Modelling the Effect of Allopregnanolone on the Resolution of Spike-Wave Discharges
Bibliographic record
Abstract
Abstract Objective Childhood absence epilepsy (CAE) is a paediatric generalized epilepsy disorder with a confounding feature of resolving in adolescence in a majority of cases. In this study, we modelled how the small-scale (synapse-level) effect of progesterone metabolite allopregnanolone induces a large-scale (network-level) effect on a thalamocortical circuit associated with this disorder. In particular, our goal was to understand the role of sex steroid hormones in the spontaneous remission of CAE. Methods The conductance-based computational model consisted of single-compartment cortical pyramidal, cortical interneurons, thalamic reticular and thalamocortical relay neurons, each described by a set of ordinary differential equations. Excitatory and inhibitory synapses were mediated by AMPA, GABAa and GABAb receptors. The model was implemented using the NetPyne modelling tool and the NEURON simulator. Results The action of allopregnanolone on individual GABAa-receptor mediated synapses has an ameliorating effect on spike-wave discharges (SWDs) associated with absence seizures. This effect is region-specific and most significant in the thalamus, particularly the synapses between thalamic reticular neurons. Significance The remedying effect of allopregnanolone on SWDs may possibly be true only for individuals that are predisposed to remission due to intrinsic connectivity differences or differences in tonic inhibition. These results are a useful first-step and prescribe directions for further investigation into the role of ALLO together with these differences to distinguish between models for CAE-remitting and non-remitting individuals.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".