The association between cross-frequency coupling and neuroplasticity via paired associative stimulation: TMS-EEG study
Bibliographic record
Abstract
The dependency of TMS-evoked responses on ongoing brain activity makes EEG phase-triggered TMS experiments highly valuable for studying brain function.While EEG offers excellent temporal resolution, it lacks detailed information about deeper brain regions.Combining these experiments with concurrent fMRI offers a more comprehensive understanding but presents challenges when measuring EEG inside an MRI scanner.Our research aims to develop a setup and assess the feasibility of predicting the mu rhythm for EEG phase-triggered TMS.Our setup includes MRI-compatible EEG amplifiers (NeurOne Tesla, Bittium Plc.), a TMS stimulator (MagPro R30, MagVenture Ltd.), and a TMS coil (Mri-B91, MagVenture Ltd.), all arranged within the bore of a 3T Siemens MAGNETOM Skyra MRI scanner.The TMS coil is mounted on a specially designed holder arm and integrated with a custom-made, slightly curved 8-channel MRI surface head coil array.The subject wears a 64-channel EEG cap (Easycap GmbH) equipped with seven custom-made carbon-wire loops (CWLs) for artifact suppression.Raw EEG data are streamed to a realtime processing unit (BOSS device, sync2brain GmgH), which uses modified firmware to suppress MRI-induced artifacts and synchronize TMS with a predefined oscillatory brain state of the EEG.In our preliminary experiments, we delivered single-pulses to the left primary motor cortex (M1) of a right-handed volunteer, monitoring the resulting network activity using interleaved fMRI while simultaneously recording 64-channel EEG at 20 kHz.Our preliminary findings suggest that CWL-based ballistocardiogram artifact removal can facilitate these studies, allowing for the estimation of the phase of the mu rhythm at each TMS onset.Despite challenges, our multimodal TMSeEEGefMRI system shows potential for personalized brain stimulation using EEG-based closed-loop paradigms.Future work will focus on real-time integration and expanding the study to explore how EEG features influence TMS-elicited BOLD responses, potentially leading to new diagnostic and treatment strategies for brain disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".