Altered amygdala volumes and microstructure in focal epilepsy patients with <scp>tonic–clonic</scp> seizures, ictal, and post‐convulsive central apnea
Bibliographic record
Abstract
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 the volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by the presence or absence of FBTCS, ictal central apnea (ICA), and post-convulsive central apnea (PCCA). METHODS: Seventy-three patients with focal impaired awareness seizures without FBTC seizures (FBTCneg group) and 30 with FBTCS (FBTCpos group) recorded during video electroencephalography (VEEG) with respiratory monitoring were recruited prospectively during presurgical investigations. We acquired high-resolution T1-weighted anatomic and multi-shell diffusion images, and computed neurite orientation dispersion and density imaging (NODDI) metrics in all patients with epilepsy and 69 healthy controls. Amygdala volumetric and microstructure alterations were compared between three groups: healthy subjects, FBTCneg and FBTCpos groups. The FBTCpos group was further subdivided by the presence of ICA and PCCA, verified by VEEG. RESULTS: Bilateral amygdala volumes were significantly increased in the FBTCpos cohort compared to healthy controls and the FBTCneg group. Patients with recorded PCCA had the highest increase in bilateral amygdala volume of the FBTCpos cohort. Amygdala neurite density index (NDI) values were decreased significantly in both the FBTCneg and FBTCpos groups relative to healthy controls, with values in the FBTCpos group being the lowest of the two. The presence of PCCA was associated with significantly lower NDI values vs the non-apnea FBTCpos group (p = 0.004). SIGNIFICANCE: Individuals with FBTCpos and PCCA 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) | 0.001 | 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".