White matter brain-age in diverse forms of epilepsy and interictal psychosis
Bibliographic record
Abstract
Abnormal brain aging is suggested in epilepsy. Given the brain network dysfunction in epilepsy, the white matter tracts, which primarily interconnect brain regions, could be of special importance. We focused on white matter brain aging in diverse forms of epilepsy and comorbid psychosis. We obtained brain diffusion tensor imaging (DTI) data at 3 T-MRI in 257 patients with epilepsy and 429 healthy subjects. The tract-based fractional anisotropy values of the healthy subjects were used to build a brain-age prediction model, and we calculated the brain-predicted age difference (brain-PAD: predicted age-chronological age) of all subjects. As a result, almost all epilepsy categories showed significantly increased brain-PAD (p < 0.001), including temporal lobe epilepsy (TLE) with no MRI-lesion (+ 4.2 yr), TLE with hippocampal sclerosis (+ 9.1 yr), extratemporal focal epilepsy (+ 5.1 yr), epileptic encephalopathy or progressive myoclonus epilepsy (+ 18.4 yr), except for idiopathic generalized epilepsy (IGE). Patients with psychogenic non-epileptic seizures also presented increased brain-PAD. In TLE, interictal psychosis significantly raised brain-PAD by 8.7 years. In conclusion, we observed increased brain aging in most types of epilepsy, which was generally consistent with brain morphological aging results in previous studies. Psychosis may accelerate brain aging in TLE. These findings may suggest abnormal aging mechanisms in epilepsy and comorbid psychotic symptoms.
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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) | 0.001 | 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".