cognitive Impairment (MCI) is a clinical prodromal stage of Alzheimer's disease. Enhancing executive functions in patients with MCI could optimize cognitive compensatory mechanisms and slow cognitive decline. The prefrontal cortex (PFC) and its connections to the hippocampus support executive functions, including working memory. Transcranial alternating current stimulation (tACS) can modulate these connections by engaging theta-gamma coupling (TGC) and may thereby strengthen working memory. This study, "tACS to engage theta-gamma coupling and enhance working memory in MCI" (tACS-MCI), will assess the feasibility and cognitive effects of EEG and MRI-guided individualized tACS. The stimulation will target the prefrontal and temporal cortices in 20 MCI participants. Participants will be randomized to receive either individualized tACS or sham tACS for 10 days. tACS individualization will involve adjusting the theta frequency, tACS electrode locations, and current intensity for each participant. Cognitive and functional assessments will occur at baseline and post-intervention. We aim to determine: 1) the feasibility of individualized tACS in MCI, including recruitment and retention; 2) whether tACS engages TGC by assessing its increase in response to tACS; and 3) changes in N-back working memory performance following tACS, as well as whether changes in TGC mediate the changes in performance. The tACS-MCI study will employ an EEG and MRI-guided individualized approach to promote synchronization between frontal and temporal cortices, using participant's unique brain structure and neurophysiology. We aim to assess the feasibility of this novel intervention as a potential approach to more effectively prevent cognitive decline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".