Controllable Pulse Parameter TMS and TMS-EEG As Novel Approaches to Improve Neural Targeting with rTMS in Human Cerebral Cortex
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) can produce after-effects on the excitability and function of the stimulated cortical site that outlasts the period of stimulation for several minutes or hours (Hamada et al., 2008; Huang et al., 2005; Ridding and Ziemann, 2010; Sommer et al., 2013). These are thought to involve early phases of long term potentiation/depression at cortical synapses. Depending on the area stimulated, the after-effects can influence performance of a variety of cognitive and motor tasks, as well as learning (Parkin et al., 2015; Censor and Cohen, 2011). Reports of beneficial effects on behaviour in healthy populations have led to widespread interest in applying rTMS therapeutically, for example in patients with neuropsychiatric and neurological disorders (George et al., 2013; Lefaucheur et al., 2014; Ridding and Rothwell, 2007). \n \nA major issue with rTMS protocols is that the effects vary considerably within and between individuals (Hamada et al., 2013; Lopez-Alonso et al., 2014; Simeoni et al., 2016; Hinder et al., 2014; Vallence et al., 2015; Vernet et al., 2013; Goldsworthy et al., 2014; Maeda et al., 2000), which causes problems in replication of results in a research setting (Heroux et al., 2015), and is an obstacle to using rTMS in a therapeutic setting. A separate, but related, issue is that rTMS over a given cortical area is often assumed to affect all neuronal populations equally and thus affect all behaviours involving that area similarly, but this may not be true. Here we argue that advanced technologies and methodologies, such as controllable pulse parameter TMS (cTMS; (Peterchev et al., 2014)) and combining TMS with electroencephalography (EEG) (Ilmoniemi and Kicic, 2010; Peterchev et al., 2014), might facilitate the development of more selective forms of stimulation targeting particular neuronal populations or brain states, and ultimately improve the reliability and behavioural specificity of rTMS protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".