Intrinsic Measures of Brain Excitability - State-of-the-Science and Potential Translations
Bibliographic record
Abstract
Cortical excitability has been defined as the reactivity of a cortical area to external stimulation (usually TMS).Commonly excitability is measured via motor-evoked potentials (MEPs) that are elicited due to TMS over the primary motor cortex (M1).There has, however, been a debate on the transferability of this measure to the excitability of other cortical areas, specifically association cortices, since strictly spoken MEPs measure corticospinal excitability and the primary motor cortex has a different functional and structural architecture as compared to other brain areas.The accurate measurement of cortical excitability is not only highly relevant in basic research but is of high importance in neurological conditions that are characterize by malfunctions of E/I circuits including epilepsy, migraine or stroke.Different stimulation-free approaches have been suggested to evaluate the 'intrinsic' excitability of brain areas beyond M1, among these specific modelling of BOLD fMRI and M/EEG data.In this talk an overview about 'intrinsic' excitability measures is given.In our research we explore 'intrinsic' excitability measures by validating them with different means such as acoustic or transcranial magnetic stimulation.Finally, we will give an outlook on how these measures can be translated into clinical practice.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".