Quantitative Electroencephalographic Biomarkers for Repetitive Transcranial Magnetic Stimulation Treatment Response Prediction in Mild Cognitive Impairment: A Pilot Study Protocol for Multi‐Center, Assessor‐Blinded, Open‐Label Clinical Trial
Bibliographic record
Abstract
RATIONALE: Over 55 million people worldwide suffer from dementia, with 10 million new cases diagnosed annually. Due to the limited efficacy of drug therapies, alternative approaches like repetitive transcranial magnetic stimulation (rTMS) have gained popularity as a non-invasive, safe method leveraging neural plasticity and brain connectivity. However, its high cost and time commitment highlight the need for biomarkers to predict treatment response. AIMS: This pilot study aims to identify a quantitative electroencephalography (QEEG) biomarker to predict which mild cognitive impairment patients will respond to rTMS. By targeting responders early, clinicians can make rTMS more cost-effective and time-efficient, reducing wasted treatment on non-responders. DESIGN: This multi-center, assessor-blinded clinical trial will examine QEEG biomarkers as predictors of rTMS treatment responsiveness in 25 patients with mild cognitive impairment (MCI). Adults aged 60 years or older will undergo cognitive assessments using the Montreal Cognitive Assessment (MoCA) or mini-mental state examination (MMSE) and have an electroencephalography (EEG) recording. Participants will complete 10 rTMS sessions targeting the left DLPFC over 2 weeks, with 2000 pulses per session at 20 Hz. Cognitive tests will be repeated post-treatment, and participants will be classified as responders or non-responders based on cognitive changes, then baseline QEEG parameters will be compared between the two groups. The primary endpoint is the proportion of responders at ten sessions after rTMS (score post-intervention > score pre-intervention = responder, according to the minimal clinically important difference (MCID) threshold (i.e., an increase of at least 3 points or 10% on the MMSE, or an increase of at least 1 point on the MoCA); score post-intervention ≤ score pre-intervention = non-responder). The secondary endpoints are the differences in baseline QEEG features between responders and non-responders. OUTCOME: By identifying responders prior to treatment, we can optimize resource allocation, minimize the time and cost associated with ineffective treatments, and ultimately improve the quality of care for individuals with MCI. TRIAL REGISTRATION: IRCT registration number: IRCT20240218061042N1 (version updated September 7, 2024).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".