EEG Monitoring during Functional MRI in Animal Models
Bibliographic record
Abstract
Despite its excellent temporal resolution, electroencephalogram (EEG) has poor spatial resolution to study the participation of different brain areas in epileptic discharges, and the propagation of seizures to subcortical areas is not revealed. Furthermore, EEG provides no information about metabolic changes that occur in the brain before and during the epileptic discharges. Thus, monitoring variations in blood flow and oxygenation in response to epileptic discharges can provide additional complementary information. Functional magnetic resonance imaging (fMRI) technology can be used to study the hemodynamic changes associated with interictal epileptiform discharges or epileptic seizures (i.e., before, during or after them) in experimental animal models and may noninvasively monitor these changes over time. Blood oxygenation level-dependent fMRI has superior spatial resolution compared with other functional imaging modalities and utilizes changes in local magnetic field properties to measure the amount of deoxyhemoglobin in each brain areas as an indicator of brain activity. Simultaneous recording of EEG and fMRI is required to achieve this objective. This article describes methods of acquiring and monitoring EEG during fMRI studies in experimental animals. Particular attention will be paid to methods used to eliminate artifacts induced in the acquired magnetic resonance images by EEG equipment and MR-related artifacts in EEG recordings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".