P.008 Functional network reorganization in temporal lobe epilepsy: looking beyond the hippocampus
Bibliographic record
Abstract
Background: Temporal lobe epilepsy (TLE) has been redefined as a disorder associated with network-level dysfunction not limited to the epileptic zone. As such, as resting state (rs) fMRI has been used to evaluate the implicated resting state networks (RSN) and their ensuing functional impairments. However, few studies have analyzed patients with (TLE-HS) and without (TLE-nonHS) hippocampal sclerosis independently. Whereas TLE-HS often warrants surgical intervention, drug-resistant TLE-nonHS might pose challenges for diagnosis and treatment decisions. Methods: This study aimed to investigate functional connectivity changes (FC) of RSNs beyond the hippocampus using rs-fMRI. Rs-fMRI data was acquired from 16 TLE-HS and nine TLE-nonHS, along with 25 healthy controls (HC). RSNs were established using a data-driven independent component analysis approach, in order to determine significant connections between HC and patient groups ipsilateral and contralateral to the seizure focus. Results: When comparing TLE-HS to HC, FC changes were found for the dorsal-attentional (DAN), visual, fronto-parietal (FPN), sensorimotor and default-mode networks (DMN). Alterations in the DAN, DMN and FPN were found when comparing TLE-nonHS to HC. Conclusions: This study demonstrated widespread network reorganization across TLE subtypes. These FC patterns hold promise as a prognostic biomarker, and may be used to define subsequent function and dysfunction in this patient population.
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 | 0.000 |
| 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) | 0.013 | 0.001 |
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".