Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters
Bibliographic record
Abstract
Abstract Background Cortico-cortical evoked potentials (CCEPs) recorded by stereo-electroencephalography (SEEG) are a valuable clinical tool to investigate brain reactivity and effective connectivity. However, these invasive recordings are spatially sparse since they depend on clinical needs. This sparsity hampers systematic comparisons across-subjects, the detection of the whole-brain spatiotemporal properties of CCEPs, as well as their relationships with classic sensory evoked potentials. Objective To demonstrate that CCEPs recorded by high-density electroencephalography (hd-EEG) are sensitive to changes in stimulation parameters and compensate for the limitations typical of invasive recordings. Methods SEEG and hd-EEG activities were simultaneously recorded during SPES in drug-resistant epileptic patients (N=36). Changes in stimulation parameters encompassed physical (pulse intensity and width), geometrical (angle and position with respect to white/grey matter) and topological (stimulated cortical area) properties. Differences were assessed by measuring the overall responses and the amplitude of N1 and N2 components of the CCEPs, and by their spectral profiles. Results While invasive and non-invasive CCEPs were generally correlated, differences in pulse duration, angle and stimulated cortical area were better captured by hd-EEG. Further, hd-EEG responses to SPES reproduced basic features of responses to transcranial magnetic stimulation and showed a much larger amplitude as compared to typical sensory evoked potentials. Conclusions The present results show that macroscale hd-EEG recordings are exquisitely sensitive to variations in SPES parameters, including local changes in physical and geometrical stimulus properties, while providing valuable information about whole-brain dynamics. Moreover, the common reference space across subjects represented by hd-EEG may facilitate the construction of a perturbational atlas of effective connectivity. Highlights CCEPs recorded with hd-EEG and SEEG are correlated. hd-EEG recording is highly sensitive to changes in stimulation parameters. hd-EEG responses show higher amplitude responses with respect to non-invasive ones. Simultaneous recordings provide a fixed observation point across subjects.
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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 | 0.000 | 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) | 0.001 | 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 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".