Graph Theory-Guided Transcranial Magnetic Stimulation in Neurodegenerative Disorders
Why this work is in the frame
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Bibliographic record
Abstract
The emergence of brain imaging techniques has shed light on the underlying mechanisms of brain diseases. Abnormalities found in the diseased brain have often been targeted by transcranial magnetic stimulation (TMS), which can modulate long-term neuronal excitability in humans in a noninvasive manner. Thus, its therapeutic application has been extensively explored for neurological and psychiatric disorders. As a result, TMS has been approved for clinical treatment for a few diseases, including drug-refractory depression. However, one of the biggest challenges with TMS is the difficulty in finding the optimal stimulation site. Until now, this process has been heavily dependent on previous activation studies and anatomical knowledge of the region itself, but it largely ignored the whole brain network that interacts with the focal brain region that has been targeted. Here, we propose a novel approach to estimate the prospective network effect following focal interference induced by TMS as a way to optimize the target identification process for TMS research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 it