Bridging computational and clinical strategies for presurgical identification of epileptogenic networks.
The 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
- [1] § METHODS › Dynamic network models of sEEG ↔ epinetmap/core/__init__.py, the whole file · a weak match · score 0.64 · sliding windows, linear dynamical, multichannel, model, sink, fragility
- [2] § METHODS › Patient population and electrode implantation ↔ main.py, lines 226–256 · score 0.51 · SOZ channels, Sink connectivity, stars, medians, computational, fragility
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
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The authors' code
Python · 60 lines · 1.5 KB · MIT · 1 match
- """
- Core computational building blocks for epinetmap.
- Organization
- ------------
- (i) Windowing: crop/resample + sliding windows for continuous multichannel data.
- (ii) Models: estimate linear dynamics (A) or extract DMD modes per window.
- (iii) Metrics: fragility + source/sink metrics from A (serial, efficient).
- (iv) Tracking: post-tracking: mode relabeling / matching after DMD extraction.
- Notes
- -----
- - All modules are serial-only by default (joblib removed).
- - APIs are written so future parallelization can map a per-window worker.
- """
- from .windowing import (
- WindowConfig,
- crop_data,
- resample_data,
- prepare_data_for_windowing,
- window_starts_samples,
- iter_window_starts,
- iter_windows_xy,
- window_slices_xy,
- )
- from .models import (
- AEstimator,
- RidgeAEstimator,
- PinvAEstimator,
- DMDAEstimator,
- estimate_A_series,
- # DMD utilities
- suggest_stacking_factor,
- estimate_r_from_energy,
- dmd_stacked_modes,
- # DMD series extraction
- DMDSeriesConfig,
- extract_dmd_series,
- # DMD plotting (raw)
- plot_dmd_freqs_over_time,
- )
- from .metrics import (
- SourceSinkMetrics,
- FragilityConfig,
- FragilityMetrics,
- Normalizer,
- compute_metrics_over_time,
- # Metrics plotting
- plot_channel_distributions,
- plot_metrics_heatmap,
- )
- from .tracking import (
- align_dmd_series_by_frequency,
- # DMD plotting (matched/aligned)
- plot_dmd_freqs_matched,
- )
__init__.py at commit 2702370, under MIT · at the source
Overview
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Swiss Epilepsy Center, Clinic Lengg, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- ETH Zurich, Zurich, Switzerland
Abstract
Objective: About one third of epilepsy patients are drug‐resistant. Resective epilepsy surgery remains a key treatment option but depends critically on accurate identification of the seizure onset zone (SOZ), which is still guided mainly by subjective visual inspection of electrophysiological signals. Network‐based metrics derived from intracranial EEG have recently shown promise for SOZ identification, but their evaluation and interpretation have remained disconnected from standard clinical procedures and reasoning.
Methods: We analyzed stereotactic EEG (sEEG) recordings from 20 patients undergoing presurgical evaluation in the interictal state and during clinical mapping via electrical stimulation. We constructed patient‐specific time‐varying dynamic network models and addressed key questions for clinical translation: how sensitive network vulnerability estimates are to the choice among related published metrics, how they depend on the time of the day, and how the resulting conclusions relate to stimulation‐evoked epileptiform discharges as a routine clinical reference for network vulnerability in 5 patients who underwent 50 Hz stimulation mapping. We then simulated virtual thermocoagulation in 6 patients who later underwent thermocoagulation by removing the clinically coagulated nodes and testing whether the resulting network changes went beyond pure network size reduction.
Results: The network metrics correlated with epileptiform discharges evoked by 50 Hz intracranial stimulation in four of five stimulated patients, supporting a link between model‐based network fragility and interictal epileptiform discharges evoked in clinical stimulation mapping. Using virtual thermocoagulation, we quantified the expected network‐level change under model node removal, capturing both local and global effects depending on individual network architecture. Across patients, more fragile network metrics pointed toward clinically defined SOZ contacts and yielded stable conclusions across time, conditions, and perturbation properties, supporting their reliability.
Significance: Together, these findings provide a clinically interpretable calibration of published network vulnerability metrics against routine clinical references, using interictal sEEG data only.
Plain Language Summary: Some people with epilepsy need brain recordings to find the tissue where seizures start. We tested whether computational models built from seizure‐free sEEG recordings can identify vulnerable parts of the epileptic network. The model‐based measures pointed toward clinically defined seizure onset regions, were stable across recording times, and often agreed with responses seen during clinical brain stimulation. These results suggest that interictal network modeling may complement standard presurgical evaluation, although larger prospective studies are needed.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.
Swiss-Epilepsy-Center/epinetmap
2702370c15a0ee483f46244b607b87f1b09b7a34, 7 August 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
9 files
- epinetmap/
__init__.py — Python, 49 lines - epinetmap/
core/ — Python, 60 lines, 1 match__init__.py - epinetmap/
core/ — Python, 695 linesmetrics.py - epinetmap/
core/ — Python, 519 linesmodels.py - epinetmap/
core/ — Python, 532 linestracking.py - epinetmap/
core/ — Python, 190 lineswindowing.py - main.py — Python, 327 lines, 1 match
- LICENSE — License, 21 lines
- README.md — Text, 59 lines
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 7 scripts, each with its path and the digest of its content;
- 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Data availability statement
The sEEG data supporting this study are not publicly available due to patient‐privacy restrictions and the terms of the ethics approval but are available from the corresponding author upon reasonable request subject to a data‐sharing agreement. The analysis code is openly available at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, pages, dates, 6 authors, 6 keywords, 4 funders, 49 references.
Cite
This paper
Dubcek, T., Ledergerber, D., Koenig, K., Elshahabi, A., Polania, R., & Imbach, L. (2026). Bridging computational and clinical strategies for presurgical identification of epileptogenic networks. Epilepsia open, 10.1002/
BibTeX
@article{dubcek2026bridg
author = {Dubcek, Tena and Ledergerber, Debora and Koenig, Kristina and Elshahabi, Adham and Polania, Rafael and Imbach, Lukas},
title = {{Bridging computational and clinical strategies for presurgical identification of epileptogenic networks}},
journal = {Epilepsia open},
year = {2026},
month = jul,
pages = {10.1002/
publisher = {Wiley},
issn = {2470-9239},
doi = {10.1002/
url = {https://
pmid = {42495815},
pmcid = {PMC13397325}
}
RIS
TY - JOUR
AU - Dubcek, Tena
AU - Ledergerber, Debora
AU - Koenig, Kristina
AU - Elshahabi, Adham
AU - Polania, Rafael
AU - Imbach, Lukas
TI - Bridging computational and clinical strategies for presurgical identification of epileptogenic networks
T2 - Epilepsia open
J2 - Epilepsia Open
PY - 2026
DA - 2026/
SP - 10.1002/
SN - 2470-9239
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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