Combining transcranial brain stimulation and PET/SPECT molecular imaging
Bibliographic record
Abstract
Abstract Transcranial brain stimulation (TMS) was introduced in 1985 by Barker and his colleagues. Since then, further improvements in technology have allowed additional applications and new stimulation protocols. In the last decade, while the use of TMS has expanded enormously in basic science as well as in the clinical scenario, the underlying neurophysiological or neurochemical mechanisms are still not fully understood. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are neuroimaging modalities utilized to investigate brain functions. In spite of their lower spatial and time resolution compared with functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), PET/SPECT have helped to elucidate some of the neurochemical mechanisms and neural plastic changes associated with TMS. In this chapter, we will provide an overview of these techniques, describing methodological details and application of TMS-PET/SPECT imaging in basic and clinical studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".