Search Results for author:
Found 10 papers, 3 papers with code
Incomplete resection of the icEEG seizure onset zone is not associated with post-surgical outcomes
Retrospective analysis of icEEG recordings from 63 subjects (735 seizures) identified seizure onset regions through visual inspection and algorithmic delineation.
Interictal MEG abnormalities to guide intracranial electrode implantation and predict surgical outcome
Finally, we assessed if the implantation of electrodes in abnormal tissue, and resection of the strongest abnormalities determined by MEG and iEEG explained surgical outcome.
Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power
We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesising a greater overlap in seizure-free patients.
MEG abnormalities and mechanisms of surgical failure in neocortical epilepsy
Resting-state MEG data were acquired for 70 healthy controls and 32 patients with refractory neocortical epilepsy.
A library of quantitative markers of seizure severity
In individual patients, 71% had a moderate to large difference (ranksum r > 0. 3) between focal and subclinical seizures in three or more markers.
Intracranial EEG structure-function coupling predicts surgical outcomes in focal epilepsy
We investigated pre-operative structure-function coupling at two spatial scales a) at the global iEEG network level and b) at the resolution of individual iEEG electrode contacts using virtual surgeries.
Volumetric and structural connectivity abnormalities co-localise in TLE
We computed grey matter volumetric changes and white matter structural connectivity abnormalities in 144 patients with unilateral TLE and 96 healthy controls.
Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy
However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently.
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
We fine-tuned our model on three small annotated datasets from different institutions and on the annotated images in EPISURG, comprising 20, 33, 19 and 133 subjects.
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning
In addition to EPISURG, we used three public datasets comprising 1813 preoperative MR images for training.
