Probing hippocampal stimulation in experimental temporal lobe epilepsy with functional MRI
Bibliographic record
Abstract
Abstract Background Electrical neurostimulation is a potentially effective therapy in epilepsy but the optimal approach is not yet clear. The parameter space is wide and the effects of different stimulations are not immediately obvious. Functional MRI (fMRI) can reveal which brain areas are affected by stimulation and help understand the induced effects. However, simultaneous deep brain stimulation (DBS)-fMRI examinations in patients are rare and the possibility to investigate multiple stimulation protocols is limited. Preclinical stimulation-fMRI studies can provide predictive value and help identify optimal neurostimulation parameters. Objective To systematically investigate the brain-wide responses to hippocampal electrical stimulations in a mouse model of mesial temporal lobe epilepsy (mTLE) using fMRI. Methods We applied electrical stimulation in the intrahippocampal kainate mouse model of mTLE and assessed the effect of different stimulation amplitudes (80-230 µA) and frequencies (1-100 Hz). In addition, the effect of prolonged 1 Hz stimulation was explored. Saline-injected mice served as controls. Results Varying the stimulation amplitudes had little effect on the resulting activation patterns. Low frequency stimulation led to a local response at the stimulation site only, whereas high frequency resulted in a spread of activation from the hippocampal formation into cortical and frontal areas. Prolonged low frequency stimulation reduced excitability. Conclusions While the amplitude parameter offers little opportunity to vary the outcome, the frequency represented the key parameter and determined whether the induced activation remained local or spread across the brain. This is in line with the few DBS-fMRI results obtained in epilepsy patients demonstrating the translational value of fMRI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".