Graph Theory-Guided Transcranial Magnetic Stimulation in Neurodegenerative Disorders
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".