TMS-related spectral perturbation as a single-trial marker of cortical excitability
Bibliographic record
Abstract
Non-invasive source imaging of cortical oscillations on a millisecond timescale is possible through electroencephalographic (EEG, measuring electric potentials) sensors placed on the scalp surface.Source reconstruction of brain activity from EEG data is non-trivial and requires algorithms acting as spatial filters.Rather than reconstructing the brain response to external events separating unrelated brain activity, we are interested in the oscillatory activity of some areas defined by cortical atlas parcels at rest.EEGsynchronized TMS is a powerful tool to investigate the role of brain oscillations in neuronal excitability and plasticity, by enabling specific oscillation phases to be differentially targeted in a brain-state-dependent TMS paradigm.However, the EEG sensor montage is not always effective to predict state-dependent TMS effects.Here we investigate oscillating signals obtained from different personalized spatial filters/cortical parcels and their possible phase effects on motor-evoked potential (MEP) amplitude.We built individual spatial filters based on linearly-constrained minimumvariance (LCMV) beamforming for extracting oscillatory activity from individually defined cortical areas.A participant-specific 3-shell volumeconductor model is segmented from T1w and T2w MR individual images.Then, a cortical source mesh is obtained at the white-matter/gray-matter boundary.The mesh points have been adjusted according to a spherical common template for sulci and gyri.They are then decimated from 160K (Freesurfer standard) to 16K through the HCP workbench, maintaining physiological information and minimum variance in the triangles.This controlled decimation provides for a reasonable number of cortical sources to reconstruct (one dipole for each mesh point) given EEG signals.Normally oriented dipoles are then assigned to 360 cortical parcels from the Glasser Atlas.Measurement session-specific EEG sensor positions are then aligned to the scalp mesh using head fiducials.A session-specific leadfield matrix is computed using the Boundary Element Method (BEM) as implemented in the Helsinki BEM Framework.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".