Brain Hypertrophy in Patients With Mesial Temporal Lobe Epilepsy With Hippocampal Sclerosis and Its Clinical Correlates
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Mesial temporal lobe epilepsy (mTLE) is generally associated with focal brain atrophy, but little knowledge exists on possible disease-related hypertrophy of brain structures. We hypothesized that repeated seizures or adaptive plasticity may lead to focal brain hypertrophy and aimed to investigate associated clinical correlates. METHODS: In this cohort study, we included patients with mTLE undergoing detailed epilepsy evaluations and matched healthy volunteers (HVs) from 2 tertiary centers (discovery and validation cohorts). We assessed areas of brain hypertrophy and their clinical correlates using whole-brain voxel-based or surface-based morphometry (VBM, SBM), subcortical volumetry, and shape analysis of T1-weighted MRI data by fitting linear models. We evaluated the functional implications of the findings on memory encoding using fMRI. RESULTS: = 0.009). In left mTLE, contralateral amygdala hypertrophy was associated with poorer verbal memory and, in right mTLE, with more frequent focal-to-bilateral tonic-clonic seizures. A larger volume of the contralateral amygdala correlated with increased functional activation of the right parietal memory encoding network in a subgroup (44/135 patients with mTLE, 26/47 HVs) receiving fMRI. DISCUSSION: Unilateral mTLE due to hippocampal sclerosis is associated with hypertrophy of the contralateral amygdala. This may represent plasticity to compensate for verbal memory deficits or may be the consequence of seizure spread to the contralateral hemisphere.
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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 | 0.001 | 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".