Transcranial magnetic vs intracranial electric stimulation: a direct comparison of their effects via scalp EEG recordings
Bibliographic record
Abstract
BACKGROUND: Single-pulse Transcranial Magnetic Stimulation (TMS) and Intracranial Electrical Stimulation (IES) are widely used to probe cortical excitability and connectivity, but their electrophysiological effects have never been compared. OBJECTIVE: This study aims to fill this gap by using high-density scalp electroencephalogram (hd-EEG) as a common read-out to compare human brain responses to TMS and IES. METHODS: The dataset includes TMS-evoked potentials (TEPs) acquired from healthy subjects (n = 22) and IES-evoked potentials (IEPs) recorded from drug-resistant epileptic patients (n = 31) during wakefulness. In a subset of subjects TEPs (n = 12) and IEPs (n = 13) were also recorded during NREM sleep. Amplitude, spectral, and spatiotemporal features of TMS and IES responses, as well as their estimated electrical fields, were compared. RESULTS: We observed marked differences between TMS and IES responses. During wakefulness, IEPs are considerably larger, slower and associated with a suppression of cortical activity, whereas TEPs are characterized by multiple waves of recurrent activation. These differences are attenuated in NREM, during which both TMS and IES elicit large EEG responses associated with a prominent suppression of cortical activity. At the global level, the spatiotemporal complexity of the responses to both TMS and IES decreases consistently following the transition from wakefulness to NREM sleep. CONCLUSION: Despite the limitations due to different subject populations (healthy vs pathological), our findings provide a first reference to parallel non-invasive and invasive brain stimulation and to interpret their differential effects. They also offer important insight on how cortical responsiveness is shaped by inhibition and adaptation mechanisms depending on input parameters and brain states.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".