Connectome biomarkers of drug‐resistant epilepsy
Bibliographic record
Abstract
Drug-resistant epilepsy (DRE) considerably affects patient health, cognition, and well-being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long-term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".