Considering brain state for individualized functional connectivity-based rTMS
Bibliographic record
Abstract
Recent endeavors to optimize the efficacy of repetitive Transcranial Magnetic Stimulation (rTMS) treatment have focused on locating individualized stimulation targets using functional connectivity derived from functional Magnetic Resonance Imaging (fMRI) scans. Practically, this approach involves three main stages: target discovery, target localization, and treatment. As of now, each stage is typically conducted while participants are "at rest", meaning they are not performing a task or being presented with a stimulus. While growing evidence suggests that the effects of TMS are sensitive to the state of the brain at the time of stimulation, brain state has largely been overlooked during the first two stages (target discovery and localization). Here, we consider the potential importance of brain state at each stage of individualized rTMS, reviewing the relevant (and interdisciplinary) literature, and providing some exploratory example cross-state analyses. We also explore how manipulating and constraining brain state with tasks or movie-watching may provide opportunities to improve the reliability of individualized rTMS targets.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".