Altered Amygdala Volumes and Microstructure in Focal Epilepsy Patients with Tonic-Clonic Seizures, Ictal and Post-Ictal Central Apnea
Bibliographic record
Abstract
Abstract and key words Objectives Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death for patients with epilepsy; however, the pathophysiology remains unclear. Focal-to-bilateral tonic-clonic seizures (FBTCS) are a major risk factor, and centrally-mediated respiratory depression may increase the risk further. Here, we determined volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by presence or absence of FBTCS, ictal central apnea (ICA) and post-ictal central apnea (PICA). Methods 73 patients with only-focal seizures and 30 with FBTCS recorded during video EEG (VEEG) with respiratory monitoring were recruited prospectively during presurgical investigations. We acquired high-resolution T1-weighted anatomical and multi-shell diffusion images, and computed neurite orientation dispersion and density imaging (NODDI) metrics in all epilepsy patients and 69 healthy controls. Amygdala volumetric and microstructure alterations were compared between healthy subjects, and patients with only-focal seizures or FBTCS The FBTCS group was further subdivided by presence of ICA and PICA, verified by VEEG. Results Bilateral amygdala volumes were significantly increased in the FBTCS cohort compared to healthy controls and the focal cohort. Patients with recorded PICA had the highest increase in bilateral amygdala volume of the FBTCS cohort. Amygdala neurite density index (NDI) values were significantly decreased in both the focal and FBTCS groups relative to healthy controls, with values in the FBTCS group being the lowest of the two. The presence of PICA was associated with significantly lower NDI values vs the non-apnea FBTCS group (p=0.004). Significance Individuals with FBTCS and PICA show significantly increased amygdala volumes and disrupted architecture bilaterally, with greater changes on the left side. The structural alterations reflected by NODDI and volume differences may be associated with inappropriate cardiorespiratory patterns mediated by the amygdala, particularly after FBTCS. Determination of amygdala volumetric and architectural changes may assist identification of individuals at risk.
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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) | 0.000 | 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) | 0.002 | 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".