Treatment Response Biomarkers of Accelerated Low Frequency Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder
Bibliographic record
Abstract
AbstractRepetitive transcranial magnetic stimulation (rTMS) is a non-invasive form of brain stimulation for the treatment of major depressive disorder (MDD). One substantial knowledge gap with rTMS is that clinically applicable treatment response biomarkers of rTMS in MDD remain elusive. This thesis contains three studies that are based on the data from two open-labeled clinical trials, and these studies aim to address the need for biomarkers by providing preliminary evidence on the utilization of electrocardiography (ECG) and electroencephalography (EEG) parameters as treatment response biomarkers of accelerated low frequency (LF) right hemisphere (R) dorsolateral prefrontal cortex (DLPFC) rTMS in MDD. The first (n=19) study aims to investigate the effect of accelerated 1Hz R-DLPFC rTMS on heart rate (HR) and heart rate variability (HRV), as well as the association between HR and HRV with treatment outcome. In this first study, HR significantly decreased during the rTMS period. Resting HR, HR during the rTMS period, and the degree of rTMS-induced HR reduction were all significantly negatively associated with treatment outcome prior to Bonferroni correction; Resting HR remained significantly associated with treatment outcome post Bonferroni correction. Furthermore, the second study (n = 24) aims to validate the results of the first study using data from a separate clinical trial. For this second study, HR also significantly decreased during the rTMS period prior to Bonferroni correction. Resting HR, HR during rTMS, and the degree of HR reduction were not significantly associated with treatment outcome; however, the trend of association remained the same as that of the first study. Lastly, the third study aims to investigate the association between baseline TMS evoked potential (TEP) N100 amplitude and 1Hz R-DLPFC arTMS treatment outcome. For this third study, baseline N100 amplitude was significantly associated with treatment outcome. The change in N100 amplitude from baseline to follow-up was significantly associated with treatment outcome prior to Bonferroni correction. In conclusion, the collective result of these three studies is generally in agreement with previous studies and provide additional preliminary evidence for future studies of larger sample sizes to further investigate the biomarker potential of ECG and EEG parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".