Temporo-Frontal Coherences and High-Frequency iEEG Responses during Spatial Navigation in Patients with Drug-Resistant Epilepsy
Bibliographic record
Abstract
The prefrontal cortex and hippocampus function in tight coordination during multiple cognitive processes. During spatial navigation, prefrontal neurons are linked to hippocampal theta oscillations, presumably in order to enhance communication. Hippocampal ripples have been suggested to reflect spatial memory processes. Whether prefrontal-hippocampal-interaction also takes place within the ripple band is unknown. This intracranial EEG study aimed to investigate whether ripple band coherences can also be used to show this communication. Twelve patients with epilepsy and intracranial EEG evaluation completed a virtual spatial navigation task. We calculated ordinary coherence between prefrontal and temporal electrodes during retrieval, re-encoding, and pre-task rest. Coherences were compared between the conditions via permutation testing. Additionally, ripples events were automatically detected and changes in occurrence rates were investigated excluding ripples on epileptic spikes. Ripple-band coherences yielded no general effect of the task on coherences across all patients. Furthermore, we did not find significant effects of task conditions on ripple rates. Subsequent analyses pointed to rather short periods of synchrony as opposed to general task-related changes in ripple-band coherence. Specifically designed tasks and adopted measures might be necessary in order to map these interactions in future studies.
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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".